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Bishop Reginald Mallett House at Lake Wawasee, view 2
1 2019-07-12T13:57:25-07:00 John David Beatty 85388be94808daa88b6f1a0c89beb70cd0fac252 32716 1 View of the house belonging to Bishop Reginald Mallett at Lake Wawasee, later demolished. It is currently the site of All Saints Episcopal Church, Syracuse, and the conference center there. plain 2019-07-12T13:57:25-07:00 John David Beatty 85388be94808daa88b6f1a0c89beb70cd0fac252This page is referenced by:
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Reginald Mallett, Third Bishop
Of the eight episcopates that have characterized the first 120 years of the Diocese of Northern Indiana, that of Reginald Mallett, the third bishop, stands in many ways as the most difficult and turbulent. Mallett came to office during the middle of World War II, and his authoritarian leadership style and personal prejudices brought strain to much of the diocese, in spite of the paradox that it grew significantly under his troubled administration. This irony makes the Mallett years with all of its flaws the most challenging to appreciate among those of his predecessors and successors.
When Bishop Campbell Gray died unexpectedly in 1944, there were no organized plans in place for a successor. The diocesan archdeacon, Ven. J. McNeal Wheatley of Trinity Fort Wayne, enjoyed strong popularity with the laity but not with his fellow clergy, nearly all of whom disliked his leadership style. At a Special Council held in South Bend on 28 June 1944, a divisive meeting was held that included a resolution calling for investigating a possible reunion with the Diocese of Indianapolis, but it was declared out of order. Seven priests were nominated for bishop, but none secured a majority. The Rev. Robert J. Murphy of St. Mark's Church in Howe received sufficient clerical votes on the eleventh and twelfth ballots, while Wheatley led the lay order on fifteen consecutive ballots. When it became obvious that the meeting was deadlocked, it adjourned for a month.
At a second convocation on 26 July, the Rev. Reginald Mallett of Grace and St. Peter's Church in Baltimore received the necessary clerical and lay support on the first ballot. Mallett had no idea he was under consideration for bishop, and when he received the call notifying him of the election, he thought at first it was a joke.
James Reginald Mallett, who never used his first name, was born at Fernbank, Ohio, on 27 February 1893, the son of the Rev. Frank James Mallett and Mary Emily (Long). His father had been a prominent and respected priest in North Carolina and Ohio. Reginald attended Erasmus Hall in New York, received his Bachelor's degree from the University of North Carolina in 1915, and then attended General Theological Seminary, graduating in 1918 during World War I. He was ordained to the priesthood later that same year by Bishop Joseph Blount Cheshire of North Carolina. He then served several parishes in North Carolina, including St. John's in Wilmington. He married Lucy Atkinson Murchison, known to her friends as "Miss Lucy," on 12 February 1924. As the granddaughter of the late Bishop Thomas Atkinson of North Carolina, Lucy came from an old Episcopal family and was warmly remembered across the diocese for her hospitality, graciousness, and keen sense of humor. They would adopt a son, Reginald II, who was later ordained a priest by Bishop Mallett in 1960.
Mallett had gone on to serve several parishes, including canon of Trinity Cathedral in Cleveland, Ohio; rector of Holy Trinity, Greensboro, North Carolina; rector of Christ Church, Chattanooga, Tennessee; and rector of Grace Church in White Plains, New York. He then had a long stint at Grace and St. Peter's Church in Baltimore, beginning in 1936, where he solidified his reputation as a strong Anglo-Catholic, a qualification that brought him to the attention of Northern Indiana as a compromise candidate for bishop. His consecration took place at St. James Church, South Bend, on 25 October 1944. A reporter for a Protestant newspaper, The Chronicle, complained about the copes, miters, candles, and incense at the service, writing, "Oh, it was very, very Catholic, and blessings were as thick as gooseberries in July."
In 1944, the diocese was still suffering from the dual effects of the Great Depression and World War II. It had just nineteen parishes and ten missions, only fifteen active clergy, and 199 total confirmations had been celebrated. The diocese paid to the national church a quota of just $3,500. When asked about what his new policies would be, Mallett said, "I made up my mind that it should be to change things as little as possible and to learn in these days of changing conditions how to plan hopefully for the future." He would continue Bishop Gray's focus on the Eucharist as the centerpiece of the liturgy every Sunday, and even those not confirmed, he believed, should be present and worship at Holy Communion even if they could not fully partake.
Early on, Mallett took a strong interest in evangelism and wanted to see a rise in the total number of confirmations across the diocese. No new parishes had been added to the diocese since Christ Church, Gary, in 1908. The new bishop challenged his priests and laity to achieve a ten percent yearly growth in the total number of communicants, doubling the national average of just five percent. His challenge came at an opportune time, just as veterans were returning from the war, starting families, and launching the Baby Boom generation. By 1946, every parish in the diocese met its annual financial assessment, having both a seat and vote at the annual convention. Northern Indiana followed only the dioceses of Florida and South Florida in the rate of growth. "From having been on its last legs during the Depression," said one writer, "the Diocese enjoyed considerable advance in the post-war world."
Having had his institution at St. James Church in South Bend, Mallett took a liking to that city and decided to sell the bishop's residence in Mishawaka and move there. He purchased a house at 2117 East Jefferson Boulevard, paying a third of the cost himself and using donations from his former parishioners in Baltimore for the remainder. In 1950, he announced that St. Paul's in Mishawaka would cease to have its pro-cathedral status, and he eventually accepted the invitation of St. James to become the new cathedral. Its vestry agreed to purchase a nearby building for use as diocesan office space. Between 1950 and early 1957, however, the diocese operated without a cathedral. The bishop had an oratory for celebrating Mass and an office in his official residence. On 30 January 1957, Mallett was formally enthroned as bishop, and the status of St. James officially changed.
If Mallett's liturgical and evangelical styles proved a good fit for his times, his leadership style did not. By his own acknowledgement, he was not a good speaker. Though he had a sense of humor, the new bishop was also authoritarian in his relations with his fellow clergy, and within a short time turned his back on several priests and made them outcasts. Some observers concluded that the behavior was decidedly unchristian and not befitting the chief pastor of the diocese. According to Robert Center, "While he was a devoted pastor to many of his clergy, there were some who, for good reason or ill, were persona non grata. There were alienation and ill-conceived hostility among them. Assigning the culpability for this pastoral breakdown would be difficult. But as any priest knows all too well, a sense of alienation is destructive of the pastoral relationship. Until the day of Bishop Mallett's death, some deep wounds were never healed. There was a price to be paid for this unhappy situation. It cost the bishop in terms of his spiritual well being, cost some of the clergy a warm relationship with their father-in-God, and cost the diocese in terms of as shadow which hovered over the diocesan family."
Part of the problem undoubtedly stemmed from Mallett's personality. Soon after becoming bishop, he became accustomed to giving orders and having them unquestionably obeyed without any attempt at collegiality. In 1947, he ordered, without first requesting, that William Sheridan leave Marion and assume the rectorship of St. Thomas, Plymouth. Sheridan obeyed and gave Mallett his stalwart support. Similarly, when the mission of St. Michael and All Angels was founded in South Bend, the bishop ordered all parishioners of the Cathedral living east of Twyckenham Avenue to join the new church and terminated their membership at the Cathedral without their consent. He forbid them to return to the Cathedral.
The same obedience did not hold at Trinity, Fort Wayne, where its rector, James McNeal Wheatley, the diocesan archdeacon who had almost been elected bishop, displeased Mallett with his independence. Without consulting Wheatley beforehand, Mallett eliminated the archdeacon office and ordered him by mail to take charge of the chaplaincy of Howe School and chairmanship of the Diocesan Council. Wheatley refused curtly in a replying telegram, infuriating the bishop. In 1947, when open conflict erupted at Trinity Fort Wayne, Mallett did nothing to intervene until Wheatley had resigned. However, in the wake of the departure, the conflict produced a deeply divided parish, half of whom despised the bishop. Once at a meeting, in an effort to challenge his heavy-handed approach to personal relations, a vestryman had called him "Reggie," much to his displeasure. The bishop excommunicated the senior warden after he urged fellow Wheatley supporters to withhold their pledges and refusing to recognize Peter Langendorff as priest-in-charge. Indeed, Mallett developed such a strong dislike for Fort Wayne that years later, when conducting a rehearsal for a confirmation class, he warned the teenagers, after ordering them to kneel, that they were required to kiss his ring during the ceremony: "Do you know how I got my name? I am Bishop Mallett, and if any of you fails to kiss my ring, I will hit you on the side of the head with a mallet." It was not spoken as a joke, and many youths later recalled being terrified of him.
