Learn Data Science Course

Industry based Training with 3 months of free internship and 100% Job Assisatnce.



Students Empowered



6 Months

Recommended 10-12 hrs/week

Sep 15 , 2021

Start Date


Hiring Partners

Students Hired by

Structured to fit into your life, guaranteed to get you a job

Learn at your own pace with 1-on-1 mentorship from industry experts and support from
student advisors and career coaches.

Unlimited 1:1 mentor support

Meet weekly with your personal mentor, with as many additional calls as you need.

100 hrs of Assured Internship

Leading Industry professionals provide you with oppurtunity to specialize in your respective field to gain experience and to reshape your skills.

Rigorous Hands-on experience

Learn by building a portfolio, including a capstone project and industry design project.

Guaranteed Job Placement

Leverage our dedicated career support team working with 200+ organizations.

Key Highlights

Program Overview

Data science Training is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.

It’s a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data.It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.

Who can benefit from this course?


For entrepreneurs just starting a company, the problems they are solving are largely around finding the right people and convincing them to join, solving for product/market fit, building a solid and attractive product and looking for cash.

Students and Job Seekers

To be a Data Scientist, first of all you need to look at your education background. You need a background in Mathematics and statistics. Further, you should be good at logical reasoning, aptitude and communication skills.

Sales and Marketing Professionals

Data Scientist help in a lot of ways–optimizing the budget within certain constraints, uncovering bloat in the budget, finding inefficiencies or problem areas in operations, designing and testing new products & etc.

We help you save-

Money – As you pay no more 10% of our competitors fee

Time: As this workshop covers 20 days course in just 2 days

Efforts – As you are taken through the real-time experiences and you can skip experimenting on your business.


Module 1: Introduction To DataScience

Module 2: Real Time UseCases Of DataScience

Module 3: Who is a DataScientist??

Module 4: Github Tutorial

Module 5: Skillsets needed for DataScientist

Module 6: 6 Steps to take in 3 Months for a complete transformation to DataScience from any other domain

Module 7: Machine Learning-Giving Computers The ability to learn from data

Module 8: Supervised vs Unsupervised

Module 9: Deep Learning vs Machine Learning

Module 10: Link to get Free Data to Practice?

Module 11: Some Great self Learning DataScience Resources(Books, Tutorials, Videos, Papers)

Python Fundamentals begins with acquiring an in-depth knowledge of the Python programming language. By the end of the week, students will be expected to program intermediate level scripts in Python.

Software Installation

Module 1: Introduction To Python

Module 2: “Hello Python Program” in IDLE

Module 3: Jupyter Notebook Tutorial

Module 4: Spyder Tutorial

Module 5: Introduction to Python

Module 6: Variable,Operators,DataTypes

Module 7: If Else, For and While Loops

Module 8: Functions

Module 9: Lambda Expression

Module 10: Filter, Map, Reduce

Module 11: Taking input from the keyboard


Module 1: Create Arrays

Module 2: Array Item Selection and Indexing

Module 3: Array Mathematics

Module 4: Array Operation


Module 1: Introduction to Pandas

Module 2: Series

Module 3: Series indexing and Selection

Module 4: Series Operation

Module 5: Introduction to Pandas

Module 6: Data Frames

Module 7: Data Collection from CSV, JSON, HTML,excel

Module 8: Data Merging,Concatenation, join

Module 9: Group By and Aggregate Function

Module 10: Order By

Module 11: Missing Value Treatment

Module 12: Outlier Detection and Removal

Module 13: Pandas builtin Data Visualisation

Visualization (matplotlib & seaborn)

we’ll begin curriculum focused on various data visualization techniques and how they can help us engage and learn from our data using Matplotlib, Seaborn, ggplot

Module 1: Line Plots

Module 2: Scatter Plots

Module 3: Pair Plots

Module 4: Histograms

Module 5: Heat Maps

Module 6: Bar Plots

Module 7: Count Plots

Module 8: Factor Plots

Module 9: Box Plots

Module 10: Violin Plots

Module 11: Swarm Plots

Module 12: Strip Plots

Module 13: Pandas Builtin Visualisation Library

This session is dedicated to creating a deep understanding of mathematical concepts we’ll later see in topics like machine learning and statistical analysis. Contrary to the traditional mathematics course, students will learn statistics and linear algebra in a programmatic way to fit a problem’s needs.

