Nagpur, the third-biggest city and winter capital of Maharashtra, India, ranks 13th nationwide in population. It’s a Smart City Project frontrunner and one of Maharashtra’s proposed Smart Cities.
Known as the Orange City, Nagpur is a key trading hub with extensive orange farming. It’s also home to big food companies like Haldiram’s, Suruchi International, and Actchawa.
Central India’s educational powerhouse, Nagpur is rapidly evolving into a smart city. It hosts tech giants such as Tech Mahindra, TCS, Global Logic, and HCL. Infosys is even setting up a campus in the Mihan Special Economic Zone.
Great news for Nagpur locals: The city offers popular data science courses. If you’re aiming to become a Data Scientist, Business Intelligence Developer, Data Analyst, Engineer, or Machine Learning Specialist, these programs are worth considering.
Comparison Chart: Top 12 Data Science Courses in Nagpur with Placement.
| Institute Rank | Name | Training Delivery Type | Offering Course Since | Fees | Placement Assistance |
| 1 | Rise Institute | Online & Offline | 2018 | INR 30k-90k | 100%* |
| 2 | Data Science Era | Online & Offline | 2012 | INR 45,000 | 100% |
| 3 | LIVEWIRE | Offline | 2017 | INR 50,000 | 100% |
| 4 | Atlanta Computer Institute | Online & Offline | 2000 | INR 79,500 | 100% |
What's Data Science and Why Does it Matter?
Picture Data Science as a digital Sherlock Holmes – a champion for information in our online world. Its mission? To make sense of our vast data troves. Imagine a massive jigsaw puzzle – that’s our data. Data Science is the clever tool that pieces it together, revealing the big picture. It uses smart techniques and math wizardry to decode the secrets hidden in numbers, text, or images.
So, why is this such a big deal? Think about the data we generate daily – from tweets to online purchases. Data Science helps businesses and researchers unlock the potential of this information. It enables companies to understand what customers really want, making your shopping experience smoother.
It also aids doctors in spotting health trends, leading to improved treatments and medicines. In essence, Data Science is like a superhero that uncovers the mysteries of our digital universe. It enhances our lives by transforming data mountains into valuable insights. Next time you’re amazed at how an app seems to read your mind, thank Data Science for making it possible!
Top 4 Data Science Programs in Nagpur
1. Rise Institute.
Rise Institute stands out as India’s leading digital skills training provider. Our curriculum is crafted by a team of over 10 data science experts, incorporating insights from 250 innovative businesses across Asia.
We’ve tailored our program to match the skill requirements of both small and large digital marketing agencies, as well as in-house marketing teams. Our approach is practical, blending hands-on work, research, and assignments to ensure a comprehensive learning experience.
Why Pick Rise Institute's Online Data Science Course?
Join our Data Science Training in Nagpur Program, India’s finest, to kickstart your data science certification journey. You’ll get practical experience with over 75 projects covering areas like Statistics, Advanced Excel, SQL, Python Libraries, Tableau, Advanced Machine Learning, and Deep Learning. These projects mirror real-world challenges from various sectors including healthcare, manufacturing, sales, media, marketing, and education. The training equips you for more than 30 different job roles, opening up a wide range of exciting career possibilities.
Benefits of Online Data Science Courses
Enrolling in an online Data Science course is like embarking on an exciting learning adventure from your own space. The flexibility is a major plus – you decide when and where to learn. No rush to get to class; you can even study in your pajamas if you want! Plus, these courses often let you set your own pace.
Need extra time on a challenging concept? No problem! Another great aspect is accessibility. The internet connects you with top-notch instructors and resources worldwide. It’s like having a virtual library at your fingertips! Online courses also use interactive tools and videos, making learning more engaging than traditional textbooks. Cost-saving is another advantage.
You save on travel expenses and pricey textbooks. You can become a Data Science pro without breaking the bank. So, if you’re ready to dive into the world of data and numbers, an online Data Science course could be your perfect starting point!