Contrasted with this attitude was the friendly sentiment Mallett felt for Christ Church Gary, its retiring rector, the Rev. James E. Foster, and the incoming rector, the Rev. James Curtis. Photographs show him sharing laughs with the priests and with Mrs. Curtis in a parish that was clearly much closer to his heart. Those whom the bishop liked remembered him and his wife, Miss Lucy, as friendly, affectionate hosts, who thought nothing of hosting fifty faculty members of Howe School for dinner in their large home. He enjoyed playing the parlor organ in his home, and the bishop and his wife often hosted various diocesan ECW groups, where the members recalled him playing. With certain priests he maintained close friendships, even Horace Varian, whom he defrocked for sexual impropriety, and kept in contact for years afterward.
Bishop Mallett took an interest in acquiring Central Normal College in Danville, Indiana, and in partnership with Bishop Richard Kirchhoffer of the Diocese of Indianapolis, hoped to turn the school into a liberal arts Episcopal college. In 1946, its president, Edgar Cummings, had approached both dioceses about its purchase. It had five buildings and assets of $300,000. Upon approval by both dioceses, the new school was called Canterbury College. In spite of high hopes of both bishops for its success, they both came to realize that the amount of money needed to sustain the school proved too great to be sustained with the available budget. Moreover, the liturgical styles of the two dioceses differed so greatly that Mallett and Kirchhoffer, who disliked each other personally, could seldom agree on strategy for the school. After years of struggling membership, the school closed permanently in 1951.
Mallett and his wife continued to extend hospitality to those priests and families in the bishop's good graces by sending invitations to the bishop's home for overnight retreats, as well for picnics at Lake Wawasee in the summers. The diocese was able to acquire additional land in 1951 when Bishop White's daughter, Mrs. George Doubleday, presented two cottages as a gift. For those whom the bishop liked, these lakeside events were warm and well-remembered for the hospitality provided. Some priests and congregations were frozen out of these events, however. In 1956, the Rev. Harold Kappes of Holy Trinity, South Bend, a popular priest with his parish, was abruptly removed from his post without explanation. Mallett always invited the African American congregation of St. Augustine's in Gary, and when his neighbors complained to him, he defended the invitation by saying they were part of his church and always welcome here.
The Baby Boom years led to significant growth in the diocese, but it was uneven. New missions were formed, and some older congregations grew while others experienced little change. The Church of the Good Shepherd, East Chicago, a mission of long-standing, was admitted as a parish in 1956, followed in quick succession the following year by St. Michael and All Angels, South Bend, and St. Andrew's, Long Beach (now Michigan City); by St. Andrew's, Valparaiso, in 1960, and by St. Augustine and St. Barnabas in Gary, both in 1961. In 1956, Mallett had cautioned against a new, larger building for St. Augustine's, telling its rector, Wallace Wells, that the plans initially drafted by Chicago architect Edward Dart were "too ambitious for a colored congregation." The congregation raised the funds anyway ($100,000) for a smaller building designed by Dart, and they did receive some diocesan financial support.
New diocesan missions were begun during this era with Mallett's encouragement, including St. Christopher's at Crown Point, St. Charles the Martyr at Butler, St. Peter's at Rensselaer, and Holy Family at Angola. Of these four, only Butler would close. Two parochial missions also were founded, including St. Alban's, a mission of Trinity Fort Wayne, and St. David's, a mission of St. John the Evangelist in Elkhart. Mallett also supported the Benedictine monks who had taken charge of Valparaiso, urging them as a matter of personal growth to establish their own self-sustaining priory at Three Rivers, Michigan. In 1961, the diocesan convention approved the creation of the Diocesan Expansion Fund with the goal of raising $200,000. The goal was to provide low-cost loans out of the fund for parishes planning to make renovations. Some felt this sum was far too low, and because of poor planning, there was no follow up to help shepherd the campaign to its conclusion. Nevertheless, the growth was real, and the money raised became the seed of the Diocese of Northern Indiana Foundation. In 1963, Northern Indiana had 22 parishes, 12 missions, and was paying a national quota of more than $24,000.
Bishop Mallett also took an interest in the church overseas. He and his wife made a number of foreign trips. In 1956, the Archbishop of Canterbury sent him with two other Anglican bishops on a secret mission to Spain, where they consecrated the Rev. Santos Molina as bishop of the Spanish Episcopal Church. The church there had been persecuted under the Franco regime, and it faced extreme hostility as a non-Catholic church. Mallett was deeply satisfied with the success of this trip and securing the Anglican Church's future in Spain.
Miss Lucy died in 1959, and by 1961, Mallett's own health began to fail. He was diagnosed with cancer, and according to Robert Center, "the constant pain and intensive treatment required effected a definite change in his normal personality." He called for the election of a Bishop Coadjutor on 12 December 1962, and March 1963, Walter Conrad Klein was elected. He retired the following October, moved back to North Carolina, and died there in 1965.
Rev. William Sheridan, then rector of St. Thomas Church in Plymouth and later bishop, commented: "[Bishop Mallett] suffered from as many weaknesses as you and I, being human. But side by side these weaknesses, there was great human warmth, kindness, and profound charity." For several years in the late 1960s the diocese ran a summer camp for youth named the Bishop Mallett Camp, but by the 1970s, perhaps due to his unpopularity, the name was changed to the Bishop White and Bishop Gray camps. Mallett's primary legacy remains the growth the diocese enjoyed under his leadership, even though the laity and clergy should take most credit for that accomplishment.
Order of Service for the Consecration of the Reverend Reginald Mallett ... 25 October 1944
media/All Saints Syracuse exterior, Nov 8 2015.jpg
All Saints Episcopal Church, Syracuse (formerly All Saints Chapel, Wawasee)
In 1905, Bishop John Hazen White engaged in a dispute with the vestry of Trinity Episcopal Church, Michigan City, over his liturgical style. In anger and frustration, he began spending his summers at Vawter Park along the south shore of Lake Wawasee, renting a cottage from Charles A. Sudlow. Finding no church in the area, he conducted Episcopal services on the cottage lawn and drew visitors from around the lake to worship. As the rift at Michigan City began to deepen, White reported to the diocesan convention that he spent his winters at Howe Military School and his summers at Wawasee, where he began to conduct open air services for summer lake dwellers. The response to these services was so enthusiastic that in 1907, he made plans to build a chapel beside the lake. With the financial help of Mr. and Mrs. Judson and Joseph Lilly, he purchased a cottage as his summer residence, which he called Bishopcroft.
White then gave consideration to building the chapel. Using money from the diocesan Emergency Fund, he purchased the land, and then using additional funds given by the Domestic and Foreign Missionary Society, he received $1,000 per year for five years with an additional $5,000 borrowed from the Capital National Bank of Indianapolis. Construction began immediately, and the building was consecrated on 7 July 1907. The chapel, called All Saints, was a rectangular, utilitarian structure with simple windows and pews and a small sacristy behind the altar. By 1917, when White had declared Michigan City's view of its cathedral an "empty illusion" and moved to South Bend, he continued to spend his summers at Wawasee and presided at services in the chapel. He inaugurated the Wawasee Conference, held in July at the South Shore Inn, where members of the diocesan family could worship and take classes.
After White's death, his successors, Campbell Gray and Reginald Mallett, also enjoyed the chapel and summer house, with the latter spending a great deal of time there. Members of other parishes would take turns worshiping and having picnics on the grounds. In the 1940s, Bishop White's daughter, Mrs. Doubleday, presented Mallett with several lots on the lake that had belonged to the White family. These gifts gave the diocese a permanent foothold at the lake.
In 1966, All Saints became an official mission of the diocese, the same year that the Wawasee Episcopal Center was established. The Rev. David Hyndman served as vicar. Youth camps were held regularly in the summer, and the site remained an informal gathering place for the diocese. By the 1980s, however, the house and church had grown increasingly derelict in condition.
In 1991, during the episcopate of Bishop Francis C. Gray and after years of planning, the diocese determined to demolish both the house and chapel and construct a new, modern church, called All Saints Syracuse, along with a large, multi-bedroom center called the Episcopal Retreat Center. The Center became available for vestry retreats and other special occasions for members of the diocese. Bishop Gray also oversaw the return of a chalice and paten back to Trinity, Michigan City, after Bishop White had removed them without permission and placed them at Wawasee. Since 2006, the parish has been under the care of the Rev. Larry Biller, who was previously its senior warden.
