MBA in Data Science by College De Paris

Key Highlights

192 Hours of Instructor-Led Sessions

64 Hours of Live Interactive Doubt Solving Sessions

32 Hours of Live Master Sessions by Industry Experts

288 Hours Self Paced Learning

288 Hours Hands on with Cloud Labs

Job-Ready Portfolio of 19 Capstone Projects

53 Auto-Graded Assessments

38+ Industry Case Studies

288 Guided Hands-On Exercises

19+ Assignments and Mini Projects

2 Mock Interviews and 2 Hackathons

Get 1-Year Gold Membership of Henry Harvin® Data Science & Analytics Academy

Get a guaranteed Internship with Henry Harvin® and in top MNCs like J.P. Morgan, Accenture & many more via Forage

Get 3 in 1 Placement support through Placement Drives, Premium access to Job portal & Personalized Job Consulting

13 Hours of Mentorship by Industry Experts

Earn Certification of Course from Henry Harvin®, Govt of India recognized & Award-Winning Institute and NSDC Certification

You Get 10-In-1 Program

Two-way Live Training Course

Two-way Live Online Interactive Classroom Sessions

Certification

Distinguish your profile with global credentials and showcase expertise with our Hallmark Completion certificate

Projects

Facility to undergo various projects along with the course.

Guaranteed Internship

Get a guaranteed Internship with Henry Harvin® and in top MNCs like J.P. Morgan, Accenture & many more via Forage.

Placement

Get 3 in 1 Placement support through Placement Drives, Premium access to Job portal & Personalized Job Consulting

Student Engagement & Events

Free Access to #AskHenry Hackathons and Competitions & many other facilities from Henry Harvin®

Masterclass

Access to 52+ Masterclass Sessions for essential soft skill development

Membership

Get Free Gold Membership of Henry Harvin®

E-Learning Access

Free access to the E-learning Portal and future updates. Get access to PPTs, Projects, Quizzes, self-paced Video-based learning, a question bank, a library, practice tests, final assessment, a forum, and doubt sessions.

Entrepreneurship Mentorship

Mentorship from Young Successful Entrepreneurs to set up a sustainable & scalable Business from scratch at both Freelance and entrepreneur levels

About the MBA in Data Science by College De Paris

MBA in Data Science by College de Paris

MBA in Data Science College de Paris is designed to equip students with the skills and knowledge required to analyze and manage large sets of complex data. This Data Science Master's Program provides hands-on experience in data visualisation, machine learning, and statistical modelling. Learn Real-world case studies to build practical skills. Experience Hands-on exposure to analytics tools & techniques such as Python, Tableau, SQL . The Data Science Master's Program focuses on topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science.

Who can Join the course?

Individuals with a background in mathematics, statistics, computer science or related fields are eligible to join this course.
Applicants must demonstrate proficiency in programming languages such as Python and R.

AGOTA™ Framework

It is a versatile 10-in-1 program that includes various aspects of competency development and career development.

Training: 192 Hours of live Two-way interactive online sessions
Projects: Facility to undergo projects related to Data Science
Masterclass: Access to 52+ Masterclass Sessions for essential soft skill development
Student Engagement & Events: Free Access to #AskHenry Hackathons and Competitions & many other facilities from Henry Harvin®
Membership: Get 18 Months of Gold Membership of Henry Harvin® Data Science & Analytics Academy for the MBA in Data Science by College de Paris
E-Learning: E-Learning: Free access to the E-learning Portal and future updates. Get access to PPTs, Projects, Quizzes, self-paced Video-based learning, a question bank, a library, practice tests, final assessment, a forum, and doubt sessions.
Certification: Get Course Completion Certification of MBA in Data Science by College de Paris in association with Henry Harvin® Govt of India recognized & Award-Winning Institute, and showcase expertise
Internship Support: Get a guaranteed Internship with Henry Harvin® and in top MNCs like J.P. Morgan, Accenture & many more via Forage.
Entrepreneurship Mentorship: Mentorship from Young Successful Entrepreneurs to set up a sustainable & scalable Business from scratch at both Freelance & Entrepreneur level
Placement Support: Get 3 in 1 Placement support through Placement Drives, Premium access to Job portal & Personalized Job Consulting

Trainers at Henry Harvin®

Most renowned industry experts with 14+ years of work experience and recognized by various organizations over the years for their work.
Have been invited to 350+ keynote classes for MBA in Data Science by College De Paris
Have delivered 510+ lectures and are currently enlisted as domain experts with Henry Harvin® Data Science & Analytics Academy

Alumni Status

Become a part of the Elite Data Science & Analytics Academy of Henry Harvin® and join the 4,60,000+ large Alumni Network Worldwide.

