Learn Data Science – Do Programming using Python & R

Course Name: – Learn Data Science – Do Programming using Python & R

Date: – Sat 26th May and Sun 27th May 2018

Cost: – 

Booking between 08 Apr 2018 to 21 Apr 2018 – 1000 INR discount, you pay 6000 INR
Booking between 22 Apr to 12 May 2018 – 500 INR discount, you pay 6500 INR
Booking between 13 May to 25 May 2018 – 0 INR discount, you pay 7000 INR

How to Join?

Click Here

Key Features

  • No PPT’s completely Hands-on Data Science – R programming training.
  • Lunch & Tea will be provided.
  • 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 7000 INR

Why to choose Data Science as career? 

Day 1 – First Day

1st and 2nd Hour Python basics (2 hrs)

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

3rd and 4th Hour NumPy (2hrs)

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

5th and 6th Hour Python Pandas (2 hrs)

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

7th Hour Data Loading, Storage, and File Formats (1 hr)

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

8th Hour and 30 mins matplotlib API (1 hr & 30 mins)

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

9th Hour ETL operations (30 mins)
10th Hour SciPy (1 hr)

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

Day 2 – Next Day

1st and 2nd Hour Introduction to R (2 hrs)

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

3rd Hour Functions ( 1 hr)

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

4th Hour Data visualization in R (1 hr)

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

5th and 6th Hour Introduction to statistics (2 hrs)

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

7th and 8th Hour Classification (2 hrs)

Classification Decision Tree
Logistic Regression
Bayesian
Support Vector Machinesa

Clustering

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

Association

Apriori Algorithm
Candidate Generation
Visualization on Associated Rules