Advanced Post Graduate Program in Data Analytics | Training in Python Scikit learn | Advance Excel training | MITSkills - PowerPoint PPT Presentation

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Advanced Post Graduate Program in Data Analytics | Training in Python Scikit learn | Advance Excel training | MITSkills


MITSkills Provide Post Graduate Course in Data Analytics. This Masters Course in Business Analytics allows students to develop a thorough understanding of Big Data analysis, Hadoop and R Programming. – PowerPoint PPT presentation

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Title: Advanced Post Graduate Program in Data Analytics | Training in Python Scikit learn | Advance Excel training | MITSkills

Advanced Post Graduate Program in Data Analytics.
  • Advanced Post Graduate Program in Data Analytics.
  • 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
  • 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.This program equips students to fill
    the need for varied domains such as IT,
    Consulting, BFSI, Telecom and Media etc.
  • Careers on Completion
  • 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

  • Scope
  • Large IT Companies who have an Analytics
  • Analytics KPOs
  • In-house Analytics Units of Large Corporates
  • Niche Analytics Firms
  • Program Details
  • 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
  • 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
  • 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.

  • Eligibility
  • B.E., B.Tech. or Graduates with minimum 50
  • B.Sc., M.Sc. (IT, Computer Science, Mathematics,
    Statistics, Electronics, Economics)
  • MBA (all streams)
  • Prior programming knowledge and relevant
    experience is preferred.
  • Commencement
  • Regular Batch 19th February 2018
  • Weekend Batch 24th February 2018

  • Course Content
  • 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
  • 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
  • Categorical Data Analysis
  • Regression Modeling 

  • Course Content
  • 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
  • R packages overview and understanding in-built
  • Vectors
  • Matrices, Data frames and Data import
  • Visual Analytics

  • Course Content
  • Logistic Regression
  • Decision Trees/CART -  Classification and
    Regression Trees Explanation
  • Confidence Interval and Sample size
  • Supervised and Unsupervised learning
  • Difference between classification and regression
  • 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

  • Course Content
  • 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
  • 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

  • Course Content
  • 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

  • Course Content
  • Python Scikit Learn
  • Introduction to Python
  • Data Types. Strings. Operators, expressions and
  • 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
  • Sentiment Analysis - Text Classification

  • Course Content
  • SAS Macro
  • Macro Variables
  • Macro Definitions
  • DATA Step SQL Interfaces
  • Macro Programs
  • Basic Queries
  • Displaying Query Results
  • SQL Joins
  • Sub queries
  • Set Operators
  • Creating Tables and Views
  • Advanced PROC SQL Features

  • Course Content
  • 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 Donts, Running Recorded
  • Fees
  • Regular Mode - Rs.1,52,000/-
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