Data Science with Python Training in Chennai

Data Science with Python Training in Chennai

Data science is a field that is poised to lead the tech revolution in the coming decades. As such expertise in this field is something that could prove to be life-changing. However, programming in the present as well as in the future cannot be compartmentalised and a quality programmer is expected to have a holistic skill set. Python is a high-level programming language that is capable of a great deal of application in the fields of AI and Data Science. With a well laid out framework and an exceptional set of trainers, we offer you the best Data Science with Python training program in Chennai.

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Data Science is the bedrock on which future businesses and policies are going to be built. Leading tech giants acknowledge that the data we are producing today on a daily basis is equivalent to the entire amount of data that was produced during the years preceding 2003. Data Science helps us recognise patterns, map deficiencies, analyse markets, predict consumer choice, manage law and order etc. Python is a programing language that can be grasped with ease. It provides for quality graphics and finds widespread application in game and web development.

While there are a number of IT institutes in the city we stand out as the best Data Science with Python training institute in Chennai. So why should you choose us for a data science and python course in Chennai?

– Data science is poised to be a major supplier of jobs in the near future as the strategic importance of the field increases.
– Every industry is going to require the application of data science in order to maintain competitiveness.
– Our Data Science training in Chennai makes you adept with the main tools used in data analytics.
– Instagram, Pinterest and many other sites are run with the help of python and its platforms.
– Our Data Science with Python training in Chennai gives you an in-depth industry oriented knowledge of python language, (including its various platforms) and data science tools.

For a promising future in programming enroll with us without wasting time.

Course objectives

The objective of the data science with python online training program is to make the learner acquainted with the fundamentals of Python language and its eco-system. The student will learn basic syntax, variables as well as types to build the foundation. Students will be introduced to the techniques used for creating, cleaning and manipulating Python lists. The course will encompass learning of functions, import packages, and Panda Data Frames. The student will also learn how to build numpy arrays and use them for complex calculations. By the end of the data science with python online training program, the student will be able to formulate clean Python lists and manipulate data to arrive at insights.

Who can learn this course?

The data science with python online training is intended for data analysts, software engineers, and statisticians with basic knowledge of a scripting language, preferably Python or some kind of a programming background. Any individual with some level of formal training in computer science is eligible for the course as a refresher of basic concepts is offered at the beginning of the course. The course aims to impart knowledge that will help the learners apply statistics, information visualization and analysis techniques to gain insights into the data.

Job Opportunity

Learning Python for your data science workflow will boost your career and help you climb up the corporate ladder. The multi-purpose language finds easy adaptability across multiple industries. Major companies like Yahoo, IBM, Disney, Google and Nokia use Python. Enroll for the data science with python online training program and lay the foundation for a successful career.

Course Syllabus

Data Science Overview

  • Data Science
  • Data Scientists
  • Examples of Data Science
  • Python for Data Science

Data Analytics Overview

  • Introduction to Data Visualization
  • Processes in Data Science
  • Data Wrangling, Data Exploration, and Model Selection
  • Exploratory Data Analysis or EDA
  • Data Visualization
  • Plotting
  • Hypothesis Building and Testing

Statistical Analysis and Business Applications

  • Introduction to Statistics
  • Statistical and Non-Statistical Analysis
  • Some Common Terms Used in Statistics
  • Data Distribution: Central Tendency, Percentiles, Dispersion
  • Histogram
  • Bell Curve
  • Hypothesis Testing
  • Chi-Square Test
  • Correlation Matrix
  • Inferential Statistics

Python: Environment Setup and Essentials

  • Introduction to Anaconda
  • Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
  • Jupyter Notebook Installation
  • Jupyter Notebook Introduction
  • Variable Assignment
  • Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
  • Creating, accessing, and slicing tuples
  • Creating, accessing, and slicing lists
  • Creating, viewing, accessing, and modifying dicts
  • Creating and using operations on sets
  • Basic Operators: ‘in’, ‘+’, ‘*’
  • Functions
  • Control Flow


  • Function definition and call
  • Function Scope
  • Arguments
  • Function Objects
  • Anonymous Functions
  • Packaging Importing


  • Classes and Objects
  • Creating object
  • Working with Class and Instance Variables Together
  • Accessing Object Variables
  • Accessing Object Functions
  • Inheritance
  • Multiple Inheritance
  • Constructor
  • Operator Overloading
  • Polymorphism
  • Encapsulation
  • Abstract class and methods

Pandas Section

  • Python Pandas – Introduction
  • Introduction to Data Structures
  • Python Pandas – Series
  • Python Pandas – DataFrame
  • Python Pandas – Basic Functionality
  • Python Pandas – Descriptive Statistics
  • Python Pandas – Indexing and Selecting Data
  • Python Pandas – Function Application
  • Python Pandas – Reindexing
  • Python Pandas – Iteration
  • Python Pandas – Sorting
  • Python Pandas – Working with Text Data
  • Python Pandas – Options and Customization
  • Python Pandas – Missing Data
  • Python Pandas – GroupBy
  • Python Pandas – Merging/Joining
  • Python Pandas – Concatenation
  • Python Pandas – IO Tools
  • Python Pandas – Comparison with SQL
  • Python Pandas – Dates Conversion
  • Plotting Data

Machine Learning Techniques

All algorithm will be explain by

  • What is mathematics behind it
  • Which scenario want to use
  • How it is different from other algorithm
  • How to interpret with Python
  • What insights getting out from result
  • Hypothesis Testing
  • Correlation
  • Outlier Detection
  • T-test
  • Anova
  • Chi-square
  • Linear regression
  • Multiple regression
  • Logistics Regression
  • Naïve Bayes classifier
  • K means clustering
  • Decision tree
  • SVM
  • Time series forecasting Overview
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