Post Graduate Program in Data Science and Generative AI

In collaboration with  Microsoft_logo_

4.8/5 (1600+ ratings)
5/5

18000+ Enrolled Students

“Data Science helps businesses by extracting insights from data, enabling data-driven decision-making, optimizing processes, and predicting trends and outcomes.”

Upcoming Batches

schedule

24 May.
2025

schedule

31 May.
2025

schedule

07 June.
2025

schedule

14 June.
2025

online-education
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Live Training Hours

Online course
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Hiring Partners

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Tools Covered

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Program designed in collaboration with

Course Curriculum

Designed by Top Industry Leaders and Domain Experts.
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Introduction to Excel for Data Professionals

You will get to learn about Excel formatting, formula structure, and essential tools used for
cleaning, organizing, and presenting data.

You will get to learn how to apply IF, VLOOKUP, INDEX-MATCH, and SUMIFS to solve real-world
business problems and automate calculations.

You will get to learn how to create dynamic dashboards, build PivotTables, and use conditional
formatting for business reporting.

Introduction to Power BI and Tableau

You will get to learn Power Query, DAX basics, slicers, and charts to build interactive
dashboards for business insights.

You will get to learn Tableau basics including connecting data, applying filters, using calculated
fields, and designing dashboards.

You will get to learn how to create visually appealing and functional dashboards using both
tools for practical, real-world cases.

Introduction to SQL and No-Code Automation Tools

You will get to learn SQL concepts like SELECT, JOINS, GROUP BY, subqueries, and window
functions to analyze business data.

You will get to learn how to use Notion, Trello, Miro, and Coda for task planning, knowledge
sharing, and team collaboration.

You will get to learn tools like Octoparse, Apify, and Parabola to scrape data and automate
workflows without writing code.

Introduction to AI Tools for Content, Finance and Productivity

You will get to learn how to use Jasper, Canva Magic Studio, Copy.ai, and Writesonic to
generate creative content and marketing copy

You will get to learn tools like Kensho, FinChat, and Domo AI to generate insights, read reports,
and build finance dashboards.

You will get to learn how to automate tasks using Notion AI, Otter.ai, Make.com, and Zapier for
meetings, CRM, and workflows.

Introduction to Advanced AI Tools and Prompt Engineering

You will get to learn prompt frameworks, templates, and multi-step logic to guide AI models in
tasks like writing, coding, and planning.

You will get to learn how to use Gumloop, Browse AI, Tome AI, Runway ML, Descript, and
Wordtune for storytelling, editing, and content.

You will get to learn how to use Bardeen AI, Levity AI, and Lamini to build document workflows,
email automation, and business bots.

Capstone Projects Using Generative AI Tools

You will get to build dashboards using Power BI, Excel, and ChatGPT that auto-generate insights
and summaries.

You will get to create marketing content using Jasper and Canva and develop explainer videos
using Runway ML and Synthesia.

You will get to build a chatbot prototype, create sentiment analysis reports, and automate
reporting with Notion AI and Zapier.

Program Highlights

The Program is designed by Industry Leaders and Domain Experts from Stanford University and Microsoft

support

500+

Hours of Content

Live Sessions

100+

Live Sessions

Assignments

100+

Assignments

Industry Projects

25+

Industry Projects

10+

Case Studies

Mock Tests

10+

Mock Tests

Domain Specialization & Electives

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banking

Banking, Finance, & Insurance: You will work on Projects such as Churn Analysis, Risk-Reward Analysis, Stock Market Analysis and Fraud Analysis to understand core BFSI Concepts.

Ecommerce

Ecommerce & Marketing: You will learn Customer lifetime Analysis, Ad Campaign Analysis, Market Basket Analysis & Dynamic Pricing from this segment.

Healthcare

Healthcare & Pharmacy: You will learn Payer and Provider Analytics, Analytics used in the Pharmaceutical Industry to understand core Healthcare Concepts.

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HR & Operations: You will learn Attrition Analysis, Promotion Analysis, Productivity Analysis & Resource Optimization to understand core BFSI Concepts.

