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  • 42 hours (7 weekends) Live, Online, Instructor led, Practical training
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  • Interact with the instructors and fellow participants through chat, voice and video as if you are in a classroom
  • We will award you with a certificate after working on a real-world project post the course
  • Online sessions will be recorded for you to revise later or if you miss a class
  • 24x7 lifetime access to the recorded sessions, course material and Python lab (installed on your own machine)
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What does it mean?

Introduction to Data Science

  • What is data science and why is it so important?
  • Applications of data science
  • Various data science tools
  • Data Science project methodology
  • What is Machine Learning?
  • Case study
In this section we shall provide you an overview into the world of data science & machine learning. You will learn about the various applications of data science, how companies are using it to prosper and go through a case study.

Introduction to Python

  • Installation of Python framework and packages
  • Working with Jupyter notebooks
  • Creating Python variables
  • Numeric , string and logical operations
  • Data containers : Lists , Dictionaries, Tuples & sets
  • Practice assignment
Python is one of the most popular & powerful languages for data science used by most top companies like Facebook, Amazon, Google, Yahoo etc. It is free and open source. This module is all about learning how to start working with Python. We shall teach you how to use the Python language to work with data.

Iterative Operations & Functions in Python

  • Writing for loops in Python
  • While loops and conditional blocks
  • List/Dictionary comprehensions with loops
  • Writing your own functions in Python
  • Writing your own classes and inheritance
  • Practice assignment
This is where you shall learn the functionalities and powerful capabilities of Python that will make it easy for you to work with data and set the stage for using Python for machine learning & data science.

Data visualization in Python

  • Need for data visualization
  • Basics of visualisation with Seaborn
  • Inferential visualisation with Seaborn
  • Practice assignment
Data visualization is extremely important to understand what the data is saying and gain insights in just one glance. Visualization of data is a strong point of the Python software using the latest Seaborn package and you will learn the same in this module.

Data Handling in Python using NumPy & Pandas

  • Introduction to NumPy arrays
  • Introduction to Pandas data frames
  • Import and export external data in Python
  • Numeric & visual summaries of the data
  • Feature engineering using Python
  • Case study
In this module you will learn to create/import and handle data using NumPy and Pandas which are powerful tool-kits in Python to help with data handling. You will learn about handling pandas data frame, creating quick data summaries, both numeric and visual & feature engineering to make your analysis more accurate.

Data Science & Machine Learning in Python

Introduction to Machine Learning

  • Understanding supervised and unsupervised learning with examples
  • Understanding biases associated with any machine learning algorithm
  • Ways of reducing bias and increasing generalisation capabilites
  • Introduction to scikit-learn & SciPy in Python
  • Practice assignment
In this module we understand what are the machine learning problems you will solve as data scientists. Then you will get updated on methodologies associated with solving such problems. These methodologies will form basis of techniques we learn ahead in the course. You will also learn more about the scikit-learn library in Python which provides the ability to solve machine learning problems

Generalised Linear Models in Python

  • Linear Regression
  • Regularisation of Generalised Linear Models
  • Ridge and Lasso Regression
  • Logistic Regression
  • Case Study
Generalized linear models (GLMs) are indispensable tools in the data science toolbox. GLM provides techniques in predictive analytics which help to predict the future .

Tree Models using Python

  • Introduction to decision trees
  • Tuning tree size with cross validation
  • Introduction to bagging algorithm
  • Random Forests
  • Grid search and randomized grid search
  • Case Study
In this module you will learn a very popular class of machine learning models which are rule based tree structures also known as Decision Trees. We'll examine the biased nature of these models and learn how to use bagging methodologies to arrive at a new technique known as Random Forest to analyse data.

Boosting Algorithms using Python

  • Concept of weak learners
  • Introduction to boosting algorithms
  • Adaptive Boosting
  • Extreme Gradient Boosting (XGBoost)
  • Case Study
Want to win data science contest on Kaggle or data hackathons or be known as a top data scientist? Then learning boosting algorithms is a must as they provide a very powerful way of analysing data and solving hard to crack problems.

Support Vector Machines (SVM) in Python

  • Brief mathematical background on SVM/li>
  • Classification with SVM
  • Regression with SVM
You will learn SVM in this module which is an advanced machine learning technique

Introduction to Text Mining in Python

  • Scraping data from the web
  • Creating a corpus
  • Cleaning text data using Python
  • Classification
  • Sentiment analysis
  • Case study
Text data forms a big chunk of data available in the world today. Analysing text data can give a business very powerful insights to take advantage of. Learn the basics of text mining using Python in this module
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  • Edvancer’s content is better than other institutes with whom I enquired and at much economical cost. After the course I got a job as a Campaign Management Analyst using SAS in ICICI Lombard.

    Rohit Kashid – Campaign Analyst, ICICI Lombard
  • Vinodh S1

    It was a great experience and pleasure to learn from Edvancer.  The online class room is as good as a real class room. It was highly interactive with brainstorming on many ideas. The course content also depicts real life scenarios. Altogether it was a great learning experience.

    Vinodh S, Sr. Specialist Architect, Sapient Corp.
  • sumit kamra - Edvancer's Student

    The course was of very high quality and engaging. The interactive atmosphere and live examples were refreshing. The instructor had the real world experience to understand our needs and was easily reachable at any point of the time. I highly recommend this course.

    Sumit Kamra, Project Manager, ICICI Bank
  • girish punjabi

    The business analytics course provides an in-depth understanding of analytics with hands-on experience on SAS using case studies from varied domains. You get all one needs for excelling in the field of analytics. The faculty have a very good grasp of all the concepts and the Edvancer team is very supportive.

    Girish Punjabi, Senior Business Analyst, IKen-IIT Bombay
  • ashish

    I got a great job as Sr. Analyst with a 75% pay hike post this course! The course is a perfect blend of analytics tools and techniques. If you want to learn real stuff in analytics and not just the theoretical concepts, this course is for you.

    Ashish Kumar – B.Tech, IIT Madras

Benefits of taking the Data Science with Python course

  • Learn to analyze data using machine learning techniques in Python
  • Become one of the most in-demand Data Scientists in the world today
  • Learn how to analyze large amounts of data to bring out insights
  • Relevant examples and cases make the learning more effective and easier
  • Gain hands-on knowledge through the problem solving based approach of the course along with working on a project at the end of the course

Who should take this course?

This course is designed for anyone who:
  • wants to get into a career in Data Science
  • wants to analyse large amounts of data to bring out the insights from the same
  • wants to learn Python for working on data science projects


  • Ideally you should be familiar with some programming(in any language).