David Lee Hyndman, 1966-1991
Mark Thompson, 1992-1995
Linda M. Hughes, 1995-1999
Martin Brownlee Lavengood, 2000-2003
Larry Biller, 2006-2021
Michael Fulk, 2022-
Bibliography: Robert J. Center, Our Heritage: A History of the First Seventy-five Years of the Diocese of Northern Indiana (South Bend: Diocese of Northern Indiana, 1973).
Parish Register of All Saints Chapel, 1907-1945


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Data Scientists need a strong foundation in Statistics, Mathematics, Linear Algebra, Computer Programming, Data Warehousing, Mining, and Modeling to build winning algorithms. Having proficiency in tools such as Python, R, R Studio, Hadoop, MapReduce, Apache Spark, Apache Pig, Java, NoSQL database, Cloud Computing, Tableau, and SAS is beneficial, but not mandatory.
Outcomes of Data Science Program in Kolhapur
In this data-driven environment, 360DigiTMG's certification prepares you for the surging demand for Big Data skills and technology in all the leading industries. There is a huge career prospect available in the field of data science and our certification program is one of the most comprehensive in the industry today. This data science course in Kolhapur is specially designed to suit both data professionals and beginners who want to make a career in this fast-growing profession. This training will equip the students with logical and relevant programming abilities to build database models. They will be able to create simple machine learning algorithms like K-Means Clustering, Decision Trees, and Random Forest to solve problems and communicate the solutions effectively. In three months, students will also explore the key techniques such as Statistical Analysis, Regression Analysis, Data Mining, Machine Learning, Forecasting and Text Mining, and scripting algorithms for the same with Python and R Programming. Understand the key concepts of Neural Networks and study Deep Learning Black Box techniques like SVM.
Work with various data generation sources
Perform Text Mining to generate Customer Sentiment Analysis
Analyze structured and unstructured data using different tools and techniques
Develop an understanding of Descriptive and Predictive Analytics
Apply Data-driven, Machine Learning approaches for business decisions
Build models for day-to-day applicability
Perform Forecasting to take proactive business decisions
Use Data Concepts to represent data for easy understanding
Block Your Time
Who Should Sign Up?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- Banking and Finance Analysts
- Marketing Managers
- Supply Chain Professionals
- HR Managers
Data Science Course Syllabus in Kolhapur
This data science program follows the CRISP-ML(Q) Methodology. The premier modules are devoted to a foundational perspective of Statistics, Mathematics, Business Intelligence, and Exploratory Data Analysis. The successive modules deal with Probability Distribution, Hypothesis Testing, Data Mining Supervised, Predictive Modelling - Multiple Linear Regression, Lasso And Ridge Regression, Logistic Regression, Multinomial Regression, and Ordinal Regression. Later modules deal with Data Mining Unsupervised Learning, Recommendation Engines, Network Analytics, Machine Learning, Decision Tree and Random Forest, Text Mining, and Natural Language Processing. The final modules deal with Machine Learning - classifier techniques, Perceptron, Multilayer Perceptron, Neural Networks, Deep Learning Black-Box Techniques, SVM, Forecasting, and Time Series algorithms. This is the most enriching training program in terms of the array of topics covered.
- Introduction to Python Programming
- Installation of Python & Associated Packages
- Graphical User Interface
- Installation of Anaconda Python
- Setting Up Python Environment
- Data Types
- Operators in Python
- Arithmetic operators
- Relational operators
- Logical operators
- Assignment operators
- Bitwise operators
- Membership operators
- Identity operators
- Check out the Top Python Programming Interview Questions and Answers here.
- Data structures
- Vectors
- Matrix
- Arrays
- Lists
- Tuple
- Sets
- String Representation
- Arithmetic Operators
- Boolean Values
- Dictionary
- Conditional Statements
- if statement
- if - else statement
- if - elif statement
- Nest if-else
- Multiple if
- Switch
- Loops
- While loop
- For loop
- Range()
- Iterator and generator Introduction
- For – else
- Break
- Functions
- Purpose of a function
- Defining a function
- Calling a function
- Function parameter passing
- Formal arguments
- Actual arguments
- Positional arguments
- Keyword arguments
- Variable arguments
- Variable keyword arguments
- Use-Case *args, **kwargs
- Function call stack
- Locals()
- Globals()
- Stackframe
- Modules
- Python Code Files
- Importing functions from another file
- __name__: Preventing unwanted code execution
- Importing from a folder
- Folders Vs Packages
- __init__.py
- Namespace
- __all__
- Import *
- Recursive imports
- File Handling
- Exception Handling
- Regular expressions
- Oops concepts
- Classes and Objects
- Inheritance and Polymorphism
- Multi-Threading
- What is a Database
- Types of Databases
- DBMS vs RDBMS
- DBMS Architecture
- Normalisation & Denormalization
- Install PostgreSQL
- Install MySQL
- Data Models
- DBMS Language
- ACID Properties in DBMS
- What is SQL
- SQL Data Types
- SQL commands
- SQL Operators
- SQL Keys
- SQL Joins
- Subqueries with select, insert, update, delete statements
- Views in SQL
- SQL Set Operations and Types
- SQL functions
- SQL Triggers
- Introduction to NoSQL Concepts
- SQL vs NoSQL
- Database connection SQL to Python
- Check out the SQL for Data Science One Step Solution for Beginners here.
Learn about insights on how data is assisting organizations to make informed data-driven decisions. Gathering the details about the problem statement would be the first step of the project. Learn the know-how of the Business understanding stage. Deep dive into the finer aspects of the management methodology to learn about objectives, constraints, success criteria, and the project charter. The essential task of understanding business Data and its characteristics is to help you plan for the upcoming stages of development. Check out the CRISP - Business Understanding here.
- All About 360DigiTMG & Innodatatics Inc., USA
- Dos and Don'ts as a participant
- Introduction to Big Data Analytics
- Data and its uses – a case study (Grocery store)
- Interactive marketing using data & IoT – A case study
- Course outline, road map, and takeaways from the course
- Stages of Analytics - Descriptive, Predictive, Prescriptive, etc.
- Cross-Industry Standard Process for Data Mining
- Typecasting
- Handling Duplicates
- Outlier Analysis/Treatment
- Winsorization
- Trimming
- Local Outlier Factor
- Isolation Forests
- Zero or Near Zero Variance Features
- Missing Values
- Imputation (Mean, Median, Mode, Hot Deck)
- Time Series Imputation Techniques
- 1) Last Observation Carried Forward (LOCF)
- 2) Next Observation Carried Backward (NOCB)
- 3) Rolling Statistics
- 4) Interpolation
- Discretization / Binning / Grouping
- Encoding: Dummy Variable Creation
- Transformation
- Transformation - Box-Cox, Yeo-Johnson
- Scaling: Standardization / Normalization
- Imbalanced Handling
- Undersampling
- Oversampling
In this module, you will learn about dealing with the Data after the Collection. Learn to extract meaningful information about Data by performing Uni-variate analysis which is the preliminary step to churn the data. The task is also called Descriptive Analytics or also known as exploratory data analysis. In this module, you also are introduced to statistical calculations which are used to derive information along with Visualizations to show the information in graphs/plots
- Machine Learning project management methodology
- Data Collection - Surveys and Design of Experiments
- Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application
- Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types
- Balanced versus Imbalanced datasets
- Cross Sectional versus Time Series vs Panel / Longitudinal Data
- Time Series - Resampling
- Batch Processing vs Real Time Processing
- Structured versus Unstructured vs Semi-Structured Data
- Big vs Not-Big Data
- Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization
- Sampling techniques for handling Balanced vs. Imbalanced Datasets
- What is the Sampling Funnel and its application and its components?
- Population
- Sampling frame
- Simple random sampling
- Sample
- Measures of Central Tendency & Dispersion
- Population
- Mean/Average, Median, Mode
- Variance, Standard Deviation, Range
The raw Data collected from different sources may have different formats, values, shapes, or characteristics. Cleansing, or Data Preparation, Data Munging, Data Wrapping, etc., are the next steps in the Data handling stage. The objective of this stage is to transform the Data into an easily consumable format for the next stages of development.