Module 1: Descriptive vs Inferential Statistics

Module 2: Mean, Median, Mode, Variance,Std. dev

Module 3: Central Limit Theorm

Module 4: Co-Variance

Module 5: Pearson’s Product Moment Correlation

Module 6: R – Square

Module 7: Adjusted R-Square

Module 8: Spearman’s. Rank order Coefficient

Module 9:Sample vs Population

Module 10: Standardizing Data(Z-score)

Module 11: Hypothesis Testing

Module 12: Normal Distribution

Module 13: Bias-Variance Tradeoff

Module 14: Skewness

Module 15: P Value

Module 16: Z-test vs T-test

Module 17: The F distribution

Module 18: The chi-Square test of Independence

Module 19: Type-1 and Type-2 errors

Module 20: Annova

Module 1: Introduction to Machine Learning

Module 2: Machine Learning Usecases

Module 3: Supervised vs Unsupervised vs Semi-Supervised

Module 4: Machine Learning process Workflow

Module 5: Training a model

Module 6: Validating results

Module 7: Overfitting vs Underfitting

Module 8: Ordinal vs Nominal data

Module 9: Structured vs unstructured vs semistructured data

Module 10: Intro to scikitLearn

Module 1: Regression Vs Classification
Module 2: Linear regression
Module 3: Multivariate regression
Module 4: Polynomial regression
Module 5: Multi-Colinearity,
Module 6: Auto correlation
Module 7: Heteroscedascity

Module 1: KNN
Module 2: Svm
Module 3: Decision Tree
Module 4: Random Forest
Module 5: Performance tuning of Random Forest
Module 6: Naive Bayse
Module 7: Overfitting Vs Underfitting

Module 1: Kmeans
Module 2: How to choose the number of K in KMeans
Module 3: Hands-on
Module 4: PCA

Module 1: Basic of Neural Network
Module 2: Type of NN
Module 3: Cost Function
Module 4: Tensorflow Basics
Module 5: Hands on Simple NN with Tensorflow
Module 6:Image classification using CNN


Job Placements Assistance


Case Study and Live Projects


Hours of course content


Data Science tools and techniques


Hiring Partners with leading companies


Interview Questions Preparation

The EduHac Advantage


Learning Support

Industry Mentors

  • Receive unparalleled guidance from Industry Mentors, Teaching Assistants and Graders
  • Receive one-on-one feedback on submissions and personalised tips for improvement

Student Success Mentors

  • Dedicated Student Success Managers are allocated to each student so as to ensure consistent progress.
  • Success Managers are your single points of contact for all your academic as well as non-academic queries.


Doubt Resolution

Q&A Forum

  • Timely Doubt Resolution by industry experts and peers
  • 100% expert verified responses to ensure quality learning

Expert Feedback

  • Personalised Feedback on Assignments and Case Studies
  • Live Sessions before Deadlines to Resolve All Queries




  • Fun-packed, informative offline learning with career guidance workshops
  • Group activities with your peers and alumni
  • Sessions by industry experts and professors

Industry Networking

  • Live sessions by industry experts or professors
  • Group discussions
  • One-on-one feedback and mentoring by industry experts


Anjali Kuragayala

Anjali K

CEO Product Business School

Vinay Full Stack

Vinay Kumar

Sr Software lead

Sandhya FUll stack


Sr Software Lead IBM

Tools and Platforms

Mongo DB
Mongo DB

Takeaways from this course


Official Certification of Participation from EduHac Power Learning


Endorsements on your LinkedIn account from EduHac

Life Time Access

Life time access to self-paced videos and class recordings

Student Reviews

Our Students Work at

Programme Fee - General

We have various options for every curious learner and tomorrow’s leaders.

INR 60,000 + GST

INR 100,000 + GST

Our Hiring Partners

Frequently Asked Questions

Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, please read our Refund Policy.

Contact us using the form on the right of any page on the EduHac website, select the Live Chat link or contact Help & Support.

All of our highly qualified trainers are industry experts with at least 15 years of experience in training and working in their respective domains. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

The prep course starts each Monday. Simply click “Enroll now” and select the cohort that best works for you.

The total estimated workload is 40-60 hours, expected to be completed in 4-6 weeks. You may complete the course faster if you have previous programming experience or if you are able to dedicate additional time to the material. You may also need more time to finish if you are brand new to programming or have a constrained schedule—but that’s no problem; you have access to the course as long as you need!

You may apply to the Digital Marketing at any time, even after enrolling in the prep course. As soon as you feel comfortable in Digital Marketing, we encourage you to fill out an application and take the admissions technical skills survey. However, students who complete the entire prep course and submit the final project will be fast-tracked through the Career Track application process.

You will be invited to join our broader weekly Digital Marketing office hours with all current Digital Marketing students. You will also have access to the community, where you can ask technical questions or seek assistance from fellow classmates or the course community manager, an expert Digital Marketer. Furthermore, you will have the support of a dedicated student advisor, there to assist you throughout the course. Finally, your classmates! You aren’t going through this alone; you’ll have the support of others starting out on their own unique Digital Marketing journeys.

Yes, once you enroll you will have lifetime access to the curriculum and exercises. You will not, however, have lifetime access to the mentorship. You will receive six mentor calls, one per week, for the expected 4-6 week duration of the course.

You will have 6 weekly mentor calls once the course starts and you will be paired with your mentor before the official start date. If you need additional time to complete, you are able to continue through the curriculum without a mentor beyond 6 weeks for as long as needed as you will have lifetime access.

Due to the short duration of the course and fast paced nature of the curriculum we do not offer refunds for our prep course. If you have a unique situation we encourage you to reach out to your student adviser to discuss potential options.

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