fbpx

Learn Data Science Course

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

800+

Students Empowered

Online

Formate

3 Months/6 Months

Recommended 10-12 hrs/week

May  30, 2020

Start Date

130+

Hiring Partners

Companies Hiring

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.

Mentor-Support-in-Eduhac

Unlimited 1:1 mentor support

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

internshipfinal-01

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.

Hands-On-Experience-Eduhac

Rigorous Hands-On Experience

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

pl1-01-01

Guaranteed Job Placement

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

Key Highlights

  • Full-time Placement Assurance* on program completion
  • Work on 60+ Data Science tools and platforms
  • End-to-End Interview Preparation
  • Mock Interviews by Hiring Managers
  • One-on-One Career Mentorship
  • Internship Opportunity with leading companies
  • Personalised Resume and LinkedIn profile review
  • 150+ Learner Hours and 15+ Case studies
Why-Workshop@Eduhac

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?

Entrepreneurs

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.

Curriculum

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

Get detailed course syllabus in your inbox

NumPy

Module 1: Create Arrays

Module 2: Array Item Selection and Indexing

Module 3: Array Mathematics

Module 4: Array Operation

Pandas

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

Get detailed course syllabus in your inbox

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

Get detailed course syllabus in your inbox

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

Get detailed course syllabus in your inbox

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

Get detailed course syllabus in your inbox

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

Get detailed course syllabus in your inbox

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

Get detailed course syllabus in your inbox

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

Get detailed course syllabus in your inbox

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

Get detailed course syllabus in your inbox

100+

Job Placements Assistance 

15+

Case Study and Live Projects

150+

Hours of course content

60+

 Data Science tools and techniques

100+ 

Hiring Partners with leading companies

500+

Interview Questions Preparation

Internship

Python

  • Python is Interpreted − Python is processed at runtime by the interpreter. You do not need to compile your program before executing it. This is similar to PERL and PHP.
  • Python is Interactive − You can actually sit at a Python prompt and interact with the interpreter directly to write your programs.
  • Python is Object-Oriented − Python supports Object-Oriented style or technique of programming that encapsulates code within objects.

Hadoop or Bigdata

  • Black Box Data − It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.
  • Social Media Data − Social media such as Facebook and Twitter hold information and the views posted by millions of people.
  • Stock Exchange Data − The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies.
  • Power Grid Data − The power grid data holds information consumed by a particular node with respect to a base station.

Spark

  • Speed − Spark helps to run an application in Hadoop cluster, up to 100 times faster in memory, and 10 times faster when running on disk. This is possible by reducing number of read/write operations to disk. It stores the intermediate processing data in memory.
  • Supports multiple languages − Spark provides built-in APIs in Java, Scala, or Python. Therefore, you can write applications in different languages. Spark comes up with 80 high-level operators for interactive querying.
  • Advanced Analytics − Spark not only supports ‘Map’ and ‘reduce’. It also supports SQL queries, Streaming data, Machine learning (ML), and Graph algorithms.

Machine Learning

  • Machine learning is a field of computer science that aims to teach computers how to learn and act without being explicitly programmed. More specifically, machine learning is an approach to data analysis that involves building and adapting models, which allow programs to “learn” through experience. Machine learning involves the construction of algorithms that adapt their models to improve their ability to make predictions.
  • According to Tom Mitchell, professor of Computer Science and Machine Learning at Carnegie Mellon, a computer program is said to learn from experience E with respect to some task.

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

Learning Support

Students Success Mentors
  • A dedicated Success Mentors is allocated to each student so as to ensure consistent progress
  • Success Mentors are your single points of contact for all your non-academic queries

Doubt Resolution

Q&A Forum
  • Timely doubt resolution by industry experts and peers
  • 100% expert verified responses to ensure quality learning

Doubt Resolution

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

Networking

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

Networking

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

Top Skills You Will learn

R/Python. You need to be really good in at least one. Also, learn to reuse your code. SQL. 99% of firms still use relational databases, and interviews still include coding tests on SQL, so learn it! Excel. Technically, this is NOT a programming language, but all senior executives use Excel, so it behooves folks to learn Excel and VBA  etc.,

Job Opportunities

Data science is a new field and you must remember that it is a multi-disciplinary field. It involves knowledge of math, statistics, ML, AI and skills of programming and effective communication. Meet the best-in-class industry professions who’s daily routine is Data Science.

Market Stats

  • India has seen more than 400% rise in demand for data science professionals across varied industry sectors at a time when the supply of such talent witnesses a slow growth.
  • 10 leading organizations with the most number of analytics opening this year are – Amazon, Citi, HCL, Goldman Sachs, IBM, JPMorgan Chase, Accenture, KPMG, E&Y & Capgemini.

Minimum Eligibility

Bachelor’s degree in science, Engineering, Business Administration, Commerce Mathematics or Masters in Mathematics, Statistics

Tools and Platforms

Takeaways from this course

Certificate

Participation-Certificate@Eduhac

Official Certification of Participation from EduHac Power Learning

Endorsement

LinkedIn@Eduhac

Endorsements on your LinkedIn account from EduHac

Life Time Access

Life time access to self-paced videos and class recordings

What Our Learners Say

Our Students Work at

Program Fee

  • Full-time Placement Assurance* on program completion
  • Work on 60+ Data Science tools and platforms
  • Personalised Resume and LinkedIn profile review
  • 150+ Learner Hours and 15+ Case studies
  • Mock Interviews by Hiring Managers
  • End-to-End Interview Peparation
  • One-on-One Career Mentorship
  • Internship Opportunity with leading companies

₹60,000

  • Full-time Placement Assurance* on program completion
  • Work on 60+ Data Science tools and platforms
  • Personalised Resume and LinkedIn profile review
  • Internship Opportunity with leading companies
  • 150+ Learner Hours and 15+ Case studies
  • Mock Interviews by Hiring Managers
  • End-to-End Interview Peparation
  • One-on-One Career Mentorship

₹100,000

Our Hiring Partners

Frequently Asked Questions

If I need to cancel my refund, will I get a refund?

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.

How can I learn more about this course?

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

Who are the trainers and how they are selected?

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.

When can I start the prep course?

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

How long is the course?

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!

Are there any prerequisites for this course? Do I need to know Python?

No prior coding experience is required, but to be successful, we recommend that students are already proficient in high-school level mathematics with an openness to learning more advanced concepts where necessary.

What role does my mentor play?

You may apply to the Data Science Career Track at any time, even after enrolling in the prep course. As soon as you feel comfortable in Python and statistics, 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.

What additional support will I have?

You will be invited to join our broader weekly data science office hours with all current data science 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 data scientist. 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 data science journeys.

Will I have lifetime access to the material?

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.

What happens if I need more than six weeks to finish the material?

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.

Can I receive a refund if I cancel without completing the course?

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.

What are you waiting for







    I would like to transform my career

    Features/Benefits

    • 100% Placement Assurance
    • Guaranteed Internship
    • Exposure to 60+ tools and platforms
    • Get certified from Facebook, Google, Hubspot and more
    • Hiring opportunity with 250+ partners
    • No Cost EMI
    • End-to-End Interview Peparation
    • Personalised Resume and LinkedIn profile review

    Found it the right choice for your career growth?

    Wait no more. Enroll talk to us today and enroll before admissions close.