- Feature Engineering on Numeric / Non-numeric Data
- Feature Extraction
- Feature Selection
- Forward Feature Selection
- Backward Feature Selection
- Exhaustive Feature Selection
- Recursive feature elimination (RFE)
- Chi-square Test
- Information Gain
- What is Power BI?
- Power BI Tips and Tricks & ChatGPT Prompts
- Overview of Power BI
- Architecture of PowerBI
- PowerBI and Plans
- Installation and introduction to PowerBI
- Transforming Data using Power BI Desktop
- Importing data
- Changing Database
- Data Types in PowerBI
- Basic Transformations
- Managing Query Groups
- Splitting Columns
- Changing Data Types
- Working with Dates
- Removing and Reordering Columns
- Conditional Columns
- Custom columns
- Connecting to Files in a Folder
- Merge Queries
- Query Dependency View
- Transforming Less Structured Data
- Query Parameters
- Column profiling
- Query Performance Analytics
- M-Language
Learn the preliminaries of the Mathematical / Statistical concepts which are the foundation of techniques used for churning the Data. You will revise the primary academic concepts of foundational mathematics and Linear Algebra basics. In this module, you will understand the importance of Data Optimization concepts in Machine Learning development. Check out the Mathematical Foundations here.
- Data Optimization
- Derivatives
- Linear Algebra
- Matrix Operations
Data mining unsupervised techniques are used as EDA techniques to derive insights from the business data. In this first module of unsupervised learning, get introduced to clustering algorithms. Learn about different approaches for data segregation to create homogeneous groups of data. In hierarchical clustering, K means clustering is the most used clustering algorithm. Understand the different mathematical approaches to perform data segregation. Also, learn about variations in K-means clustering like K-medoids, and K-mode techniques, and learn to handle large data sets using the CLARA technique.
- Clustering 101
- Distance Metrics
- Hierarchical Clustering
- Non-Hierarchical Clustering
- Clustering Evaluation metrics
Dimension Reduction (PCA and SVD) / Factor Analysis Description: Learn to handle high dimensional data. The performance will be hit when the data has a high number of dimensions and machine learning techniques training becomes very complex, as part of this module you will learn to apply data reduction techniques without any variable deletion. Learn the advantages of dimensional reduction techniques. Also, learn about yet another technique called Factor Analysis.
- Prinicipal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
Learn to measure the relationship between entities. Bundle offers are defined based on this measure of dependency between products. Understand the metrics Support, Confidence, and Lift used to define the rules with the help of the Apriori algorithm. Learn the pros and cons of each of the metrics used in Association rules.
- Association rules mining 101
- Measurement Metrics
- Support
- Confidence
- Lift
- User Based Collaborative Filtering
- Similarity Metrics
- Item Based Collaborative Filtering
- Search Based Methods
- SVD Method
The study of a network with quantifiable values is known as network analytics. The vertex and edge are the nodes and connection of a network, learn about the statistics used to calculate the value of each node in the network. You will also learn about the google page ranking algorithm as part of this module.
- Entities of a Network
- Properties of the Components of a Network
- Measure the value of a Network
- Community Detection Algorithms
Learn to analyse unstructured textual data to derive meaningful insights. Understand the language quirks to perform data cleansing, extract features using a bag of words and construct the key-value pair matrix called DTM. Learn to understand the sentiment of customers from their feedback to take appropriate actions. Advanced concepts of text mining will also be discussed which help to interpret the context of the raw text data. Topic models using LDA algorithm, emotion mining using lexicons are discussed as part of NLP module.
- Sources of data
- Bag of words
- Pre-processing, corpus Document Term Matrix (DTM) & TDM
- Word Clouds
- Corpus-level word clouds
- Sentiment Analysis
- Positive Word clouds
- Negative word clouds
- Unigram, Bigram, Trigram
- Semantic network
- Extract, user reviews of the product/services from Amazon and tweets from Twitter
- Install Libraries from Shell
- Extraction and text analytics in Python
- LDA / Latent Dirichlet Allocation
- Topic Modelling
- Sentiment Extraction
- Lexicons & Emotion Mining
- Check out the Text Mining Interview Questions and Answers here.
- Machine Learning primer
- Difference between Regression and Classification
- Evaluation Strategies
- Hyper Parameters
- Metrics
- Overfitting and Underfitting
Revise Bayes theorem to develop a classification technique for Machine learning. In this tutorial, you will learn about joint probability and its applications. Learn how to predict whether an incoming email is spam or a ham email. Learn about Bayesian probability and its applications in solving complex business problems.
- Probability – Recap
- Bayes Rule
- Naïve Bayes Classifier
- Text Classification using Naive Bayes
- Checking for Underfitting and Overfitting in Naive Bayes
- Generalization and Regulation Techniques to avoid overfitting in Naive Bayes
- Check out the Naive Bayes Algorithm here.
k Nearest Neighbor algorithm is a distance-based machine learning algorithm. Learn to classify the dependent variable using the appropriate k value. The KNN Classifier also known as a lazy learner is a very popular algorithm and one of the easiest for application.
- Deciding the K value
- Thumb rule in choosing the K value.
- Building a KNN model by splitting the data
- Checking for Underfitting and Overfitting in KNN
- Generalization and Regulation Techniques to avoid overfitting in KNN
In this tutorial, you will learn in detail about the continuous probability distribution. Understand the properties of a continuous random variable and its distribution under normal conditions. To identify the properties of a continuous random variable, statisticians have defined a variable as a standard, learning the properties of the standard variable and its distribution. You will learn to check if a continuous random variable is following normal distribution using a normal Q-Q plot. Learn the science behind the estimation of value for a population using sample data.
- Probability & Probability Distribution
- Continuous Probability Distribution / Probability Density Function
- Discrete Probability Distribution / Probability Mass Function
- Normal Distribution
- Standard Normal Distribution / Z distribution
- Z scores and the Z table
- QQ Plot / Quantile - Quantile plot
- Sampling Variation
- Central Limit Theorem
- Sample size calculator
- Confidence interval - concept
- Confidence interval with sigma
- T-distribution Table / Student's-t distribution / T table
- Confidence interval
- Population parameter with Standard deviation known
- Population parameter with Standard deviation not known
Learn to frame business statements by making assumptions. Understand how to perform testing of these assumptions to make decisions for business problems. Learn about different types of Hypothesis testing and its statistics. You will learn the different conditions of the Hypothesis table, namely Null Hypothesis, Alternative hypothesis, Type I error, and Type II error. The prerequisites for conducting a Hypothesis test, and interpretation of the results will be discussed in this module.
- Formulating a Hypothesis
- Choosing Null and Alternative Hypotheses
- Type I or Alpha Error and Type II or Beta Error
- Confidence Level, Significance Level, Power of Test
- Comparative study of sample proportions using Hypothesis testing
- 2 Sample t-test
- 2 Proportion test
- Chi-Square test
Data Mining supervised learning is all about making predictions for an unknown dependent variable using mathematical equations explaining the relationship with independent variables. Revisit the school math with the equation of a straight line. Learn about the components of Linear Regression with the equation of the regression line. Get introduced to Linear Regression analysis with a use case for the prediction of a continuous dependent variable. Understand about ordinary least squares technique.
- Scatter diagram
- Correlation analysis
- Correlation coefficient
- Ordinary least squares
- Principles of regression
- Simple Linear Regression
- Exponential Regression, Logarithmic Regression, Quadratic or Polynomial Regression
- Confidence Interval versus Prediction Interval
- Heteroscedasticity / Equal Variance
- Check out the Linear Regression Interview Questions and Answers here.
In the continuation of the Regression analysis study, you will learn how to deal with multiple independent variables affecting the dependent variable. Learn about the conditions and assumptions to perform linear regression analysis and the workarounds used to follow the conditions. Understand the steps required to perform the evaluation of the model and to improvise the prediction accuracies. You will be introduced to concepts of variance and bias.
- LINE assumption
- Linearity
- Independence
- Normality
- Equal Variance / Homoscedasticity
- Collinearity (Variance Inflation Factor)
- Multiple Linear Regression
- Model Quality metrics
- Deletion Diagnostics
- Check out the Linear Regression Interview Questions here.
You have learned about predicting a continuous dependent variable. As part of this module, you will continue to learn Regression techniques applied to predict attribute Data. Learn about the principles of the logistic regression model, understand the sigmoid curve, and the usage of cut-off value to interpret the probable outcome of the logistic regression model. Learn about the confusion matrix and its parameters to evaluate the outcome of the prediction model. Also, learn about maximum likelihood estimation.
