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

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Advanced Post Graduate Program in Data Analytics.
http//www.mitskillsindia.com/
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• 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
  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

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• 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

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• Scope
• Large IT Companies who have an Analytics
  Practice
• 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
  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.

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• Eligibility
• 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.
• Commencement
• Regular Batch 19th February 2018
• Weekend Batch 24th February 2018

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• 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
  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 

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

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• Course Content
• 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
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•   Hadoop
• Hadoop Architecture
• Basic Features HDFS Data Characteristics

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• 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
  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

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• 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

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• Course Content
• 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

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• Course Content
• 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

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• 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
  Macro
• Fees
• Regular Mode - Rs.1,52,000/-