• 300

    Students Empowered

  • Online


  • 9 Months

    Recommended 10-12 hrs/week

  • Feb ,5 2022

    Start Date

  • 130+

    Hiring Partners

Structured To Fit Into Your Life, Guaranteed To Get You A Job Opening doors to the best industry hiring leaders.

Unlimited 1:1 Mentor Support​

Meet weekly with your personal advisor, for the many additional calls you need.

100 Hrs Of Assured Internship​

Leading industry professionals give you the opportunity to work specifically in your field to gain experience and refine your skills.

Rigorous Hands-On Experience​

Learn about building a portfolio, which includes a key stone project and industry design project.

Guaranteed Job Placement​

Use our dedicated support team working with more than 200 organizations.

Key Points

  • Full-time Placement Assurance on program completion
  • Work with Advanced 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


The Data Science Program of the Product school provides you with an in-depth understanding and advanced knowledge of the eight most important areas of data science and includes real-world projects and visual metrics for domain information. You will be exposed to advanced Data Science tools with in-depth project information and Mimic Pro simulation to get you ready for the job.

Python Basics

Welcome To The


‣ Introduction To DataScience
‣ Real Time UseCases Of DataScience
‣ Who is a data scientist??
‣ Github Tutorial
‣ Skillsets needed for data scientist
‣ 6 Steps to take in 3 Months for a
complete transformation to DataScience
from any other domain
‣ Machine Learning-Giving Computers
The ability to learn from data
‣ Supervised vs Unsupervised
‣ DeepLearning vs Machine Learning
‣ Link to get Free Data to Practice?
‣ Some Great self Learning DataScience
Resources(Books, Tutorials, Videos, Papers, etc.)



Python Fundamentals


‣ Software Installation
‣ Introduction To Python
‣ “Hello Python Program” in IDLE
‣ Jupyter Notebook Tutorial
‣ Spyder Tutorial
‣ Introduction to Python
‣ Variable,Operators,DataTypes
‣ If Else, For and While Loops
‣ Functions
‣ Lambda Expression
‣ Filter, Map, Reduce
‣ Taking input from the keyboard

Python Advance


‣ Create Arrays
‣ Array Item Selection and Indexing
‣ Array Mathematics
‣ Array Operation



‣ Introduction to Pandas
‣ Series
‣ Series indexing and Selection
‣ Series Operation
‣ Introduction to Pandas
‣ Data Frames
‣ Data Collection from csv,json,html,excel
‣ Data Merging,Concatenation,join
‣ Group By and Aggregate Function
‣ Order By
‣ Missing Value Treatment
‣ Outlier Detection and Removal
‣ Pandas builtin Data Visualisation


‣ Line Plots
‣ Scatter Plots
‣ Pair Plots
‣ Histograms
‣ Heat Maps
‣ Bar Plots
‣ Count Plots
‣ Factor Plots
‣ Box Plots
‣ Violin Plots
‣ Swarm Plots
‣ Strip Plots
‣ Pandas Builtin Visualisation Library


‣ Descriptive vs Inferential Statistics
‣ Mean,Median,Mode,Variance,Std. dev
‣ Central Limit Theorem
‣ Co-Variance
‣ Pearson’s Product Moment Correlation
‣ R – Square
‣ Adjusted R-Square
‣ Spearman’s. Rank order Coefficient
‣ Sample vs Population
‣ Standardizing Data(Z-score)
‣ Hypothesis Testing
‣ Normal Distribution
‣ Bias Variance Tradeoff
‣ Skewness
‣ P Value
‣ Z-test vs T-test
‣ The F distribution
‣ The chi-Square test of Independence
‣ Type-1 and Type-2 errors
‣ Annova

Intro to ML

‣ Introduction to Machine Leaning
‣ Machine Learning Usecases
‣ Supervised vs Unsupervised vs SemiSupervised
‣ Machine Learning process Workflow
‣ Training a model
‣ Validating results
‣ Overfitting vs Underfitting
‣ Ordinal vs Nominal data
‣ Structured vs unstructured vs semi-structured data
‣ Intro to sci-kit learn




‣ Regression Vs Classification
‣ Linear regression
‣ Multivariate regression
‣ Polynomial regression
‣ Multi-Colinearity,
‣ Autocorrelation
‣ Heteroscedasticity
‣ Hands-On



‣ Svm
‣ Decision Tree
‣ Random Forest
‣ Performance tuning of Random Forest
‣ Naive Bayse
‣ Overfitting Vs Underfitting
‣ Hands-On


