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Register in October and get 3 months of membership, absolutely free!
Henry Harvin® No.1 Data Science Course by India Today
Expedite your Career with Prestigious Diploma in Data Science | Learn Data Science with the #1 course and Get Ahead in Your Career | Learn Extensive use of Tools & Technologies and Analytics Techniques
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There's a reason that 95% of our alumni undertake 3+ courses as a minimum with Henry Harvin®
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Average salary hike
Access the best jobs in industry
Maximum salary hike
Two-way Live Training Course
Two-way Live Online Interactive Classroom Sessions
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Certification
Distinguish your profile with credentials and showcase expertise with our Hallmark Completion certificate
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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
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Student Engagement & Events
Free Access to #AskHenry Hackathons and Competitions & many other facilities from Henry Harvin®
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Masterclass
Access to 52+ Masterclass Sessions for essential soft skill development
Membership
Get Free Gold Membership of Henry Harvin®
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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.
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Entrepreneurship Mentorship
Mentorship from Young Successful Entrepreneurs to set up a sustainable & scalable Business from scratch at both Freelance and entrepreneur levels
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According to a recent evaluation, more than 93% of the firms use Artificial Intelligence for enhanced products and services
“Big Data is at the Foundation of all Mega Trends that are happening” - Chris Lynch
It is a versatile 10-in-1 program that includes various aspects of competency development and career development.
Become a part of the Elite Data Science & Analytics Academy of Henry Harvin® and join the 4,60,000+ large Alumni Network Worldwide.
Know the complete offerings of our Diploma in Data Science in Nagpur
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Live Course 1: Programming for Non Programmers
Programming is an increasingly important skill. This course will establish your proficiency in handling basic programming concepts. This program will help you to gain basic programming concepts like data types, variables, strings, loops, functions, and software engineering concepts like multithreading and multitasking.
Duration: 8 hours
Key Learning Objectives
- Achieve fundamental programming knowledge
Understanding the basics of data structures, data types, variables, C++, and JAVA
Course Curriculum
- Course Introduction
- Java Foundation
- C++ Foundation
- Jira
Live Course 2: Statistics for Data Science
Statistics is the discipline of allocating a prospect through the classification, collection, and analysis of data. A substructure part of Data Science, this Course helps you in defining the statistical terms. The Course explains measures of central tendency and dispersion and comprehended skewness, correlation, regression, and distribution. It will enable you to make data-driven predictions through statistics and the essential applications of it.
Duration: 8 hours
Key Learning Objectives
- Learn the fundamentals of statistics
- Collaborate with different types of data
- How to organize different types of data
- Compute the measures of central tendency, asymmetry, and variability
- Evaluate correlation and covariance
- Distinguish different types of distribution and work on it
- Estimate confidence intervals
- Perform and Evaluate hypothesis testing
- Make data-driven decisions
- Know comprehensively the mechanics of regression analysis
- Carry out regression analysis
- Use and understand dummy variables
Course curriculum
Lesson 1 - Introduction
Lesson 2 - Sample or Population Data?
Lesson 3 - The Fundamentals of Descriptive Statistics
Lesson 4 - Measures of Central Tendency, Asymmetry, and Variability
Lesson 5 - Practical Example: Descriptive Statistics
Lesson 6 - Distributions
Lesson 7 - Estimators and Estimates
Lesson 8 - Confidence Intervals: Advanced Topics
Lesson 9 - Practical Example: Inferential Statistics
Lesson 10 - Hypothesis Testing: Introduction
Lesson 11 - Hypothesis Testing: Let’s Start Testing!
Lesson 12 - Practical Example: Hypothesis Testing
Lesson 13 - The Fundamentals of Regression Analysis
Lesson 14 - Subtleties of Regression Analysis
Lesson 15 - Assumptions for Linear Regression Analysis
Lesson 16 - Dealing with Categorical Data
Lesson 17 - Practical Example: Regression Analysis
Live Course 3: Data Science with R
The next step to becoming a data scientist is learning R—the most in-demand open source technology. R is the most powerful Data Science and analytics language, which has a steep learning curve and vigorous community. Data Science with R is becoming the technology of choice for organizations that are adopting the power of analytics for competitive expedience.
