Data Science

Introduction to statistics

Summarizing Data

• Central Tendency measures – Mean, Median and Mode
• Measures of Variability – Range, Inter quartile Range, Standard Deviation and variance
• Measures of Shape – Skew ness and Kurtosis
• Co-variance, Correlation

Data Visualization

• Histograms
• Pie charts
• Bar Graphs
• Box Plot

• ‘f’ Test
• ‘z’ Test
• ‘t’ Test
• Chi-Square test

• Expected value and variance
• Discrete and Continuous
• Bernoulli Distribution
• Binomial Distribution
• Poisson Distribution
• Normal Distribution
• Exponential Distribution
• Empirical Rule Chebyshev’s Theorem

• Overview
• Random sampling
• Stratified sampling
• Cluster sampling
• Central Limit Theorem

• Type I error
• Type II error
• Null and Alternate Hypothesis
• Reject or Acceptance criterion
• P-value

• ANOVA
• Assumptions
• One way
• Two way

• What is Machine Learning?
• Statistics (vs) Machine Learning
• Types of Machine Learning
  1. Supervised Learning
  2. Un-Supervised Learning
  3. Reinforcement Learning

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

Association Rules (Market Basket Analysis)

• Apriori

Cluster Analysis

• Hierarchical clustering
• K-Means clustering

Dimensionality Reduction

• Principal Component Analysis
• Discriminant Analysis (LDA/GDA)

• Confusion Matrix
• ROC Curve (AUC)
• Gain and Lift Chart
• Kolmogorov-Smirnov Chart
• Root Mean Square Error (RMSE)
• Cross Validation
  
  1. Leave one out cross validation (LOOCV)
  2. K-fold cross validation

• Introduction to Natural Language Processing
• Sentiment Analysis
• Text Similarity
• R Programming Language

Introduction

• R Overview
• Installation of R and RStudio software
• Important R Packages
• Datatypes in R – Vectors, Lists, Matrices, Arrays, Data Frames

Decision making & Loops

• If-else, while,for
• Next, break.try-catch

Functions

• Writing function
• Nested functions

Built-in functions

• Vapply, Sapply, Tapply, Lapplyetc.

Data Preparation/Manipulation

• Reading and Writing Data
• Summarize and structure of data
• Exploring different datasets in R
• Sub Setting 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.

Introduction

• How is Python different from R
• Installing Anaconda- Python
• Setting up with spyder

Datatypes in Python

Importing modules

Introduction to Strings

String manipulation

• For
• While
• If else

• Lambda
• apply

Numpy

• Introduction to Dataframes
• Conversion of written R codes into python

Scipy-Machine Learning in Python

Beautiful Soup

Matplotlib