Learn Data Science – Do Programming using Python & R
Course Name: – Learn Data Science – Do Programming using Python & R
Date & Time: – Sat 02nd Mar to Sat 27th Apr 2019 every Saturday from 7 PM to 9:30 PM
Cost: –
Booking between 13 Jan 2018 to 26 Jan 2019 – 1000 INR discount, you pay 13000 INR
Booking between 27 Jan to 23 Feb 2019 – 500 INR discount, you pay 13500 INR
Booking between 24 Feb to 01 Mar 2019 – 0 INR discount, you pay 14000 INR
How to Join?
Click Here
Prerequisite
Good news for enrolled candidates of Data Science training where they will get chance to attend FREE sessions on Mathematics which are prerequisite required to accomplish Data Science training, see syllabus and other details here
Key Features
 No PPT’s completely Handson Data Science – R programming training.
 Installation required in your laptop for training
 R download link, get from here
 Python download link, get from here
 IPython download link, get from here
 For MAC system download link of Python, NumPy, SciPy & matplotlib get from here
 All at only 14000 INR
Why to choose Data Science as career?

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

NumPy
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 

Python Pandas
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 

Data Loading, Storage, and File Formats
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 

matplotlib API
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 

ETL operations 

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

Introduction to R
Introduction to R
R Packages
R Programming
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 

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

Data visualization in R
Bar chart, Dot plot
Scatter plot, Pie chart
Histogram and Box plot
Heat Maps
World Cloud 

Introduction to statistics
Type of Data
Distance Measures (Similarity, dissimilarity, correlation)
Euclidean space.
Manhattan
Minkowski
Cosine similarity
Mahalanobis distance
Pearson’s correlation coefficient
Probability Distributions
Hypothesis Testing I
Hypothesis Testing
Introduction
Hypothesis Testing – T Test, Anova
Hypothesis Testing II
Hypothesis Testing about population
Chi Square Test
F distribution and F ratio
Regression Analysis
Regression
Linear Regression Models
Non Linear Regression Models 

Classification
Classification Decision Tree
Logistic Regression
Bayesian
Support Vector Machinesa
Clustering
Kmeans Clustering and Case Study
DBSCAN Clustering and Case study
Hierarchical Clustering
Association
Apriori Algorithm
Candidate Generation
Visualization on Associated Rules 