Duration: 32 Hours
Key Learning Objectives
- Gain a substantial understanding of business analytics
- Install R, R-studio, and workspace setup, and learn about the various R packages
- Master R programming and understand how various statements are executed in R
- Gain an in-depth understanding of data structure used in R and learn to import/export data in R
- Define, understand and use the various apply functions and dplyr functions
- Understand and use the various graphics in R for data visualization
- Gain a basic understanding of various statistical concepts
- Understand and use the hypothesis testing method to drive business decisions
- Understand and use linear, non-linear regression models, and classification techniques for data analysis
- Learn and use the various association rules and Apriori algorithm Learn and use clustering methods including K-means, DBSCAN, and hierarchical
clustering
Course curriculum
Lesson 1 - R Basics
Lesson 2 - Data Structures in R
Lesson 3 - R Programming Fundamentals
Lesson 4 - Working with Data in R
Lesson 5 - Handling Data in R
Lesson 6 - Introduction to Business Analytics
Lesson 7 - Introduction to R Programming
Lesson 8- Data Structures
Lesson 9 - Data Management in R
Lesson 10 - Advanced Data Visualization
Lesson 11 - Descriptive Statistics in R
Lesson 12 - Regression Analysis
Lesson 13 - Decision Tree: Classification
Lesson 14 - Clustering: K-means and Hierarchical
Lesson 15 - Association Rule Analysis
Course 4: Data Science with Python
This Data Science with Python course will set up your mastery of Data Science and analytics techniques using Python. In this Python for Data Science course, you will learn the essential concepts of Python programming and gain in-depth knowledge in data analytics, Machine Learning, data visualization, web scraping, and natural language processing
Duration: 40 hours
Key Learning Objectives
- Learn an in-depth understanding of Data Science processes, data wrangling, data exploration, data
visualization, hypothesis building, and testing Install the required
- Understand Python environment and other auxiliary tools and libraries
- Understand the essential concepts of Python programming such as data types, tuples, lists, dicts,
basic operators, and functions
- Perform high-level mathematical computing using the NumPy package and its vast library of
mathematical functions
- Carry out scientific and technical computing using the SciPy package and its sub-packages such
as Integrate, Optimize, Statistics, IO, and Weave
- Carry out data analysis and manipulation using data structures and tools provided in the Pandas
package
- Gain an in-depth understanding of supervised learning and unsupervised learning models such as
linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
- Use the Matplotlib library of Python for data visualization
- Extract useful data from websites by performing web scraping using Python
Course curriculum
Lesson 1 - Python Basics
Lesson 2 - Python Data Structures
Lesson 3 - Python Programming Fundamentals
Lesson 4 - Working with Data in Python
Lesson 5 - Working with NumPy Arrays
Lesson 6 - Data Science Overview
Lesson 7 - Data Analytics Overview
Lesson 8 - Statistical Analysis and Business Applications
Lesson 9 - Python Environment Setup and Essentials
Lesson 10 - Mathematical Computing with Python (NumPy)
Lesson 11 - Scientific Computing with Python (Scipy)
Lesson 12- Data Manipulation with Pandas
Lesson 13 - Data Visualization in Python using Matplotlib
Course 5: Natural Language Processing
This Natural Language Processing course will give you a comprehensive detail of the science behind applying Machine Learning algorithms to process large amounts of natural language data. Learn the concepts of statistical machine translation and neural models, deep semantic similarity model (DSSM), neural knowledge base embedding, deep reinforcement learning technique, neural models applied in image captioning, and visual question answering using Python’s Natural Language Toolkit (NLTK).
