Data Science with Python

This course combines statistical and machine learning techniques with Python programming to analyze and interpret complex data

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This Data Science Training programe provides comprehensive coverage of Data Science and Statistics, along with hands-on learning of Python. This training lets you gain expertise in Machine Learning Algorithms. It helps in data analysis and their related methods to understand and analyze Big Data.

Who can learn ?

This Training is designed for the IT Beginners/professionals and Developers who want to make their career as Data Scientist, Business Analysts and Analysts Managers


Knowledge of understanding basic programming concepts and basic knowledge in statistics will be added advantage.

Date Weekdays / Weekend Timings
december 12 MON - FRI (45 Days) 7:00 AM to 9:00 AM (IST)




  • Python and its uses
  • Installing Python and PyCharm
  • Hello world Program in Python
  • Some mathematical operations in Python
  • Strings in Python
  • Accepting Input from the user in Python
  • Performing operations on a string in Python
  • Variables in Python
  • In place operators in Python
  • Writing the first program in PyCharm

Control structures in Python

  • If statement
  • Elif statement
  • Introduction to list in Python
  • List operations in Python
  • List functions in Python
  • Range function in Python
  • Code reuse and functions in Python
  • For loop in Python
  • Boolean logic in Python
  • While Loop in Python

Functions and Modules in Python

  • Passing arguments to functions
  • Making function return value
  • Passing functions as arguments
  • Modules

Exception Handling and File Handling in Python

  • Errors and Exceptions
  • Appending to a file

Exception handling

Finally Block

File Handling

Reading Data from file

Adding data to file

Some More Types in Python

  • Dictionaries
  • Numeric Functions

Dictionary Functions


List Slicing

List Comprehension

String Formatting

String functions

Functional Programming in Python

  • Functional Programming
  • Generators




Business Statistics

Introduction to Analytics

  • Analytics Industry Overview
  • Application of Analytics & Challenges to Analytics

Data Understanding

  • Data Types
  • Cumulative Distributions

Summarizing Techniques

Five Number Summary

Histograms & Ogives

Box Plots

Scatter Diagram

Frequency Tables and Distribution

Measure of Central Tendency, Dispersion and its importance

  • Understanding Range
  • Standard Deviation

Inter Quartile Range


Probability and Probability Distribution

  • Introduction to Probability

Types of Probability

Probability Rules

Probability Distribution

Random Variables

  • Discrete Random Variable
  • Continuous Random Variable

Discrete Distributions

  • Binomial Distribution
  • Poisson Distribution

Continuous Distribution

  • Normal Distribution
  • Standard Normal Distribution
  • Z scores

Sampling and Sampling Distribution

  • Introduction to Sampling

Random Sampling & Non Random Sampling

Sampling Techniques

  • Stratified Sampling Method
  • Cluster Sampling Method

Sampling Distribution

Central Limit Theorem

  • Standard Error Concept

Statistical Inference

  • Estimation
  • Introduction
  • Point Estimates and Interval Estimates
  • Calculating Interval Estimates using ‘Z’ table
  • Introduction to ‘t’ distribution o Degrees Of Freedom
  • Calculating Interval Estimates using ‘t’ table
  • Confidence Intervals with t & z distributions
  • Determining Sample Size in Estimation

Hypotheses Testing

  • Introduction
  • One Sample Test & Two Sample Tests
  • Z test
  • t test
  • One Tail & Two Tail Test
  • Dependent & Independent Samples

Concept of p-value


  • Introduction
  • F distribution
  • One way ANOVA
  • CHISQ Test
  • Some Non Parametric Tests

Simple Regression & Correlation

  • Introduction
  • Limitations & Errors in Simple Regression Analysis
  • Residual Analysis for Regression Assumptions

Dependent and Independent Variables

Correlation Analysis

Estimation in Regression

Least Squared Method

Standard Error Of Line

Finding Regression Equation

Hypotheses Testing for estimates

Multiple Regression analysis: Introduction


Fitting the model

Regression Assumptions

Logistic Regression

  • Understanding Logistics Regression
  • Difference between linear and logistics regression
  • Model Suitability

Odds Ratio

Logit Model

Building Models

ROC concept

Model Fitting

Evaluation of goodness of fit


  • Introduction

Environment Set-up

Jupyter Overview

Python for Data Analysis

  • Welcome to the Numpy
  • Numpy Arrays
  • Quick Note on Array Indexing
  • Numpy Array Indexing
  • Numpy Operations
  • Numpy Exercises
    • Welcome to Pandas Series
    • Data Frames
    • Missing Data
    • Groupby
    • Merging, Joining and Concatenating
    • Operations
    • Data Input and Output

Python for Data Analysis

  • Pandas Exercise

Python for Data Visualization

Welcome to Matplotlib

  • Python for Data Visualization
  • Introduction to Seaborn
  • Distribution Plots
  • Categorical Plots
  • Matrix Plots
  • Grids
  • Regression Plots
  • Style and Color
  • Seaborn Exercise

Python for Data Visualization – Pandas Built-in Data Visualization

Pandas Data Visualization Exercise

Python for Data Visualization – Plotly and Cufflinks

Python for Data Visualization – Geographycal Plotting

Introduction to Geographical Plotting

  • Choropleth Maps
  • Choropleth Exercises

Capstone Project

Introduction to Machine Learning

Linear Regression

  • Theory
  • Model selection update for Scikit Learn 0.18

Linear Regression with Python Linear Regression Project

Cross Validation and Bias-Variance Trade-off

Logistic Regression

  • Theory
  • Logistic Regression with Python

Logistic Regression Project

K Nearest Neighbors

  • Theory
  • KNN with Python

KNN Project

  • Decision Trees and Random Forests
  • Decision Trees and Random Forest Project
  • Support Vector Machines
  • K Means Clustering
  • NLP Project
  • Neural Nets and Deep Learning
  • Introduction to Tree Methods
  • Decision Trees and Random Forest with Python
  • Theory
  • Support Vector Machines with Python

SVM Project

  • Theory
  • K Means with Python

K Means Project

Principal Component Analysis

  • Theory
  • PCA with Python

Recommender Systems

  • Theory
  • Recommender Systems with Python

Natural Language Processing

  • Theory
  • NLP with Python
  • Neural Network Theory
  • Welcome to Deep Learning
  • What is TensorFow?
  • Changes with TensorFlow
  • TensorFlow Installation
  • TensorFlow Basics
  • MNIST with Multi-Layer Perceptron
  • TensorFlow with ContribLearn

TensorFlow Project

Frequently Asked Questions

When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 3 days and we will give you a full refund.

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