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Data Science

Freelance Project/Job During the course/We provide Full Framework Code./Free Post Training Support/Free Placement Assistance.

Data Science Course Outline:

Module1: Introduction to Data Science and Statistical Analytics
Module2: Introduction to R / Python
Module3: Data Exploration, Data Wrangling and R Data Structure
Module4: Data Visualization
Module5: Introduction to Statistics
Module6: Predictive Modeling – 1 (Linear Regression)
Module7: Predictive Modeling – 2 (Logistic Regression)
Module8: Decision Trees
Module9: Random Forest
Module10: Unsupervised learning
Module11: Association Analysis and Recommendation engine
Module12: Sentiment Analysis
Module13: Time Series
Module14: Data Science Project
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Introduction to Data Science and Statistical Analytics

  • Introduction to Data Science 
  • Use cases 
  • Need of Business Analytics 
  • Data Science Life Cycle 
  • Different tools available for Data Science
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Introduction to R / Python


  • Installing R and R-Studio
  • R packages
  • R Operators
  • If statements and loops (for, while, repeat, break, next)
  • Switch case

Python Basics

  • An introduction to the basic concepts of Python.
  • Learn how to use Python both interactively and through a script.
  • Create your first variables and acquaint yourself with Python's basic data types.

Python Lists

  • Learn to store, access and manipulate data in lists: the first step towards efficiently working with huge amounts of data.

Functions and Packages

  • To leverage the code that brilliant Python developers have written
  • you'll learn about using functions, methods and packages.
  • This will help you to reduce the amount of code you need to solve challenging problems!


  • NumPy is a Python package to efficiently do data science.
  • Learn to work with the NumPy array, a faster and more powerful alternative to the list, and take your first steps in data exploration.
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Data Exploration, Data Wrangling and R Data Structure

  • Importing and Exporting data from external source
  • Data exploratory analysis
  • R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List)
  • Functions
  • Apply Functions
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Data Visualization

  • Bar Graph (Simple, Grouped, Stacked)
  • Histogram
  • Pi Chart
  • Line Chart
  • Box (Whisker) Plot
  • Scatter Plot
  • Correlogram
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Introduction to Statistics

  • Terminologies of Statistics 
  • Measures of Centers
  • Measures of Spread
  • Probability
  • Normal Distribution
  • Binary Distribution
  • Hypothesis Testing
  • Chi Square Test
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Predictive Modeling – 1 (Linear Regression)

  • Supervised Learning – Linear Regression 
  • Bivariate Regression
  • Multiple Regression Analysis
  • Correlation (Positive, negative and neutral)
  • Industrial Case Study
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
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Predictive Modeling – 2 (Logistic Regression)

  • Logistic Regression
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Decision Trees

  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
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Random Forest

  • Random Forest
  • What is Naive Bayes?
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Unsupervised learning

  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • What is Canopy Clustering?
  • What is Hierarchical Clustering?
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Association Analysis and Recommendation engine

  • Market Basket Analysis (MBA)
  • Association Rules
  • Apriori Algorithm for MBA
  • Introduction of Recommendation Engine
  • Types of Recommendation : User-Based and Item-Based
  • Recommendation Use-case
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Sentiment Analysis

  • Introduction to Text Mining
  • Introduction to Sentiment
  • Setting up API bridge, between R and Tweeter Account
  • Extracting Tweet from Tweeter Acc
  • Scoring the tweet
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Time Series

  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective ETS model for forecasting
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Data Science Project

  • Project1
  • Project2
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