1) Introduction
2) Data types and operator
3) List tuples and dictionaries
4) Object oriented
5) Exceptions handling
6) File handling
7) Modules

Introduction
Environment
Ndarray Object
Data Types
Array Attributes
Array Creation Routines
Array from Existing Data
Array From Numerical Ranges
Indexing
Slicing
Broadcasting
Array Manipulation
Binary Operators
String Functions
Mathematical Functions
Arithmetic Operations
Statistical Functions
Sort, Search & Counting Functions
Byte Swapping
Copies & Views
Matrix Library
Linear Algebra
I/O with NumPy

Introduction
Data Structures
Series
DataFrame
Panel
Basic Functionality
Descriptive Statistics
Function Application
Reindexing
Iteration
Sorting
Text Data
Options
Customization
Indexing
Selecting Data
Statistical Functions
Window Functions
Aggregations
Missing Data
GroupBy
Merging/Joining
Concatenation
Date Functionality
Timedelta
Categorical Data
Visualization
IO Tools
Sparse Data

Reading and Writing Data in Text Format
Reading Text Files in Pieces
Writing Data Out to Text Format
Manually Working with Delimited Formats
JSON Data
XML and HTML: Web Scraping

Figures and Subplots
Colors, Markers, and Line Styles
Ticks, Labels, and Legends
Subplot
Saving Plots to File
matplotlib Configuration
Plotting Functions in pandas
Line Plots
Bar Plots
Histograms and Density Plots
Scatter Plots
Python Visualization Tool Ecosystem

Introduction
Basic Functionality
Cluster
Constants
FFTpack
Integrate
Interpolate
Input and Output
Linalg
Ndimage
Optimize
Stats
CSGraph
Spatial
ODR

R Programming

Introduction to R
R Packages

R Programming
if statements
for statements
while statements
repeat statements
break and next statements
switch statement
scan statement
Executing the commands in a File
Data structures
Vector
Matrix
Array
Data frame
List

DPLYR & apply Function
Import Data File
DPLYP – Selection
DPLYP – Filter
DPLYP – Arrange
DPLYP – Mutate
DPLYP – Summarize

Bar chart, Dot plot
Scatter plot, Pie chart
Histogram and Box plot
Heat Maps
World Cloud

Type of Data
Distance Measures (Similarity, dissimilarity, correlation)
Euclidean space.
Manhattan
Minkowski
Cosine similarity
Mahalanobis distance
Pearson’s correlation coefficient
Probability Distributions

Hypothesis Testing
Introduction
Hypothesis Testing – T Test, Anova

Hypothesis Testing about population
Chi Square Test
F distribution and F ratio

Regression
Linear Regression Models
Non Linear Regression Models

Classification Decision Tree
Logistic Regression
Bayesian
Support Vector Machinesa

K-means Clustering and Case Study
DBSCAN Clustering and Case study
Hierarchical Clustering

Apriori Algorithm
Candidate Generation
Visualization on Associated Rules