Electives

Model Deployment: You will get to learn Machine Learning Model Deployment using Rest APIs and AWS EC2 to facilitate an ML Model to an end user.
Natural Language Processing: You will learn Part of speech tagging, Named Entity Recognition, Topic Modelling, Text summarization, and Text Classification.
Deep Learning: You will get to learn ANN, CNN, and Various Important CNN Architectures crucial for solving high-level problems.

Weekdays Problem Solving Sessions

Get Problem solving sessions from Industry experts on weekdays

Solve problems on hackerrank and hackerearth to improve your problem solving capability.

Learn new problem solving tips and tricks everyday.

Solve problems using Python programming language, sql, and excel.

Get personalized doubt sessions, if required anytime.

Industry Projects

Industry

Automate Marketing Campaign Content at Scale

Empower marketing teams by auto-generating personalized ad copies, product descriptions,
and social media creatives.
Leverage AI design and content tools to scale campaigns across platforms while maintaining
brand tone and consistency.

Skills: Jasper, Copy.ai, Canva Magic Studio, Writesonic, AdCreative.ai, SurferSEO, MarketMuse,
Notion AI

Classification

AI-Powered Financial Insight Generator

Enable business teams to access instant financial summaries and insights without manual
effort.
Use AI to query reports, analyze 10-K filings, and forecast metrics directly inside dashboards
and spreadsheets.

Skills: FinChat, Kensho, Domo AI, Power BI AI visuals, Numerous.ai, Notion AI

Prediction

Automated Meeting Summary & Task Tracker

Create an AI system to transcribe meetings, summarize action items, and integrate with team
workspaces.
Use voice-to-text and AI summarization to boost productivity and reduce manual
documentation effort.

Skills: Otter.ai, Notion AI, Zapier, Make.com, Trello, Miro, Quip

Climate

Dynamic Business Proposal Generator

Build a no-code AI form that captures client requirements and outputs polished proposals.
Automate text enhancement, formatting, and visual presentation using dedicated business
tools.

Skills: Gumloop, Wordtune, Tome AI, Notion, Coda

Product Prices

Real-time Market Tracker with Alerts

Monitor competitor websites or pricing data automatically and trigger Slack/email alerts.
Combine web automation tools with spreadsheet AI insights for live dashboards.

Skills: Apify, Browse AI, Parabola, Numerous.ai, Zapier, Slack Integration

AI Customer Support Summary System

Develop a system that summarizes support tickets, classifies queries, and suggests replies.
Automate common support workflows while maintaining context and clarity.

Skills: Levity AI, Descript, Notion AI, Typewise, Bardeen AI

Recommend products

Self-updating SEO Blog Generator

Generate SEO-optimized blog content and schedule publication across channels with minimal
human intervention.
AI handles topic selection, keyword optimization, and structured formatting.

Skills: Jasper, SurferSEO, Writesonic, Make.com, Notion AI

Classification cnn

Smart Investor Pitch Deck Creator

Design compelling pitch decks automatically from prompts and input ideas.
Use AI storytelling and design tools to create investor-ready presentations with visuals,
structure, and data.

Skills: Tome AI, Canva Magic Studio, Notion AI, Wordtune

Product Prices

Automated Marketing Content Engine

Generate high-quality ad copy, product descriptions, and blog content tailored for different
platforms and audiences.
Integrate AI tools to automate weekly campaign planning and optimize content for conversions
and SEO.

Skills: Jasper, Copy.ai, Writesonic, SurferSEO, MarketMuse, AdCreative.ai, Canva Magic Studio

AI-Enhanced Financial Insight Dashboard

Enable real-time analysis of financial statements and forecasts using AI-enhanced spreadsheet
tools.
Integrate natural language queries and visualizations for decision-makers.

Skills: FinChat, Numerous.ai, Power BI AI visuals, Kensho, Domo AI

Recommend products

AI Meeting Notes and Task Tracker

Automatically transcribe meetings, extract key points, and assign follow-ups with integrations
to team tools.
Streamline knowledge retention and action tracking across distributed teams.