Data Science Course Syllabus
Module 1: Introduction to Data Science | Introduction to the Industry & Buzzwords Industrial application of data science Introduction to different Data Science Techniques Important Software & Tools Career paths & growth in data science |
Module 2: Introduction to Excel | Introduction to Excel- Interface, Sorting & Filtering, Excel Reporting- Basic & Conditional Formatting Essential Excel Formulae Layouts, Printing and Securing Files |
Module 3: Introduction to Stats | Introduction to Statistics & It’s Applications Different types of Data Population vs Sample Sampling Techniques Intro: Inferential vs. descriptive statistics |
Module 4: Descriptive Stats Using Excel Datasets | Categorical Variables Visualization Using Excel Charts- FDT, Pie Charts, Bar Charts & Pareto Numerical Variables Visualization of Frequency & Absolute Frequency- Using Histogram, Cross Table & Scatter Plot Measure of Spread ( Mean, Mode , Median) Measure of Variance( Skewness, SD, Variance, Range, Coef. Of Variance, Bivariate Analysis, Covariance & Correlation) |
Module 5: Inferential Stats Using Excel Datasets | Introduction to Probability Permutation & Combinations Types of events Normal distribution Standard Normal distribution Normal vs. Standard Normal distribution Confidence Intervals & Z-Score Hypothesis Testing & It’s Types |
Module 6: Database Design & MySQL | Relational Database theory & Introduction to SQL MySQL Installation Database Creation in the MySQL Workbench Querying in MySQL Joins and Set Operations SQL Practice Case Study Window Functions Case Statements, Stored Routines and Cursors Ø Query Optimisation and Best Practices  Ø Problem-Solving Using SQL |
Module 7: Data Visualization Using Advanced Excel | Introduction LOOKUP functions Pivot Tables WHATIF Analysis Dashboard Creation Recording Macros Advanced Visualizations- PIVOT Charts, Sparklines, Waterfall Charts Data Analysis ToolPak – Regression in Excel |
Module 8: Data Visualization Using Tableau | Introduction to Tableau Introduction What is Data Analytics? Why Data Visualisation? What is Tableau? Why Tableau? Tableau vs Excel and PowerBI Exploratory and Explanatory Analysis Getting started with Tableau Visualizing and Analyzing data with Tableau – I Introduction Bar Charts Line Charts and Filters Area Charts Box plots and Pivoting Maps and Hierarchies Pie Charts Treemaps and Grouping Dashboards Visualizing and Analyzing Data with Tableau – II Introduction Joins and Splits Numeric and String functions Logical and Date functions Histograms and parameters Scatter Plots Dual Axis Charts Top N Parameters and Calculated Fields Stacked bar Charts Dashboards – II and Filter Actions Storytelling Summary |
Module 9: Python Programming | Installing Anaconda & Basics of Python Introduction to programming languages Compiler vs Interpreter Getting Started With Python Introduction to jupyter Notebooks Identifiers, Keywords Print function Comment, Indentation Data Types Functions Understanding what are functions Defining and calling functions Local and global variables Different types of arguments Map,reduce,filter,lambda and recursive functions Data Structures in Python Introduction Lists Tuples Sets Dictionaries Practice Exercise Summary Operator Input and Output Different Arithmetic , logical and Relational operators Input, Output function Eval function Format Function Control Flow If elif else statement For and while loops Break , continue and Pass statement List and dictionary comprehensions Functions Understanding what are functions Defining and calling functions Local and global variables Different types of arguments Map,reduce,filter,lambda and recursive functions  File Handling Purpose of file handling Different function in file handling (open,read, write,close) Different modes (r,w,a,r+,w+,a+) With block Exception Handling, OOPX & Regex What is exception handling Try, except, else and finally block Different types of Exception Concept of Oops Different functions in Regex Metacharacters in Regex |
