Admissions open for August 2017 batch

Advanced Post Graduate Program in Data Analytics

  • Large IT Companies who have an Analytics Practice
  • Analytics KPOs
  • In-house Analytics Units of Large Corporates
  • Niche Analytics Firms
  • The Post Graduate program in Data Analytics is truly your gateway to learning about data analysis, visualization, predictive modeling. The program Start off with Basic courses that build a strong foundation for more in-depth and application-based learning in the Advance analytics Program in the second stage of the program.
  • Given the need for specialist knowledge, we provide a range of courses in cutting-edge topics like data mining, visualization techniques, predictive modeling, Basics of SQL, Ubuntu and statistics.
  • On completion of the program, students would have learned to apply data analysis techniques to solve real-world business problems, successfully present results using data visualization techniques, demonstrate knowledge of statistical data analysis techniques utilized in business decision - making.
  • B.E., B.Tech. or Graduates with minimum 50 % grades.
  • B.Sc., M.Sc. (IT, Computer Science, Mathematics, Statistics, Electronics, Economics)
  • MBA (all streams)
  • Prior programming knowledge and relevant experience is preferred.
  • Regular Batch -  16th August 2017 
  • Weekend Batch - 19th August 2017
Statistics Theory
  • Introduction to Statistical Concepts
  • Variables and Data Types
  • Bar, Line Chart, Histogram, pie chart, Box plot
  • Measures of data-Measure of center - Mean, Median, Mode
  • Measure of Spread - Range, variance, standard deviation
  • Measure of shape - Skewness, Kurtosis
  • Statistical Distributions
  • Test of Association - Correlation, Regression
  • Test of Inference - Chi-Square, t-test, Analysis of Variance
  •  One-Way ANOVA
  • ANOVA with Data from a Randomized Block Design
  • Stepwise Regression and Diagnostic tests for regression
  • Categorical Data Analysis
  • Regression Modeling 
SQL Training
  • SQL Overview
  • SQL SELECT statements
  • SQL Functions and Expressions
  • SQL Updating
  • SQL Joins, SQL Sub queries and Unions
  • SQL Summarization
R Studio
  • Introduction to R and R studio
  • R Installation - R GUI and Rstudio, R Studio tour
  • R packages overview and understanding in-built functions
  • Vectors
  • Matrices, Data frames and Data import
  • Visual Analytics
  • Summarizing the data and probability distribution of data
  • Testing of hypothesis and Confidence Interval
  • Linear Regression
  • Logistic Regression
  • Decision Trees/CART -  Classification and Regression Trees Explanation
  • Confidence Interval and Sample size determination
  • Supervised and Unsupervised learning
  • Difference between classification and regression algorithms
  • Naïve Bayes Classifier
  • Principal Component Analysis
  • Factor Analysis
  • Discriminant Analysis
  • Time Series Analysis
  • Decision Tress: CART
  • k-means clustering
  • Market Basket Analysis
Hadoop
  • Hadoop Architecture
  • Basic Features: HDFS Data Characteristics
  • Map Reduce Architecture
  • HDFS Architecture
  • Hive Architecture.
Tableau – Data Visualization
  • Visualization Design and Data Types
  • Tableau and Data Connections
  • Chart Types, Dashboards and Work Sharing
Spark & Scala
  • Spark - Intro - distinguish between spark and Hadoop
  • Spark Architecture 
  • RDD Fundamentals
  • Basic Scala Programming 
  • Basics: Primitive Types, Type inference, Vars vs Vals methods
  • Classes: Introduction, Objects, Collections
  • Lists : Collection Manipulation, Simple Methods
  • Spark SQL introduction 
  • Spark SQL Data frames
  • Spark Job Extraction 
Base SAS
  • SAS Programs introduction to SAS programs
  • Accessing Data
  • Producing Detail Reports
  • Formatting Data Values
  • Reading SAS Data Sets
  • Reading Spreadsheet and Database Data
  • Reading Raw Data Files
  • Manipulating Data
  • Combining Data Sets
  • Creating Summary Reports
  • Summarizing Data
  • Data Transformations
  • Debugging Techniques
  • Processing Data Iteratively
  • Restructuring a Data Set
Python Scikit Learn
  • Introduction to Python
  • Data Types. Strings. Operators, expressions and delimiters.
  • Conditionals and Loops
  • Lists and Tuples
  • Modules in Python - Introduction to Numpy, Scipy and Pandas.
  • Basics of Machine Learning
  • Supervised learning
  • unsupervised learning
  • Introduction to SKLEARN
  • Sklearn library
  • Classification / regression, Linear Regression.
  • Simple Linear Regression, Decision Tree, Logistic Regression.
  • Support Vector Machine
  • introduction to clustering, types of clustering, running the k-means algorithm & Building the model
  • Sentiment Analysis - Text Classification
  • Into to H20
 
SAS Macro
  • Macro Variables
  • Macro Definitions
  • DATA Step & SQL Interfaces
  • Macro Programs
SAS SQL
  • Basic Queries
  • Displaying Query Results
  • SQL Joins
  • Sub queries
  • Set Operators
  • Creating Tables and Views
  • Advanced PROC SQL Features
Excel
  • Reference Functions- VLOOKUP, HLOOKUP, Relative / Absolute referencing, Multilevel sorting
  • Linkage with External files, SmartArt, Name Range, Data Validation, Statistical Functions
  • What if Analysis - Goal Seek, Data Table, Scenario Manager
  • Database Functions - DSUM, DMAX, DAVERAGE etc.
  • Macro - Steps / Dos’ Don’ts’, Running Recorded Macro
Regular Mode Rs.1,48,000/- only
Weekend Mode

MITSkills Provide Post Graduate program in Data  Analytics allows students to develop a thorough understanding of Big Data analysis, determine the changing role of Information Sciences and bring in creative solutions to tackle the challenges that arise.

The McKinsey Global Institute has predicted that by 2018, the US alone could face a shortage of between 140,000 to 190,000 people with deep analytical skills, and a shortage of 1.5 million managers and analysts who can leverage data analysis to make effective decisions for their organizations.

Program Highlights

The course is designed to provide in-depth knowledge in the area of Big Data and Business Analytics. It Develops essential data science skills like data mining, data modeling, data architecture, extraction, transformation, loading development and business intelligence development with Top leading technologies like SAS, R, Python, Hadoop etc

Program Duration

  • Regular Batch (6 Months)
  • Weekend Batch (8 Months)

Learning Methodology

  • Instructor-led classroom training using a combination of lectures by experienced faculty, case studies and live project work.

Careers on Completion

This program equips students to fill the need for varied domains such as IT, Consulting, BFSI, Telecom and Media etc.

Sample Job Titles available in various industries: Data Analyst, Big Data Analytics - Banking Domain, Data Scientist, Data Consultant- Analytics, Data Mining, Head - Data Analytics, Senior Data Scientist, Business Intelligence officer, Business Analyst, Digital Analytics Big Data Consultant, Data Scientist (Predictive Analysis)... and more

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