Duration: 16 hours
Key Learning Objectives
- Apply Deep Learning models to solve machine translation and conversation problems
- Implement deep structured semantic models (DSSM) to retrieve information
- Understand deep reinforcement learning techniques applied in Natural Language Processing
- Use neural models applied in image captioning and visual question answering
Course curriculum
Lesson 1 - Introduction to Natural Language Processing
Lesson 2 - Feature Engineering on Text Data
Lesson 3 - Natural Language Understanding Techniques
Lesson 4 - Natural Language Generation
Lesson 5 - Natural Language Processing Libraries
Lesson 6 - Natural Language Processing with Machine Learning and Deep Learning
Lesson 7 - Speech Recognition Technique
Course 6: Tableau
This Tableau training will help you master the various aspects of the program and gain skills such as building visualization, organizing data, and designing dashboards. You will also learn concepts of statistics, mapping, and data connection. Tableau is an essential asset to those wishing to succeed in Data Science.
Duration: 32 hours
Key Learning Objectives
- Learn the concepts of Tableau, become proficient with statistics, and build interactive dashboards
- Master data sources and datable blending, create data extracts, and organize and format data
- Master arithmetic, logic, table and LOD calculations, and ad-hoc analytics
- Become an expert on visualization techniques such as heat map, treemap, waterfall, Pareto, Gantt chart, and market basket analysis
- Learn to analyze data using Tableau Desktop as well as clustering and forecasting techniques
- Gain command of mapping concepts such as custom geocoding and radial selections
- Master Special Field Types and Tableau Generated Fields and the process of creating and using parameters
- Learn how to build interactive dashboards, and story interfaces and how to share your work
Course Curriculum
Lesson 1 - Getting Started with Data Visualization and Tableau
Lesson 2 - Working with Tableau
Lesson 3 - Working on Metadata and Data Blending
Lesson 4 - Deep Diving with Data and Connections
Lesson 5 - Creating Charts
Lesson 6 - Adding Calculations to your Workbook
Lesson 7 - Mapping Data in Tableau
Lesson 8 - Dashboards and Stories
Lesson 9 - Visualizations for an Audience
Lesson 10- Integration of Tableau with R and Hadoop
Course 7: Simulated Data Science Projects
Electives 1: Artificial Intelligence
Module 1: Neural Network
This module will equip the candidate with the knowledge of Nural Networks. Gain Comprehensive knowledge about the Activation Functions and feedforward neural network. Learn about backpropagation and gradient descent. Know about the full connected layer forward and backward pass. Get acquainted with Data Preprocessing, Data Augmentation, weight initialization, working with google collab, and more
Module 2: Computer Vision
This module will help the candidate to gain knowledge of Computer Vision. Learn to work with images. Gain knowledge about Convolutions 2D for images. Know about the CNN architectures. Get acquainted with the knowledge of Semantic segmentation using UNet, and more
Module 3: Natural Language Programming (NLP)
This module will equip the candidate with the knowledge of Natural Language Processing. Learn the Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop words, BOW, TF-IDF. Know about Word embedding, POS Tagging, LSTM application, Encoder-Decoder attention, and more
Electives 2: Machine Learning
The Machine Learning course will make you a Master in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will learn concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Machine Learning knowledge.
Key Learning Objectives
- Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling
- Acquire practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach that includes working on four
major end-to-end projects and 25+ hands-on exercises
- Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning
- Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-n
means clustering, and more in Python
- Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which
include Boosting & Bagging techniques
- Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning
- Gain expertise in Machine Learning using the Scikit-Learn package
- Use the Scikit-Learn package for natural language processing
Electives 3: Deep Learning with KERA & Tensorflow
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.
Know the complete offerings of our Diploma in Data Science
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Descriptive Statistics
Model building and Fine Tuning
Explanatory Data Analysis
Supervised and Unsupervised Learning
Inferential Statistics
Machine Learning
Deep Learning
Neural Networking
Hypothesis Testing
Know more about 50+ tools covered in this Diploma in Data Science in Nagpur
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Know more about the value of this certificate in the industry
We are dedicated to supporting our students throughout their career journey. Join us, and let's embark on a journey towards a successful and fulfilling career together.
Exclusive access to our dedicated job portal and apply for jobs. More than 2100+ hiring partners’ including top start–ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.
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Data Analyst
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Data Mining Engineer
AI Research Scientist
Data Science Consultant
Data Product Manager
Data Visualization Specialist
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