Module 10: Python For Data Science | NumPy Introduction to NumPy Basics of NumPy Operations Over 1-D Arrays Practice Exercise I Multidimensional Arrays Creating NumPy Arrays Mathematical Operations on NumPy Mathematical Operations on NumPy II Computation Times in NumPy vs Python Lists Practice Exercise II Pandas  Introduction to Pandas Basics of Pandas Pandas – Rows and Columns Describing Data Indexing and Slicing Operations on Dataframes Groupby and Aggregate Functions Merging DataFrames Pivot Tables Practice Exercise |
Module 11: Data Visualization Using Python- Matplotlib & Seaborn | Introduction to Data Visualisation with Matplotlib Introduction to Matplotlib The Necessity of Data Visualisation Visualisations – Some Examples Facts and Dimensions Bar Graph Scatter Plot Line Graph and Histogram Subplots Choosing Plot Types Summary Data Visualisation: Case Study Introduction Case Study: Mind Map Case Study Overview Data Handling and Cleaning: I Data Handling and Cleaning: II Sanity Checks Outliers Analysis with Boxplots Histograms Summary Practice Questions Data Visualization with Seaborn Introduction Distribution Plots Styling Options Pie – Chart and Bar Chart Scatter Plots Pair Plots Revisiting Bar Graphs and Box Plots Heatmaps Line Charts Stacked Bar Charts Case Study Summary Plotly Practice Questions |
Module 12: Exploratory Data Analysis | Data Sourcing Module Introduction Introduction to EDA Public and Private Data Private Data Public Data Web Scraping-I Web Scraping-II Summary Data Cleaning Introduction Data Types Fixing the Rows and Columns Impute/Remove Missing Values Handling Outliers Standardising Values Fixing Invalid Values and Filter Data Practice Questions Summary Univariate Analysis Introduction to Univariate Analysis Categorical Unordered Univariate Analysis Categorical Ordered Univariate Analysis Statistics on Numerical Features Graded Questions Summary Bivariate and Multivariate Analysis Introduction Numeric – Numeric Analysis Correlation vs Causation Numerical – Categorical Analysis Categorical – Categorical Analysis Multivariate Analysis Graded Questions Summary Module Summary |
Module 13: Supervised Learning Model – Regression | Introduction to Simple Linear Regression Introduction to Simple Linear Regression Introduction to machine learning Regression line Best fit line Strength of simple linear regression Simple linear regression in python Assumptions of simple linear regression Reading and understanding the data Hypothesis testing in linear regression Building a linear model Residue analysis and predictions Linear Regression using SKLearn Multiple Linear Regression Motivation-when one variable is not enough Moving from SLR to MLR-new considerations Multi collinearity Dealing with categorical variables Model assessment in comparison Feature selection Multiple Linear Regression in Python Reading and understanding the data Data preparation Initial steps Building the model I & II Residue analysis and predictions Variable selection using RFE Industry Relevance of Linear Regression Linear regression revision Prediction versus projection Media company case study Exploratory data analysis Model building – I, II & III Assessing the model Interpreting the results |
Module 14: Supervised Learning Model – Classification | Univariate Logistic Regression Binary classification Sigmoid curve Finding the best fit sigmoid curve – I Finding the best fit sigmoid curve – II Odds and log Odds Multivariate Logistic Regression – Model Building Multivariate Logistic Regression – Model Building Data cleaning and preparation – I & II Building your first model Feature elimination using RFE Confusion metrics and accuracy Manual feature elimination Multivariate Logistic Regression – Model Evaluation Multivariate Logistic Regression – Model Evaluation Metrics beyond accuracy-sensitivity and specificity Sensitivity and specificity in Python Understanding ROC curve ROC curve in python Finding the optimal threshold Model evaluation metrics – exercise Precision and recall Making predictions Logistic Regression – Industry Applications – Part I Getting familiar with logistic regression Nuances of logistic regression-sample selection Nuances of logistic regression-segmentation Nuances of logistic impression-variable transformation-I, II & III Logistic Regression: Industry Applications – Part II Model evaluation – A second look Model validation and importance of stability Tracking of model performance over time Logistic Regression – Industry Applications – Part II Commonly face challenges in implementation of logistic regression Model evaluation – A second look Model validation and importance of stability Tracking of model performance over time |
