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Data Science

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R Programming:

R - Core

  1. R Environment, R Studio, R installation , Advantages of R, R scripts
  2. Variables
  3. Data Types
  4. Creating Functions
  5. Installing and using Packages
  6. Reading Data into R ( Excel, CSV, JSON )
  7. Control Statements and Loops ( if-else, while, for etc.)
  8. Data Reshaping, Data manipultation, Formatting
  9. Graphs, using various modules ( Bar, Line, Histogram,pie etc)

 Statistics with R

  1. Summary
  2. Correlation and Covariance
  3. T-Tests
  4. ANOVA
  5. Probability Distributions ( Normal, Bionomial, Poission Distributions )
  6. Probability Distributions ( Linear Regression, Logical regression, Market Basket Analysis,Naive Bayes)
  7. Neural Networks ( Artificial Neural networks ) / Machine Learning / Deep Learning
  8. Principal Component Analysis
  9. Time Series Analysis
  10. Unsupervised learning: Clustering
  11. Decision Trees
  12. K Nearest Neighbours (kNN)

Very Advance and important for projects

  1. RServer
  2. RShiny
  3. RMarkdown

Python: ( Eclipse with PyDev)

  1. Introduction to Python, Concepts of Pythons language,  Installations.
  2. Basic Syntax and Indenting, Variables, Data types, Collections ( Lists,Tuples, Dictionary)
  3. Control Statements and Loops ( if-else, while, for etc.)
  4. Functions, Built-in, User Defined
  5. Lambda, Filter, Zip and Reduce
  6. Modules, Standard Modules, Importing into code
  7. Exceptions. Built in, User defined
  8. Writing, Reading files ( Excel, CSV, JSON )
  9. Using the pickle module (Shelve)
  10. Regular Expressions

Object Oriented

  1. Classes
  2. Inheritance, Polymorphism and other OOP concepts

Database:

  1. connections to various databases ( MySQL, Postgresql : Any One)
  2. DB Related statements ( Create, Select, insert, update etc )

Important Features

  1. Decorators
  2. Python Threads, locks (Rlock) and multiprocessing with some basic examples.
  3. Iterators and generators.

Data science: ( Basic and Relevant concepts in Statistics )

Basics

  1. What is Sample and Population?
  2. What is Variable? How many types of Variables?
  3. What is Percentile? What are plots?
  4. What is Central Limit theorem? Mean, Median, Mode
  5. Standard deviation, Skewness, Degrees of freedom
  6. What is covariance? What is correlation?
  7. Bernoulli, Binomial, Poisson, Normal distribution
  8. Decision Making Rules,
  9. Test of Hypothesis
  10. Type I Error, Type II Error
  11. Null and Alternative Hypothesis
  12. Reject or acceptance criterion
  13. One sample Test, Two sample Test, ANOVA, P Value

Prediction Modelling

  1. Simple Linear Regression
  2. Multiple Linear Regression
  3. Extra sum of squares
  4. R – Square, R- Square Adjusted
  5. Variable Selection
  6. Multicollinearity
  7. Transformations
  8. Logistic Regression

Artificial Intelligence / Machine learning

  1. Supervised and Unsupervised Learning
  2. Neural Network
  3. Naïve Bayes
  4. Laplace Estimator
  5. Market Basket Analysis
  6. KNN – Classifier
  7. Random Forests

ML/DL using Python - Very Advance and important for projects

  1. Tensorflow - Image Preiction
  2. Tensorflow - Image classifier /
  3. Tensorboard
  4. Pandas ( Data structures & Analysis )
  5. Keras (  Deep Learning)
  6. Numpy ( fundamental package for scientific computing with Python)
  7. Scipy ( Python-based ecosystem of open-source software for mathematics, science, and engineering)
  8. Matplotlib ( Graphs)
  9. Jupyter Notebook - Interactive Web
  10. convolutional Neural network

Mini Project

Pre Requisites: C, C++ or any Programming Language knowledge.

Target Audience: B.E/B.Tech/MCA/M.Sc