Gold Membership Benefits

Avail 18 Months Gold Membership of Henry Harvin® Data Science & Analytics Academy that includes E-Learning Access through recorded Videos
Free Masterclass Sessions access.
Earn the Prestigious Henry Harvin®️ Alumni Status and become one of the reputed 4,60,000+ alumni globally.
Guaranteed Internship with Henry Harvin®️ or partner firms
Weekly 10+ job opportunities offered.
Experience Industry Projects during the training

Learning benefits

Earn globally recognized Certification
Be well accomplished in analytics tools and technologies such as Python, Tableau, SQL
Develop advanced skills in data analysis and management.
Gain expertise in machine learning algorithms and statistical modeling techniques.
Acquire practical experience through project-based learning.
Know about the Machine learning techniques such as Regression, Predictive Clustering, Time Series Forecasting, Classification.
Create an analytics framework using statistics and data modeling to structure any Business problem
Learn to use data cleaning and data transformation operations using tools and Techniques
Learn Comprehensively about Deep Learning, Natural Language Processing (NLP)
Be Job-ready for the post of Data Scientist, Data Engineer, or Analyst for top Analytics companies

Career Benefits

Exposure to 4+ Millions of Jobs in the Arena of Data Science
Be Part of expanding Data Science and Big Data Market
Get Hired by International Brands like Google, Amazon and other top brands in the industry, as a Data Scientist
Add the Certification to your CV & LinkedIn Profile with professional & Technical development
Be Highly Paid as a Freelancer or as a full-time Professional after the Successful completion of MBA in Data Science