Module 15: Advanced Machine Learning | Unsupervised Learning: Clustering Introduction to Clustering K Means Clustering Executing K Means in Python Hierarchical Clustering Business Problem Solving Introduction to Business Problem Solving Case Study Demonstrationchurn example Practice Questions Tree Models Introduction to Decision Trees Algorithms for Decision Tree Construction Hyperparameter Tuning in Decision Trees Ensembles and Random Forests Time Series Forecasting – I (BA) Introduction to Time Series Smoothing Techniques Time Series Forecasting – II (BA) Introduction to AR Models Building AR Models Model Selection Principles of Model Selection Model Building and Evaluation |
Module 16: AI- NLP, Neural Networks & Deep Learning | Introduction to NLP What is NLP? History and evolution of NLP Applications of NLP Challenges in NLP Overview of NLP pipeline Corpus and Corpus Linguistics NLTK Toolkit Introduction to the NLTK toolkit Preprocessing text data with NLTK Basic NLP tasks using NLTK (e.g., Part-ofSpeech Tagging, Named Entity Recognition) Stemming and Lemmatization WordNet in NLTK Chunking and Chinking Sentiment Analysis with NLTK Tokenization and Topic Modeling Tokenization in NLP Bag-of-Words representation Topic Modeling with LDA Latent Semantic Analysis Word Embeddings Sentiment Analysis Project: Introduction to Sentiment Analysis Sentiment Analysis using supervised and unsupervised methods Building a Sentiment Analysis model with Python Evaluating Sentiment Analysis models AI vs Deep Learning vs ML Introduction to Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) Applications of AI, ML, and DL Differences between AI, ML and DL The Concept of Neural Networks Introduction to Neural Networks Types of Neural Networks Layers in Neural Networks Activation Functions Neural Networks – Feed-forward, Convolutional, Recurrent Feed-forward Neural Networks Convolutional Neural Networks Recurrent Neural Networks Applications of Neural Networks Deep Learning Project Building a Deep Learning model with Python Image Classification with Convolutional Neural Networks Natural Language Processing with Recurrent Neural Networks |
Data Science Projects and Assignments
Major Projects
- Customer Lifetime Value Analysis: This project uses SQL to calculate customer lifetime value, helping understand the long-term revenue generated by each customer.
- Predicting Customer Churn: Here, you’ll build a predictive model using SQL to identify customers likely to leave based on their behavior and transaction history.
- E-Commerce Sales Dashboard: Create an interactive dashboard using Tableau & SQL to analyze retail sales data, spot trends, and make data-driven decisions.
- Customer Segmentation Visualization: Develop a customer segmentation dashboard with Tableau to identify distinct customer groups based on demographics, behavior, and buying patterns.
- Movie Suggestion System: This project uses Python and libraries like Pandas, NumPy, and Scikit-Learn to create a system that recommends movies based on user likes and ratings.
- Twitter Mood Analysis: Using Python with NLTK and TextBlob, this project analyzes Twitter data to understand the overall feeling about a specific topic.
- COVID-19 Data Visuals: This project uses Python with Matplotlib, Seaborn, and Plotly to show COVID-19 data, helping understand how the pandemic affected different places.
- Stock Market Visuals: Using Python with Pandas, Matplotlib, and Bokeh, this project creates visuals of stock market data to spot trends in stock prices over time.
- Airbnb Data Study: This project looks at Airbnb data to find patterns in pricing, availability, and quality of listings in various cities.
- Bike Sharing Analysis: This project studies bike sharing data to understand how people use bikes in different cities and what affects bike usage.