- Principles of Logistic regression
- Types of Logistic regression
- Assumption & Steps in Logistic regression
- Analysis of Simple logistic regression results
- Multiple Logistic regression
- Confusion matrix
- False Positive, False Negative
- True Positive, True Negative
- Sensitivity, Recall, Specificity, F1
- Receiver operating characteristics curve (ROC curve)
- Precision Recall (P-R) curve
- Lift charts and Gain charts
- Check out the Logistic Regression Interview Questions and Answers here.
Learn about overfitting and underfitting conditions for prediction models developed. We need to strike the right balance between overfitting and underfitting, learn about regularization techniques L1 norm and L2 norm used to reduce these abnormal conditions. The regression techniques of Lasso and Ridge techniques are discussed in this module.
- Understanding Overfitting (Variance) vs. Underfitting (Bias)
- Generalization error and Regularization techniques
- Different Error functions, Loss functions, or Cost functions
- Lasso Regression
- Ridge Regression
- Check out the Lasso and Ridge Regression Interview Questions and Answers here.
Extension to logistic regression We have multinomial and Ordinal Logistic regression techniques used to predict multiple categorical outcomes. Understand the concept of multi-logit equations, baseline, and making classifications using probability outcomes. Learn about handling multiple categories in output variables including nominal as well as ordinal data.
- Logit and Log-Likelihood
- Category Baselining
- Modeling Nominal categorical data
- Handling Ordinal Categorical Data
- Interpreting the results of coefficient values
As part of this module, you learn further different regression techniques used for predicting discrete data. These regression techniques are used to analyze the numeric data known as count data. Based on the discrete probability distributions namely Poisson, negative binomial distribution the regression models try to fit the data to these distributions. Alternatively, when excessive zeros exist in the dependent variable, zero-inflated models are preferred, you will learn the types of zero-inflated models used to fit excessive zeros data.
- Poisson Regression
- Poisson Regression with Offset
- Negative Binomial Regression
- Treatment of data with Excessive Zeros
- Zero-inflated Poisson
- Zero-inflated Negative Binomial
- Hurdle Model
Support Vector Machines / Large-Margin / Max-Margin Classifier
- Hyperplanes
- Best Fit "boundary"
- Linear Support Vector Machine using Maximum Margin
- SVM for Noisy Data
- Non- Linear Space Classification
- Non-Linear Kernel Tricks
- Linear Kernel
- Polynomial
- Sigmoid
- Gaussian RBF
- SVM for Multi-Class Classification
- One vs. All
- One vs. One
- Directed Acyclic Graph (DAG) SVM
Kaplan Meier method and life tables are used to estimate the time before the event occurs. Survival analysis is about analyzing the duration of time before the event. Real-time applications of survival analysis in customer churn, medical sciences, and other sectors are discussed as part of this module. Learn how survival analysis techniques can be used to understand the effect of the features on the event using the Kaplan-Meier survival plot.
- Examples of Survival Analysis
- Time to event
- Censoring
- Survival, Hazard, and Cumulative Hazard Functions
- Introduction to Parametric and non-parametric functions
Decision Tree models are some of the most powerful classifier algorithms based on classification rules. In this tutorial, you will learn about deriving the rules for classifying the dependent variable by constructing the best tree using statistical measures to capture the information from each of the attributes.
- Elements of classification tree - Root node, Child Node, Leaf Node, etc.
- Greedy algorithm
- Measure of Entropy
- Attribute selection using Information gain
- Decision Tree C5.0 and understanding various arguments
- Checking for Underfitting and Overfitting in Decision Tree
- Pruning – Pre and Post Prune techniques
- Generalization and Regulation Techniques to avoid overfitting in Decision Tree
- Random Forest and understanding various arguments
- Checking for Underfitting and Overfitting in Random Forest
- Generalization and Regulation Techniques to avoid overfitting in Random Forest
- Check out the Decision Tree Questions here.
Learn about improving the reliability and accuracy of decision tree models using ensemble techniques. Bagging and Boosting are the go-to techniques in ensemble techniques. The parallel and sequential approaches taken in Bagging and Boosting methods are discussed in this module. Random forest is yet another ensemble technique constructed using multiple Decision trees and the outcome is drawn from the aggregating the results obtained from these combinations of trees. The Boosting algorithms AdaBoost and Extreme Gradient Boosting are discussed as part of this continuation module. You will also learn about stacking methods. Learn about these algorithms which are providing unprecedented accuracy and helping many aspiring data scientists win first place in various competitions such as Kaggle, CrowdAnalytix, etc.
- Overfitting
- Underfitting
- Voting
- Stacking
- Bagging
- Random Forest
- Boosting
- AdaBoost / Adaptive Boosting Algorithm
- Checking for Underfitting and Overfitting in AdaBoost
- Generalization and Regulation Techniques to avoid overfitting in AdaBoost
- Gradient Boosting Algorithm
- Checking for Underfitting and Overfitting in Gradient Boosting
- Generalization and Regulation Techniques to avoid overfitting in Gradient Boosting
- Extreme Gradient Boosting (XGB) Algorithm
- Checking for Underfitting and Overfitting in XGB
- Generalization and Regulation Techniques to avoid overfitting in XGB
- Check out the Ensemble Techniques Interview Questions here.
Time series analysis is performed on the data which is collected with respect to time. The response variable is affected by time. Understand the time series components, Level, Trend, Seasonality, Noise, and methods to identify them in a time series data. The different forecasting methods available to handle the estimation of the response variable based on the condition of whether the past is equal to the future or not will be introduced in this module. In this first module of forecasting, you will learn the application of Model-based forecasting techniques.
- Introduction to time series data
- Steps to forecasting
- Components to time series data
- Scatter plot and Time Plot
- Lag Plot
- ACF - Auto-Correlation Function / Correlogram
- Visualization principles
- Naïve forecast methods
- Errors in the forecast and it metrics - ME, MAD, MSE, RMSE, MPE, MAPE
- Model-Based approaches
- Linear Model
- Exponential Model
- Quadratic Model
- Additive Seasonality
- Multiplicative Seasonality
- Model-Based approaches Continued
- AR (Auto-Regressive) model for errors
- Random walk
- Check out the Time Series Interview Questions here.
In this continuation module of forecasting learn about data-driven forecasting techniques. Learn about ARMA and ARIMA models which combine model-based and data-driven techniques. Understand the smoothing techniques and variations of these techniques. Get introduced to the concept of de-trending and de-seasonalize the data to make it stationary. You will learn about seasonal index calculations which are used to re-seasonalize the result obtained by smoothing models.
- ARMA (Auto-Regressive Moving Average), Order p and q
- ARIMA (Auto-Regressive Integrated Moving Average), Order p, d, and q
- AutoTS, AutoARIMA
- A data-driven approach to forecasting
- Smoothing techniques
- Moving Average
- Exponential Smoothing
- Holt's / Double Exponential Smoothing
- Winters / Holt-Winters
- De-seasoning and de-trending
- Seasonal Indexes
- RNN, Bidirectional RNN, Deep Bidirectional RNN
- Transformers for Forecasting
- N-BEATS, N-BEATSx
- N-HiTS
- TFT - Temporal Fusion Transformer
The Perceptron Algorithm is defined based on a biological brain model. You will talk about the parameters used in the perceptron algorithm which is the foundation of developing much complex neural network models for AI applications. Understand the application of perceptron algorithms to classify binary data in a linearly separable scenario.
- Neurons of a Biological Brain
- Artificial Neuron
- Perceptron
- Perceptron Algorithm
- Use case to classify a linearly separable data
- Multilayer Perceptron to handle non-linear data
Neural Network is a black box technique used for deep learning models. Learn the logic of training and weights calculations using various parameters and their tuning. Understand the activation function and integration functions used in developing a Artificial Neural Network.
- Integration functions
- Activation functions
- Weights
- Bias
- Learning Rate (eta) - Shrinking Learning Rate, Decay Parameters
- Error functions - Entropy, Binary Cross Entropy, Categorical Cross Entropy, KL Divergence, etc.