Skills: Otter.ai, Notion AI, Trello, Make.com, Quip, Zapier

Classification cnn

No-Code Proposal Generator

Create a dynamic form-based system that outputs business proposals based on user input,
auto-enhanced by AI.
Perfect for freelancers, agencies, or sales teams to scale custom documentation.

Skills: Gumloop, Wordtune, Notion, Tome AI

Product Prices

Live Market & Competitor Monitor

Track product listings, price changes, or new launches from competitor sites and generate
alerts and insights.
Use AI with automation tools to provide continuous intelligence for business teams.

Skills: Browse AI, Apify, Slack Integration, Parabola, Numerous.ai

AI-Powered Customer Support Analyzer

Summarize, tag, and classify incoming support tickets while suggesting auto-replies and
escalation steps.
Boost response time and efficiency in customer service workflows.

Skills: Levity AI, Notion AI, Descript, Bardeen AI, Typewise

Recommend products

Auto-Publish SEO Blog Engine

Build a system that creates SEO-optimized blogs and schedules them for posting with minimal
manual intervention.
Ensure consistent, keyword-rich publishing across business verticals.

Skills: Jasper, SurferSEO, Writesonic, Make.com, Notion AI

Classification cnn

AI-Generated Investor Pitch Deck

Turn business ideas or input data into professional, investor-ready presentations using GenAI.
Auto-generate visual layouts, key metrics, and story structure.

Skills: Tome AI, Canva Magic Studio, Wordtune, Notion

Case Studies

Automated Business Reporting with Generative AI

Deployment of AI tools to streamline monthly reporting workflows across sales, marketing, and finance departments. In this case study, you will use tools like Notion AI, Numerous.ai, and Power BI AI visuals to generate natural language summaries from dashboards and spreadsheets. You will evaluate how AI-generated reports improve clarity, reduce reporting time, and support faster decision-making. The case will also explore integrating tools like Zapier or Make.com for end-to-end automation.

AI-Powered Customer Support Summarization System

Design and implementation of an AI-driven solution for summarizing customer support tickets and automating response suggestions. In this case study, you will use tools like Levity AI, Descript, and Notion AI to categorize queries, extract key issues, and generate summaries or draft replies. You will assess the impact of the system on support resolution time, customer satisfaction, and agent productivity. Comparisons will be made between manual ticket handling and AI-augmented workflows.

AI-Powered Marketing Content Generation for E-commerce

Implementation of generative AI tools to automate the creation of marketing content for an online retail brand. In this case study, you will use tools like Jasper, Canva Magic Studio, and Writesonic to generate personalized product descriptions, ad creatives, and social media posts. You will evaluate the impact of AI-generated content on engagement metrics, time savings for the marketing team, and consistency in brand voice. A/B testing will be used to compare human-written and AI-generated content to assess effectiveness in real-world campaigns.

Outcomes of this Program

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  • Great Understanding of all kinds of Data and Datasets.
  • Extract Information and Patterns from any kind of Raw Data.
  • Create Visualizations and Dashboards for Data Driven Decision Making.
  • Great Understanding of Structured and Unstructured Databases
  • Perform any kind of analysis on any kind of data.
  • Great Understanding of Supervised and Unsupervised Machine Learning Algorithms.
  • Hands on with Tableau, Power BI, and Excel for Building any kind of Dashboards or Data Visualizations.
  • Critical Understanding of Problems, Strategic Problem Solving and Automating Processes.

Alumni Highlights

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400+

Global Companies

6 LPA

Average CTC

34.7 LPA

Highest CTC

84%

Average Hike

role offers

This Program is Ideal for

Working Professionals

Working Professionals

Working Professionals looking forward to transition and build their Careers into Data Science and Analytics.

data-sceince

Data Science Enthusiasts

Working Professionals who wish to Upskill themselves by learning the latest technologies to get the most out of it.