- House Price Guessing: Using Python and Scikit-Learn, this project builds a model to guess house prices based on things like location, size, and features.
- Loan Risk Prediction: This project uses Python and Scikit-Learn to create a model that predicts if someone might not pay back a loan based on their credit history.
- Sales Trend Prediction: This project uses advanced methods like ARIMA and LSTM to predict future sales trends and find what affects sales.
- Product Review Mood Check: This project uses NLP techniques like Word Embeddings and RNNs to check product reviews and understand how customers feel about different products.
- Deep Learning for Image Sorting: This project uses advanced deep learning methods like FCN and U-Net to sort images and find objects in them.
- Language Translation with AI: This project builds a model using advanced deep learning techniques like Transformers to change text from one language to another.
Case Studies & Homework:
- Healthcare Feedback Examination
- Management Dashboard Development
- Retail Sales Report Evaluation
- Software Company Staff Data Review
- Industrial Dataset Grouping & Contrast
- Visual Tools: Frequency Tables, Pie Charts, Pareto Diagrams, Histograms, Scatter Plots, Heat Maps, Bar Graphs and more.
- Patient Illness Likelihood Study Using Health Records
- Vehicle Model & Menu Item Combination Probability Assessment
- Production & Product Introduction Data Sorting & Review
- Customer Issue Resolution Study Using Bell Curves
- Item Rating & Worker Output Analysis Using Standard Scores
- New Product Demand Study Using Statistical Tests
- Stock Control & Customer Group Systems Using Data Lookups
- Sales Pattern & Staff Planning with Pivot Tables
- Price Strategy & Money Model Creation Using Scenario Tools
- Sales & Operations Dashboard Building
- Health & Building Report Automation Using Macros
- Shop Sales Chance Analysis Using PIVOT Charts
- Finance Firm Report Study Using Sparklines & Waterfall Charts
- Consumer Goods Ad Spend to Revenue Impact Study Using Regression
- Transport Pricing Model Using Statistical Analysis
Data science program costs
Data Science Master Program Fees: 30k-90k
Contact Details
Rise Institute Main Office: Office 36, Akshar Geometrix Silver Christ Kamothe, near Khandeshhwar, Sector-25, Khandeshhwar, Railway Station, Navi Mumbai, Maharashtra 410209
Email: contact@riseinstitute.tech
Phone: +91 93242 88446
Website: www.riseinstitute.tech
2. Data Science Era
Getting good training in new tech has been hard for many people in India.
Data Science Era wants to make it easier for everyone to get quality training in new tech fields. They teach things like Data Science, Analytics, Big Data, AI, Machine Learning, Cloud Computing, Development, DevOps, Digital Marketing, Management, Engineering, and more.
Course Topics:
Module 1 – Python Basics
- Your first code
- Data types
- Math and Variables
- Working with Text
Module 2 – Python Data Structures
- Lists and Tuples
- Sets
- Dictionaries
Module 3 – Python Coding Basics
- If-else statements
- Loops
- Functions
Module 4 – Using Data in Python
- Reading and Writing Files
- Data Handling with Pandas
- Pandas Data Operations and Storage
Course Features:
- 100+ hours of instruction
- Practice Assessment Included
- Completion Certificate
- Skill Level Indication
- 4 Industry-Based Projects
- Hands-On Learning
- One-on-One Mentoring from Industry Experts
| Name | Data Science with Python Course |
| Course Duration | 5 Months |
| Website | www.datatrained.com |
| Address: | 3rd floor, Zal Complex, Bazar, beside Indian Oil Petrol Pump, Sadar, Nagpur, Maharashtra 440001 Mob: 099757 36962 |
| Avg. Google Rating: | 5 |
| Offering DS Training in Nagpur Since: | 2012 |
| Fees | INR 45,000 |
| Branches in India: | Noida, Delhi, Nagpur, Mumbai |
| Flagship Course Names: |
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| Exclusive Data Science Institute: | Yes |
| Training Delivery Model | Online & Offline |
3. LIVEWIRE
LIVEWIRE combines advanced skills, expert instructors, and personalized attention. As part of CADD Centre, a global skill development group, they aim to unite these elements to prepare students for exciting tech careers.