- Artificial Neural Networks
- ANN Structure
- Error Surface
- Gradient Descent Algorithm
- Backward Propagation
- Network Topology
- Principles of Gradient Descent (Manual Calculation)
- Learning Rate (eta)
- Batch Gradient Descent
- Stochastic Gradient Descent
- Minibatch Stochastic Gradient Descent
- Optimization Methods: Adagrad, Adadelta, RMSprop, Adam
- Convolution Neural Network (CNN)
- ImageNet Challenge – Winning Architectures
- Parameter Explosion with MLPs
- Convolution Networks
- Recurrent Neural Network
- Language Models
- Traditional Language Model
- Disadvantages of MLP
- Back Propagation Through Time
- Long Short-Term Memory (LSTM)
- Gated Recurrent Network (GRU)
- Sequence 2 Sequence Models
- Transformers
- Generative AI
- ChatGPT
- Mid Journey
- Crayon
- What Is Prompt Engineering?
- Understanding Prompts: Inputs, Outputs, and Parameters
- Crafting Simple Prompts: Techniques and Best Practices
- Evaluating and Refining Prompts: An Iterative Process
- Role Prompting and Nested Prompts
- Chain-of-Thought Prompting
- Multilingual and Multimodal Prompt Engineering
- Generating Ideas Using "Chaos Prompting"
- Using Prompt Compression
SUNY University Syllabus
- Data Engineering, Machine Learning, & AWS
- Amazon S3 Simple Storage Service
- Data Movement
- Data Pipelines & Workflows
- Jupyter Notebook & Python
- Data Analysis Fundamentals
- Athena, QuickSight, & EMR
- Feature Engineering Overview
- Problem Framing & Algorithm Selection
- Machine Learning in SageMaker
- ML Algorithms in SageMaker
- Advanced SageMaker Functionality
- AI/ML Services
- Problem Formulation & Data Collection
- Data Preparation & SageMaker Security
- Model Training & Evaluation
- AI Services & SageMaker Applications
- Machine Learning
- Machine Learning Services
- Machine Learning Regression Models
- Machine Learning Classification Models
- Machine Learning Clustering Models
- Project Jupyter & Notebooks
- Azure Machine Learning Workspaces
- Azure Data Platform Services
- Azure Storage Accounts
- Storage Strategy
- Azure Data Factory
- Non-relational Data Stores
- Machine Learning Data Stores & Compute
- Machine Learning Orchestration & Deployment
- Model Features & Differential Privacy
- Machine Learning Model Monitoring
- Azure Data Storage Monitoring
- Data Process Monitoring
- Data Solution Optimization
- High Availability & Disaster Recovery
- Certificate Course in Data Science by SUNY
Data Science Panel of Coaches in Kolhapur
Alumni Speak
Data Science Trends in Kolhapur
The demand for Data Scientists is predicted to increase by 30% by 2025. With the inclusion of Cloud and IoT technologies, there has been an exponential growth of data that has led to the expansion of roles for data scientists in the field of Machine Learning and Big Data technology. In the times to come a Data scientist role will not be just subjected to technical aspects but will rise to more of a collaborator and a facilitators role. An entry-level fresher earns around Rs.4.0 lakhs. And if he decides to stay put for another 5 to 10 years on the job, he gets a handsome promotion to the Rs 7 to 11 lakhs per annum layer. If he persists and dedicates a lifetime to growing in his career he can garner anywhere from 25 lakhs to a whopping one crore per annum.
In India, Data Scientists have 4 job hops in 8 years with a 2-year tenure with each employer. They normally get a 60-100% salary increase on job changes. First, the aspirant joins as an intern or in a Junior position and then moves on to becoming a Senior Data Scientist. After this, he gets elevated to the Principal position and finally heads as Chief Data Scientist of the company. The top employers in Data Science are IBM, Accenture, JPMorgan Chase, Amex, McKinsey & Co, Impetus, Wipro, and Microsoft. Accenture offers the highest salary of Rs.19.6 lacs per annum.
Some of the top sightseeing places to see in Kolhapur, Chhatrapati Shahu Palace, Shri Mahalaxmi - Ambabai Temple, Rankala Lake, Shree Jyotiba Devasthan, Kopeshwar Temple, Kaneri Museum and many others.
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"AI to contribute $16.1 trillion to the global economy by 2030. With 133 million more engaging, less repetitive jobs AI to change the workforce." - (Source). Data Science with Artificial Intelligence (AI) is a revolution in the business industry.. AI is potentially being adopted in automating many jobs leading to higher productivity, less cost, and extensible solutions. It is reported by PWC in a publication that about 50% of human jobs will be taken away by the AI in the next 5 years.
There is already a huge demand for AI specialists and this demand will be exponentially growing in the future. In the past few years, careers in AI have boosted concerning the demands of industries that are digitally transformed. The report of 2018 states that the requirements for AI skills have drastically doubled in the last three years, with job openings in the domain up to 119%.
FAQs for Data Science Certification Course Training in Kolhapur
360DigiTMG offers a comprehensive data science course in Kolhapur that covers various aspects of data science, including data analysis, machine learning, data visualisation and more.
While there are no specific prerequisites, basic knowledge of programming languages like Python and statistics can be beneficial for better understanding the concepts taught during the course.
The data science course training at 360DigiTMG in Kolhapur is led by experienced and industry-leading instructors with extensive knowledge and practical experience in the field of data science.
The course covers a wide range of topics, including data preprocessing, exploratory data analysis (EDA), machine learning algorithms, data visualisation, big data technologies, Predictive modelling techniques, deep learning algorithms, generative AI, ChatGPT and more.
Yes, the data science course at 360DigiTMG emphasises hands-on learning. Students will work on real-world projects and case studies to apply the concepts they learn during the course.
No, work experience is not typically a mandatory requirement to join the Data Science Course Training at 360DigiTMG in Kolhapur. The course is designed to cater to both fresh graduates and working professionals looking to upskill or transition into the field of data science. However, having prior work experience, especially in relevant domains, can be an added advantage during the course and can further enhance your learning experience.
Yes, students from non-technical backgrounds can apply for the Data Science Course Training at 360DigiTMG in Kolhapur. As mentioned earlier, having a bachelor's degree in any discipline is generally sufficient for eligibility. The course is designed to accommodate learners from diverse backgrounds, and it often includes introductory sessions on programming and other technical concepts to help students grasp the fundamentals of data science.
There are typically no specific age restrictions for joining the Data Science Course Training at 360DigiTMG in Kolhapur. The course is open to both recent graduates and experienced professionals who wish to pursue a career in data science or enhance their existing skills. As long as the candidates meet the educational and skill prerequisites, they are eligible to enrol in the course.
360DigiTMG offers both online and offline data science courses, giving students the flexibility to choose the mode of learning that suits them best.
360DigiTMG's data science course stands out due to its comprehensive curriculum, hands-on approach, experienced instructors, and industry-relevant projects, which prepares students to meet the demands of the rapidly evolving data science field.
Yes, the data science certificate awarded upon completing the course at 360DigiTMG Kolhapur typically includes details about the topics covered during the training. This can be a valuable reference for potential employers and recruiters.
Yes, 360DigiTMG is a reputable training institute with a strong track record of producing skilled data science professionals. The certificate holds value in the industry and is recognized by various employers.
Absolutely! You can proudly display your data science course certificate from 360DigiTMG on platforms like LinkedIn to demonstrate your commitment to continuous learning and showcase your data science skills to potential employers.
Yes, 360DigiTMG provides Blockchain enabled certificates. This makes it simple to share and check your credentials online.
Certificates issued by 360DigiTMG for data science courses in Kolhapur typically do not have an expiration date. They represent the completion of your training and acquired skills and remain relevant throughout your career.
Yes, 360DigiTMG offers placement support to students who have completed their data science courses in Kolhapur. They have tie-ups with various companies in the industry and aim to connect students with potential employers.
360DigiTMG provides placement assistance through various means, including conducting placement drives, organising job fairs, and inviting companies for campus interviews. They also assist students with resume preparation, interview preparation, and career counselling to help them excel in their job search.
360DigiTMG collaborates with a diverse range of companies from different industries that are looking to hire skilled data science professionals. These companies could include IT firms, consulting companies, finance and banking institutions, healthcare organisations, e-commerce companies and more.
While the specific requirements may vary for different courses, in general, students who actively participate in the course, complete assignments, and demonstrate a good grasp of the concepts have higher chances of availing placement support.
Yes, 360DigiTMG may offer the option to pay the course fees in instalments. This can help students manage their finances more effectively. It's advisable to check with the career counsellor about their payment policies and instalment options to determine the most suitable payment plan for you.
The course fees for the Data Science program at 360DigiTMG in Kolhapur may vary depending on the program duration and the specific features offered. For accurate and up-to-date information regarding fees, it is recommended to directly contact or call to toll free number 1800 212 654321.