Model Validation


‣ Classification Report
‣ Confusion Report
‣ Cross validation
‣ Hands On


Clustering & PCA

‣ Kmeans
‣ How to choose the number of K in KMeans
‣ Hands-on

‣ Hands-on


Ensemble Methods


‣ What is Ensembling
‣ Types of Ensembling
‣ Bagging
‣ Boosting
‣ Stacking
‣ Random Forest
‣ Important Feature Extraction
‣ XGBoost


‣ Tokenizer
‣ Stop Word Removal
‣ Tf-idf
‣ Document similarity
‣ Word2vec Model
‣ t-SNE visualisation
‣ Sentiment Analysis

Deep Learning

‣ Basic of Neural Network
‣ Type of NN
‣ Cost Function
‣ Tensorflow Basics
‣ Hands-on Simple NN with Tensorflow
‣ Image classification using CNN

Case Studies


● How Airbnb characterizes data science

● How data science is involved in decision-making at Airbnb

● How Airbnb has scaled its data science efforts across all aspects of the company


● Processed the data to extract audio features for each artist

● Visualized the data using D3.js.

● Applied k-means clustering to separate the artists into different groups

● Analyzed each feature for all the artists


One of the best ways to explain the benefits of data science to people who don’t quite grasp the industry is by using Netflix-focused examples. Yes, Netflix is the largest internet-television network in the world. But what most people don’t realize is that, at its core, Netflix is a customer-focused, data-driven business. Founded in 1997 as a mail-order DVD company, it now boasts more than 53 million members in approximately 50 countries.


LinkedIn stores a large amount of user data including several details like their contact information, previous history, interests, activities on different social networking sites, etc. in its data warehouse for being aware of the trends and patterns.
Using the insights gained from the user data, LinkedIn connects individual users with their friends and people related to their areas of interest. It also helps them to make some decisions regarding the business.

  • 100+

    Job Placements Assistance

  • 15+

    Case Study and Live Projects

  • 1000+

    Hiring Partners with leading companies

  • 150+

    Hours of course content

  • 60+

    Digital Marketing tools and platforms

Tools And Platforms

Our Mentor

DineshData science lead

IIT Alumni Data science lead - EXIUM

VYshnaviData Analyst

Data Analyst and Data science faculty

Rajiv MukherjeeMentor and Startup Innovation

IIM Alumni 15 years Of Work Experience with Product companies

Alokk NathSpeaker & Mentor

Startup Founder, Product Management & Marketing

Takeaways From This Course


Official Certification of Participation from Product Bschool Learning


Endorsements on your LinkedIn account from PBS

Life Time Access

Life time access to self-paced videos and class recordings

Inpiring everyone to learn with

230+ Stories Of GrowthFind Out How Product B Schools Transformed Their Careers After Learning With

Neha Sharma Data base developer

Data science is an esoteric subject but this course made it so simple for me . The course material and quizzes were at its best . Product business school faculties exhibit good communication skills and made session more interactive.

Kiran KRData analyst

The course I selected was data science with python and Machine learning . Beginning it feels difficult but days passed by we can feel the depth of the subject . Anyone who is unsure of how to step in and explore the world of data science should join this course with product business school

Vinusha gopala KrishnanData scientist

My experience of learning data science with product business school was great and was able to achieve my goal of getting a job as well . I would seriously recommend for the people who are looking for a career uplift and even for freshers

Our Students Work At

Program Fee - General INR 50,000/-

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

View Plans
  • Study the course at Rs 50,000/- which can be paid at once or availed at super easy EMI’s.
  • Flexible Monthly EMI Payments starting from 5,555 INR/month*(Contact the admissions office for more details)
  • Placement fees of 50,000/- will be charged after getting placed in a company with a minimum package of 3L*

Application Process


Step 1: Fill The Application Form

Apply by filling a simple “online application form”


Step 2: Interview Process

Go through a screening call with the Admission Director’s office.


Step 3: Join Program

An admission letter will be rolled out to the selected few candidates. Secure your seat by paying the admission fee.

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    Frequently Asked Questions

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    Module 6 : Youtube Marketing

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    Module 1 : Introduction
    Module 2 : Creation of search network Campaign
    Module 3 : Google Display Network
    Module 4 : Mobile Ad Campaign
    Module 5 : Shopping Campaign
    Module 6 : Youtube Marketing

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    Module 1 : Introduction
    Module 2 : Creation of search network Campaign
    Module 3 : Google Display Network
    Module 4 : Mobile Ad Campaign
    Module 5 : Shopping Campaign
    Module 6 : Youtube Marketing

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