They offer numerous courses in cutting-edge IT, computer science, electronics, and electrical topics, including data science, machine learning, IoT, AI, and blockchain. These programs ready students for roles in software, automotive, healthcare, and other tech-forward industries.
Industry professionals and product developers create their curricula. Seasoned instructors lead training in modern digital classrooms and industry-grade labs. The approach uses immersive and hybrid teaching methods to enhance skill development.
Course Curriculum:
- Data Science Introduction
- Python Programming Overview
- Exploratory Data Analysis
- Data Visualization Techniques
- Data Distribution and Correlation
- Regression Analysis Methods
- Clustering: Hierarchical and K-means
- Classification: KNN, Naïve Bayes
- Decision Trees and Random Forests
- Text Mining and Word Clouds
- Dimension Reduction and Association Rules
- Time Series Forecasting
- Machine Learning Fundamentals
- Applied Data Science with Python
- Career Readiness Program
Program Highlights
- Expert Faculty
- Student Guidance and Support
- Competitive Pricing
- Hands-On Project Work
- Job Placement Assistance
- Industry-Aligned Training
| Name | Master in Data Science |
| Course Duration | 6 Months |
| Website | www.livetechskills.com |
| Address: | Plot No. 13, 1st floor, Block 2, MB Tower, Nandanvan Main Rd, Mire Layout, Jawahar Nagar, Nandanvan, Nagpur, Maharashtra 440009 Mob: 090960 60149 |
| Avg. Google Rating: | 5 |
| Offering DS Training in Nagpur Since: | 2017 |
| Fees | INR 50,000 |
| Branches in India: | Nagpur |
| Flagship Course Names: |
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| Exclusive Data Science Institute: | Yes |
| Training Delivery Model | Offline |
4. Atlanta Computer Institute
ATLANTA COMPUTER INSTITUTE in Nagpur is Central India’s premier IT education provider. Operating for 23 years, it’s an ISO 9001:2015 certified organization.
Their faculty comprises experienced professionals with diverse tech backgrounds, available throughout the day. They assist with accommodation for out-of-town students. Both regular and accelerated course options are offered. Atlanta’s placement cell connects qualified students with job opportunities. Job-seekers can choose from career-oriented courses, with both online and in-person classes available for all programs.
Atlanta Computer Institute’s Nagpur branch offers cutting-edge technology and expert instruction. Their well-planned courses are led by qualified, experienced staff. IT software training provides excellent career prospects. Students can also take data science certification exams at the institute.
Course Curriculum:
- Module 1: Data Science Basics
- Module 2: Core Foundations (Math & Statistics)
- Module 3: Advanced Statistical Methods
- Module 4: Python Fundamentals
- Module 5: Machine Learning & Data Mining
- Module 6: Predictive Analytics with R
The institute boasts modern computer labs and a systematic approach to training. Their comprehensive data science program covers essential topics, from introductory concepts to advanced analytics tools. This structured curriculum equips students with in-demand skills for the growing field of data science.
| Name | Data Science Courses with Python |
| Course Duration | 6 Months |
| Website | www.atlantacomputer.in |
| Address: | 2nd Floor, Shivaji Complex, Near Coffee House & Bank of Baroda, West High Court Road, Opp. Titan Eye, Dharampeth, Nagpur, Maharashtra 440010 Mobile: 90281 57794, 9595 321479 |
| Avg. Google Rating: | 5 |
| Offering DS Training in Nagpur Since: | 2000 |
| Fees | INR 79,500 |
| Branches in India: | Nagpur |
| Flagship Course Names: |
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| Exclusive Data Science Institute: | Yes |
| Training Delivery Model | Online & Offline |