It is crucial to be aware of the approved ways of payment for the course fees, including any specialised ones, online transactions, credit/debit cards, bank transfers, and more.
Jobs in the field of Data Science in Kolhapur
The top sectors creating the most data science jobs are BFSI, Energy, Pharmaceutical, Healthcare, E-commerce, Media, and Retail. The maximum demand for Data Scientists are in the Metros cities like Delhi-NCR and Mumbai. It's demand is also catching up in emerging cities like Pune and Bangalore.
Salaries in Kolhapur for Data Scientist
The average salary of a Data Scientist is Rs.10.3 lakhs per annum in India. Professionals specializing in advanced analytics and predictive modelling can command higher salaries.
Data Science Projects in Kolhapur
The Indian government has initiated several data science projects in the fields of Agriculture, Electricity, Water, Healthcare, Education, Road Traffic Safety and Air Pollution. The Government of India has initiated several data science research initiatives as well.
Role of Open Source Tools in Analytics
Python and R are easy to learn and maintain and therefore, Godsend to developers in Data Science. Their extended libraries make it possible to stretch the applications of Python from Big Data Analytics to Machine Learning.
Modes of Training for Data Science with Python
The course in Kolhapur is designed to suit the needs of students as well as working professionals. We at 360DigiTMG give our students the option of both classroom and online learning. We also support e-learning as part of our curriculum.
Industry Application of Data Science
Data Science is used for securities fraud early warning, card fraud detection systems, demand enterprise risk management, analysis of healthcare information, seismic interpretation, reservoir characterization, energy exploration, traffic control and route planning.
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One of my favorite moments in the film Monty Python and the Holy Grail is when the Bridgekeeper asks King Arthur, "what is the air-speed velocity of an unladen swallow?" The film never answers that question, although more than a few resourceful folks have put forward their best estimates. Before the end of this article, I fear I may be asking an equally esoteric question about the noble European swallow: the only difference? I expect you to figure out the answer, because I'm flummoxed.
I received a package in the mail today containing a postcard with the picture of a bird on it, along with a Sony IC Recorder that contained a file named 50-112-251-215.mp3 with the sound of birds chirping for 18 seconds, with a picture of bird watcher Jonathon Jongsma as the featured image. On the back of the postcard was the following message:
Greetings from Innsbruck, Michael.
Spied this fellow building his nest unusually high in a tree. I believe that means the snow will be better here this year. Including a recording of his lovely little song for your enjoyment.
Do with it what you will.
Cheers, J
I suspect the bird pictured is a European swallow, since the barn swallow is Austria's national bird, and bears a striking resemblance to our fine feathered friend. What secret message is hidden within this bird's idle tweets, and what did "J" hear that made him send the recording in the first place?
Be sure to check the Unfiction forums for the discussion of what has been uncovered so far, and check back soon for updates on the story as it unfolds.
EDITED 10/16 to add: those of you nervous about visiting the website at the end of the initial puzzle might want to try this link instead.


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Optum completes acquisition of DaVita Medical Group from DaVita
- DaVita Medical Group's high-quality practices in California, Colorado, Florida, New Mexico and Washington are now part of OptumCare
- Combination will improve care quality and patient experiences and lower costs for 16 million people; and serve more than 80 health plans
EDEN PRAIRIE, Minn. and DENVER (June 19, 2019) — Optum, a leading health services company, completed its acquisition of DaVita Medical Group, one of the nation's leading independent medical groups, from DaVita Inc. (NYSE: DVA). As part of the transaction, HealthCare Partners Nevada, DaVita Medical Group's primary care practice in southern Nevada, will become part of Intermountain Healthcare.
DaVita Medical Group joins OptumCare, which serves more than 80 health plans, and will provide quality care to a combined 16 million patients.
"We are excited to take this important step in building a next-generation comprehensive, coordinated health care organization. Together we will improve patient health and experiences while lowering costs across the continuum of care – including primary, specialty, urgent and surgical care," said Andrew Witty, CEO of Optum.
"In joining Optum, DaVita Medical Group begins an exciting new chapter, helping physicians further advance patient care," said Kent Thiry, Executive Chairman of DaVita Inc.
"DaVita will continue its focus on improving quality of life for patients and expanding access to integrated care in the kidney care space," said Javier Rodriguez, CEO of DaVita Inc.
DaVita Inc. will continue to own and operate its U.S. and international kidney care businesses.
About DaVita Inc.
DaVita is a Fortune 500® health care provider focused on transforming care delivery to improve quality of life for patients around the globe. The company is the largest provider of kidney care services in the U.S. and has been a leader in clinical quality and innovation for 20 years. Through DaVita Kidney Care, the company treats patients with chronic kidney failure and end stage renal disease. DaVita is committed to bold, patient-centric care models, implementing the latest technologies and moving toward integrated care offerings for all. As of March 31, 2019, DaVita serves 203,500 patients at 2,689 outpatient dialysis centers in the United States. The company also operates 234 outpatient dialysis centers in nine countries across the world. DaVita has reduced hospitalizations, improved mortality, and worked collaboratively to propel the kidney care industry to adopt an equitable and high-quality standard of care for all patients, everywhere. To learn more about how DaVita is leading the health care evolution, please, visit DaVita.com/About.
About Optum
Optum is a leading information and technology-enabled health services business dedicated to helping make the health system work better for everyone. With more than 165,000 people worldwide, Optum delivers intelligent, integrated solutions that help to modernize the health system and improve overall population health. Optum is part of UnitedHealth Group (NYSE:UNH). For more information, visit www.optum.com.
SOURCE: Optum


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by Jessica Martin-Weber
In all the needs we put before our own,
It can feel like in the equation of our life, we don't matter.
In always being available to meet other's need for sustenance, connection, guidance, and care…
It can feel like our own needs for rest and space are no longer allowed.
In all the spinning plates we juggle and all the work that always STILL needs to be done…
It can feel like we are dropping everything and can't cope.
In helping instill a sense of self in the ones that call us "mom" or "mama" or "mommy" by encouraging their character and personal growth…
It can feel like who we are isn't important and our own personal growth is forgotten.
In the pursuit of what is best for our children and cheering them on in their adventures…
It can feel like nobody cares about doing the same for us and we can only watch the adventures of others.
In supporting the ones we love in their opportunities to follow their dreams…
It can feel like we are unsupported and our own opportunities and goals are discarded.
In providing community and camaraderie for the most precious people in our lives…
It can feel like we are left-overs, unwanted, invisible, and alone.
In managing our feelings to tend to the big and small emotions of the developing ones we care for…
It can feel like our own emotions are a nuisance and insignificant.
In the nurturing of those who need us most…
It can feel like we are emptying ourselves so completely but that it will never be enough.
You matter.
Moms matter.
You aren't perfect and you never will be, whatever perfect means.
Your highs and your lows.
The good, the bad, and the ugly.
The flab, the stretch marks, the skin and bones, or the extra padding.
The moments of pride, the moments of shame.
Your hurt and your joy.
Your vagina, your scars, your breasts, and your bottles.
And you matter.
The oxygen mask theory is that if you don't have your own mask on, you'll become incapacitated and unable to help those who need assistance. In other words, we all matter.
Moms, this campaign is for you and we want you to know that you matter. And when moms matter, it is good for the whole family.
Thanks to NOONI'S, Simple Wishes, TWELVElittle, and Wilde Brands.
Our friends at NOONI'S, Simple Wishes, TWELVElittle, and Wilde Brands believe so much in moms that they decided to sponsor the very first #TLBMomsMatter and you have a chance to win some of their products! A bundle of products for 2 winners, plus a few runner-up prizes.
Each of the 2 bundles includes:
A NOONI'S Leak-Resistant Nursing Sleep Bra, retail value $50
No nursing pads needed in NOONI'S sleep bra, the first all-in-one full-coverage bra designed to wick away, absorb, and prevent breast milk from leaking through. NOONI'S relaxed fit straps and waistband allow for effortless nursing access. And, our baby-soft fleece interior is not only incredibly cozy, but also stays warm when wet and won't stick to healing chapped nipples. Use Code MomsMatter for 10% off your purchase HERE!
Simple Wishes, winner's choice of nursing and pumping bras, with matching panties, retail value $75
Comfort, support, adjustability, and a touch of sexy – why shouldn't moms have all of that in one bra? Simple Wishes wants all moms to feel confident, and a comfortable sexy bra is a great place to start! Use code SUPERMOM25 for 25% off your purchase HERE!