Know the complete offerings of our MBA in Data Science by College De Paris

Curriculum For MBA in Data Science by College De Paris

Module 1: SQL
In this module the candidate will learn about concepts of SQL, which includes understanding JOIN, String functions etc.
- SQL Overview
- SQL Manipulation
- JOIN; Inner, Left, Right, Full Outer, and Cross JOIN
- String Functions
- Mathematical Functions
- Date-Time Functions
- Hunting Tips
- Case Study
- Project
Module 2: Power BI
In this module the candidate will learn about Power BI Concepts such as Power Query, Data Modelling, Reporting etc.
- Business Intelligence (BI) Concepts
- Microsoft Power BI (MSPBI) Introduction
- Connecting Power BI with Different Data Sources
- Power Query for Data Transformation
- Data Modelling in Power BI
- Reports in Power BI
- Reports & Visualization Types in Power BI
- Dashboards in Power BI
- Data Refresh in Power BI
- End to End Data Modelling & Visualization
- Case Study
- Project
Module 3: Python Programming
In this module the candidate will learn about the major concepts of Python Programming
- Python Basics
- Python Programming Fundamentals
- Python Data Structures
- Working with Data in Python
- Working with NumPy Arrays
- Case Study
- Project
- Dataset
Module 4: R Programming
In this module the candidate will learn about the major concepts of R programming
- R Basics
- Working with Data in R
- R Programming Fundamentals
- Data Structures in R
- Handling Data in R
- Case Study
- Project
Module 5: CRISP ML(Q)
In this module the candidate will learn about the concept of Project Management using CRISP ML(Q)
- Project Management Methodology
- Case Study
Module 6: Data Types and Data Processing
In this module the candidate will learn about the Data types and its processing.
- Nominal, Ordinal,Interval, Ratio, Data Cleaning techniques
- Case Study
- Project
- Dataset
Module 7: Statistics
In this module the candidate will learn about the statistics and its types.
- Descriptive,Inferential, Hypothesis Testing
- Case Study
- Project
Module 8: EDA
In this module the candidate will learn about the EDA, Business moments and features about engineering.
- Business moments, Graphical representation, Feature Engineering
- Case Study
- Project
- Dataset
Module 9: Mathematical Foundation
In this module the candidate will learn about the Mathematical foundation which includes understanding optimization, linear algebra etc.
- Optimization, Derivatives, Linear Algebra, Matrix Operations
- Case Study
- Project
Module 10: Clustering
In this module the candidate will learn about the clustering, its types and function.
- Hierarchical Clustering, K Means Clustering
- Case Study
- Project
- Dataset
Module 11: Dimension reduction
In this module the candidate will learn about the concept of Dimension reduction.
- PCA,SVD
- Case Study
- Project
- Dataset
Module 12: Association Rules
In this module the candidate will learn about the Association rules, terminologies used, performance measures and many more.
- Market Basket Analysis, Association Rules Intuition, Association Rules Applications, Association Rules Terminology Association Rules Performance Measures
- Case Study
- Project
Module 13: Recommendation Engine
In this module the candidate will learn about the Recommendation engine and its functions.
- Intro to personalized strategy, similarity measures, user-based collaborative filtering, item-to-item collaborative filtering, recommendation engine vulnerabilities
- Case Study
- Project
- Dataset
Module 14: Text Mining and NL
In this module the candidate will learn about the Text Mining, importance, terminology, BOW, DTM, TDM Etc.
- Text Mining Importance, BOW, Terminology and Preprocessing, Textual Data cleaning, DTM and TDM, Corpus level, positive and negative word clouds, social media web scraping
- Case Study
- Project
- Dataset
Module 15: Naive Bayes
In this module the candidate will learn about the Probability concepts, Naive Bayes etc.
- Probability, Joint probability, conditional probability, Naive Bayes formula, Use case
- Case Study
- Project
- Dataset
Module 16: KNN
In this module the candidate will learn about the KNN.
- Nearest Neighbour Classifier, 1- Nearest Neighbour classifier, K- Nearest Neighbour Classifier, Controlling complexity in KNN, Euclidean Distance
- Case Study
- Project
- Dataset
Module 17: Decision Tree
In this module the candidate will learn about the Decision tree, how to build it, Attribute selection, etc.
- What is a Decision Tree, Building a Decision Tree, Greedy Algorithm, Building the best Decision Tree, Attribute selection- Information gain
- Case Study
- Project
- Dataset
Module 18: Ensemble Techniques
In this module the candidate will learn about the Ensemble Techniques, Ensemble Primer, voting, stacking, bagging etc.
- Project
- Ensemble Primer, Voting, Stacking, Bagging, and Random Forest, Boosting Models
- Case Study
- Dataset
Module 19: Confidence Interval
In this module the candidate will learn about the Confidence Interval. Normal Distribution, Central Limit Theorem etc.
- Project
- Intro to Normal Distribution, Probability Calculation for normally distributed data, Normal QQ plot, Central Limit Theorem, Confidence Interval
- Case Study
- Dataset
Module 20: Hypothesis Testing
In this module the candidate will learn about the Hypothesis testing and different types of tests.
- Hypothesis Testing, Flowchart- Y is continuous, 2 sample T-Test, One Way ANOVA, Flowchart- Y is discrete, 2 proportion Test, Chi-Square Test
- Case Study
- Project
- Dataset
Module 21: Regression Techniques
In this module the candidate will learn about the Regression, its types- Simple, multiple etc.
- Case Study
- Project
- Simple Linear, Multiple Linear, Logistic Regression, Multinomial Regression, Ordinal Regression, Advance Regression
- Dataset
Module 22: SVM
In this module the candidate will learn about the SVM, Best fit, kernel tricks and many more.
- SVM Hyperplanes, Best fit Hyperplane, Kernel Tricks, Multiclass Classification using SVM
- Case Study
- Project
- Dataset
Module 23: Survival Analytics
In this module the candidate will learn about Survival Analytics, its applications and function.
- Intro to Survival Analytics, Applications, Time to event, Censoring, Kaplan Meier Survival Function
- Case Study
- Project
Module 24: Forecasting
In this module the candidate will learn about Forecasting, times series, errors in forecasting, methods of forecasting etc.
- TimeSeries vs Cross-Sectional Data, Time Series Dataset, Forecasting Strategy, Time Series Components, Time Series Visualizations, Time Series Partition, Forecasting Methods, Forecasting Errors, Seasonal Index
- Case Study
- Project
Module 25: ANN
In this module the candidate will learn about ANN, Perceptron functions, Error surface, Activation function etc.
- Case Study
- Project
- Dataset
- Neural Network Primer, Perceptron and Multi-Layered Perceptron Algorithm, Activation Function, Error Surface, Gradient Descent Algorithm
Module 26: CNN
In this module the candidate will learn about CNN, Net challenges, MLP Filters and more.
- Image Net Challenge, Parameters Explosion and MLP, Convolutional Networks, Convolutional Layers and Filters, Pooling Layer, Practical Issues, Adversaries
- Case Study
- Project
Module 27: RNN
In this module the candidate will learn more about RNN, RNN Vs. Deep LSTM Etc.
- Traditional Language Models, Wny not MLP, Recurrent Neural Networks,RNN types, CNN+RNN, Bidirectional RNN, Deep Bidirectional RNN, RNN vs LSTM, Deep RNN vs Deep LSTM's
- Project
- Case Study
Module 28: Complimentary Module 1: Soft Skills Development
- Business communication
- Preparation for the Interview
- Presentation Skills
Module 29: Complimentary Module 2: Resume Writing
- Resume Writing