Data Science Training

•  What is Data Science?
•  Why now?
•  Where Data Science is applicable?

Introduction to statistics

Summarizing Data

• • • Central Tendency measures – Mean, Median and Mode

    • Measures of Variability – Range, Interquartile Range, Standard Deviation and Variance

    • Measures of Shape – Skewness and Kurtosis

    • Covariance, Correlation Data Visualization

    • Histograms

    • Pie charts

    • Bar Graphs

    • Box Plot Probability basics

Parametric and Non parametric Statistical Tests

• • • ‘f’ Test

    • ‘z’ Test

    • ‘t’ Test

    • Chi-Square test Probability Distributions

    • Expected value and variance

    • Discrete and Continuous

    • Bernoulli Distribution

    • Binomial Distribution

    • Poisson Distribution

    • Normal Distribution

    • Exponential Distribution

    • Empirical Rule

    •  Chebyshev’s Theorem

Sampling methods and Central Limit Theorem

• • • Overview

    • Random sampling

    • Stratified sampling

    • Cluster sampling

    • Central Limit Theorem

Hypothesis Testing

• • • Type I error

    • Type II error

    • Null and Alternate Hypothesis

    • Reject or Acceptance criterion

    • P-value 

Confidence Intervals

 ANOVA

• • Assumptions

  • One way

  • Two way

Classification

• Nearest Neighbor Methods (knn)
• Logistic

Tree based Models – Decision Tree

• Basics
• Classification Trees
• Regression Trees

Probabilistic methods

• Bayes Rule
• Naïve Bayes Regression Analysis
• Simple Linear Regression
• Assumptions
• Model development and interpretation
• Sum of Least Squares
• Model validation
• Multiple Linear Regression Regression Shrinkage Methods
• Lasso
• Ridge

Advanced Models – Black Box

• Support Vector Machine
• Neural Networks

 Ensemble Models

• Bagging
• Boosting
• Random Forests 

Optimization

• Gradient Descent (Batch and Stochastic) 

Recommendation Systems

• Collaborative filtering

• User based filtering

Item based filtering

• Introduction to Natural Language Processing Sentiment

•  Analysis

• Text Similarity

Introduction

• R Overview

• Installation of R and RStudio software

• Important R Packages

• Datatypes in R – Vectors, Lists, Matrices, Arrays, Data FramesDecision making & Loops

• If-else, while, for

• Next, break. try-catch 

Functions

• Writing functions

• Nested functions

 Built-in functions

• Vapply, Sapply, Tapply, Lapply etc.Data Preparation/Manipulation

•  Reading and Writing Data

• Summarize and structure of data

• Exploring different datasets in R

• Subsetting Data Frames

• String manipulation in Data Frames

• Handling Missing Values, Changing Data types, Data Binning Techniques,Dummy Variables Data Visualization using ggplot2

• Basic charts – Histograms, Bar plots, Line graphs, Scatter plots etc.