Wilde Brands brings you the very first chips made from 100% chicken! A 4 pack is yours for the winning. A $20 value.
These chips have the satisfying crunch you crave and they are a total protein snack. Perfect for moms-to-be, new moms, and seasoned moms too. And speaking of seasoning, the Wilde Chicken and Waffles flavor is amazing! Use code LEAKYBOOB for 20% off your purchase HERE!
TWELVElittle is offering winner's choice of anything on their website, a $75-179 value
Adorable and fashionable, these baby bags are for all moms and dads. Each bag is thoroughly designed with individual style, and engineered with every practicality. Made of high quality water resistant fabrics and genuine leather, these bags continue to be the perfect accessory long after you graduate from the baby stage. Use code TLB20 for 20% off your purchase HERE!
4 other winners will each receive a 4-pack of Wilde Brands chips! That's 6 chances to win in this giveaway.
Enter the giveaway with the widget at the bottom of this post!
Meet our #TLBMomsMatter campaign ambassadors:
Hey y'all! I'm Ashley- wifey to my college sweetheart Nelson and mama of 3 under 3.
I work full time at a university as a program coordinator. Aside from work, I adore family time and going on adventures.
My goal with blogging is to encourage mamas to live their best and most healthy lives!
Follow Ashley on Instagram and don't miss her blog.
I'm Kimberly! Mom of unexpectedly four, aged 10, 7, 5, and 1. Married 15 years this summer (a child bride, OBVIOUSLY ; ) ).
I love baking, mom dates, Netflix, research, all day shopping trips, being outside, making lists, and being right. I'm passionate about motherhood and not losing your personhood within it, and the spirit of The Leaky Boob's message. I'm also typing this as I nurse the baby back to sleep, because #whateverworks, right?
Find Kimberly on Instagram.
I'm Lissette. Often loud New Yorker but mostly exhausted pigeon. 😀 Married to a pretty tall guy named Ralph and together we have 2 kids, aged 4 and 2 months, and 2 cats, Nintendo and Sony.
I'm a nerdy momma who also loves crafting. My 4th trimester has just been breastfeeding, changing cloth diapers, trying to fid new volunteers for our community garden, and maybe too much Instagram… lol. Excited to be a part of this! 🙂
Look for Lissette on Instagram!
My name is Nayeli, I am a mom of two!
I am a small business owner, my husband and I are videographers. I'm mostly at home with my little ones, and when I am out at work our families help us with the kids.
I felt that with my first pregnancy/parenthood journey I didn't have all the knowledge/research available to me, so that's what I'trying to do on my page- help parents-to-be/new parents be aware of all the options they have!
Follow Nayeli on Instagram.
My name is Rachel! I am mother to 4 living children, 1 angel baby, former foster mother to 4 different children, and current foster mom to a teenager and her baby (yes, I'm a grandma at 33!), and wife to Milkman.
I can be found stealing kisses from my husband in the kitchen, munching on baby cheeks, nursing a kid or two, chasing chickens around the farm we live on, juggling social workers, and sending memes to my bestie.
I blog over at SheRocksTheCradle.com, where I talk about parenting, babies, fostering, breastfeeding, and marriage. I am so excite to be back on a TLB Campaign because it gives me the opportunity to build a community with other mothers.
Follow Rachel on Instagram and Facebook!


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Hi, My name is Lisa and by profession I am a registered nurse. I am married to my high school sweetheart and we have 2 children who are now successful young adults. I have been a crafter my whole life. Back in 2005, when my son was doing his senior project with his girlfriend (who is now his wife) they were creating a scrapbook of their lives. They elicited my help as I was the crafty one. We took a ride to The Dollar Tree (our local dollar store) who had a section dedicated to scrap booking; papers, albums, embellishments, flowers, and we grabbed everything we could find. I knew about the new way of scrap booking, but never gave it any thought until that trip to The Dollar Tree. After helping the kids create their albums , I was curious as to where I could get more "scrap booking stuff". So I drove to our local Michaels and Joanns craft stores. That is when I became hooked.
Then I sought out the local scrapbook stores and found stuff I didn't even know existed until then. I shopped and shopped and shopped some more. I found online galleries and saw the kind of scrap booking the artists did. I was amazed at the styles. It just wasn't cut and paste and stickers. It was 3 dimensional, it was gorgeous, and oh so inspiring. So I created a gallery, created my first layout and the rest is history. My style has changed a bit throughout the years, but playing with paper and scissors is still so therapeutic for me. I am honored and excited to be Guest Designing for C'est Magnifique.
Thanks everyone! Be sure to stop back on June 1st when we will be posting our June Sketch Challenge.


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DURBAN, South Africa (4 December 2011)__ Latest draft texts on REDD+ that will go forward to a plenary session for approval have postponed a decision on financing to next year and watered down safeguards requirements, leaving REDD+ projects in limbo and indigenous groups unprotected.
The UNFCCC's ad hoc working group on the Long-term Cooperative Action (AWG-LCA) asked the secretariat to prepare a technical paper to review what kind of financing sources will be suitable for specific REDD+ activities, said Louis Verchot, leading climate change scientist at the Center for International Forestry Research (CIFOR). "REDD projects have to go another whole year without any clear indication as to how this whole system is going to be financed," he said at the sidelines of the UN climate summit in Durban.
Another disappointment is in the scientific sub-committee SBSTA's "very weak" text on safeguards to protect local communities, indigenous peoples and biodiversity. The draft reduces requirements from collecting data and measuring impacts of REDD+ to merely reporting how developers implement safeguard measures, said Verchot. "Safeguards aren't going to be safeguards." (see the text in two parts here and here)
The decisions, or lack thereof, were made in "a void" as negotiators struggle to agree on what will happen after Kyoto Protocol, the current legally-binding climate treaty that doesn't include REDD+ and that expires in 2012. "We have no idea how to fund" REDD+, said Verchot. As such, "we don't have an objective, we don't have a means to an objective. We're going to make all the decisions about all the rules of how we're going to get to our objective, but we don't even know where we're going."
REDD+ aims to reward developing nations for reducing emissions by protecting, restoring and sustainably managing forests. AWG-LCA prepares the draft text on REDD+ finance (see pages 30-31 here) while the Subsidiary Body of Scientific and Technological Advancement (SBSTA) makes recommendations to the Conference of the Parties on how to measure, report and verify carbon emissions as well as on safeguards.
Financing decision in 2012
The AWG-LCA wants to know what sources of financing including public and markets can do for the different kinds of activities under REDD+, such as to reduce deforestation, reduce degradation, and manage forest in a sustainable manner, said Verchot. The ad hoc group will make a recommendation for REDD+ financing based on this information next year.
While acknowledging that such a review is good to have, "we're seven years into REDD and we're still looking at what the options are. We're not making the hard decisions." Asia Pacific negotiators, including China, said that they hoped to reach an agreement on financing for REDD+ in Durban.
The crux of the matter is that there's no certainty on whether any binding agreement to cut emissions will replace Kyoto, said Verchot. "REDD is sort of cut loose… it's being launched into the world, it's out there, but how it relates to anything else is not clear."
Very weak safeguards
The final draft of the text suggests that countries would only have to submit qualitative information on how safeguards were implemented, said Verchot. "We have no way of actually measuring impact" of REDD+ on communities, which requires collecting and comparing before-and-after data, said Verchot.
The weakened safeguards text was probably adopted to accommodate developing countries' frustration towards donors' complex and costly requirements, which often differ from one agency to the next, at a time when only little money is flowing in for REDD+, said Verchot. "The feeling among developing countries is that they need to start getting something out of the program and there needs to be fewer hoops to jump through before receiving the promised support," he said.
The current text significantly lightens the load for forested nations. "At the same time, there's a need to ensure that local communities are not being harmed," said Verchot. In the current form, safeguards are merely principles to adhere to, he said. "If we're performing or not performing, it's not going to matter, because we're not going to measure it."
Other issues, such as reference emissions levels, did not change much from the previous draft (see CIFOR story on these here). SBSTA is going to ask the Intergovernmental Panel on Climate Change to reformulate their existing methods and guidelines for measuring, reporting and verification (MRV) to meet the need for REDD+. This will help to ensure that the forest carbon accounting is consistent with the national emissions accounting guidelines, said Verchot, who estimated this process to take two years.
For other reports from COP17, visit the blogs of these organizations:
– The Center for People and Forests (RECOFTC)
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