Freshers and Students

Freshers and students who wish to start their Careers and get their first job into the Data Science and Analytics Field.

Success Stories

Career Support and Guidance

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  • Personalized Feedbacks for Weekly Assignments and Case Studies by Top Industry Experts and Subject Matter Experts.
  • 20+ step by step Guided Industry Projects and Case Studies from Domain Experts and Subject Matter Experts.
  • Multiple Mock Interviews conducted by Kaggle Grandmasters, Seasoned Data Scientists from Startups and MNCs.
  • Resume and Cover Letter Preparation from Top curated Resume Experts. Guidance for Building Linkedin Profile, Github Profile etc.
  • Lifetime Access to Meritshot Learning Portal, Meritshot Alumni Support, and Placement Services at Meritshot.

Weekly Commitment

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Problem solving sessions (Mon-Fri) 60 minutes

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Live Sessions (Sat-Sun) 90 minutes.

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One hour on weekdays, and 3-4 hours on weekends, 11-13 hours/week

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Weekly Assignments shared every monday. (Average time spent to solve: 45 minutes)

Program flow

5 Months

Learn Python, R, Tableau, Excel, SQL, Power BI, Statistics, Data Science and Machine Learning with real-world Projects and Case-studies.

2 Weeks

Work on Domain Specialization Projects, Case studies, and Elective Subjects such Model Deployment, NLP, and Deep Learning.

2 Weeks

Become Job ready by preparing Portfolio on Github, Hackerrank, Medium, etc.

Program Certificates

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Frequently Asked Questions

While there may be some overlap in their roles and responsibilities, there are distinct differences between a Data Analyst, Business Analyst, Data Engineer, and a Data Scientist. Here are some general descriptions of each:

  1. Data Analyst: A Data Analyst is responsible for gathering, processing, and performing statistical analyses on data. They may also be responsible for creating reports and visualizations to help business stakeholders understand trends and insights in the data.
  2. Business Analyst: A Business Analyst focuses on identifying business problems and proposing solutions through data analysis. They may work closely with business stakeholders to gather requirements and help define project goals. They may also be involved in identifying and recommending new business opportunities.
  3. Data Engineer: A Data Engineer is responsible for designing, building, and maintaining the infrastructure needed to support data processing and analysis. They may be involved in designing data pipelines, managing data storage, and developing tools and processes for data access and retrieval.
  4. Data Scientist: A Data Scientist is responsible for identifying business problems that can be solved through data analysis, as well as designing and implementing statistical models and machine learning algorithms to extract insights from data. They may also be responsible for communicating insights to non-technical stakeholders and developing strategies for data-driven decision-making.

While the specific roles and responsibilities may vary depending on the organization, these are some general differences between a Data Analyst, Business Analyst, Data Engineer, and Data Scientist. It's worth noting that these roles may also require different skill sets, with some overlap in areas like statistics, programming, and data analysis.

There are several programming languages and BI tools that are commonly used in data science, and the choice of which one to use often depends on the specific needs of the project or organization. Here are some of the most popular options

  1. Programming Languages: Python and R are two of the most commonly used programming languages in data science. Python is popular because of its versatility and ease of use, while R is often preferred for its statistical capabilities.
  2. BI Tools: There are several BI tools that are popular in data science, including Tableau, and Power BI. These tools are often used for data visualization and reporting, and can help to make insights more accessible to non-technical stakeholders.

We are going to cover Python Programming Language in this course, because it is the most popular programming language right now, and we will cover Tableau as a BI Tool because of its popularity and versatility.

Choosing a domain specialization can be beneficial when learning data science, but it is not always necessary. Here are some factors to consider when deciding whether or not to specialize in a particular domain:

  1. Relevance to your career goals: If you have specific career goals in mind, choosing a domain specialization can help you gain the skills and knowledge you need to succeed in that field. For example, if you are interested in healthcare, specializing in healthcare data analytics could be a good choice.
  2. Depth of knowledge: Specializing in a particular domain can help you gain a deeper understanding of the data and analysis techniques used in that field. This can be especially important when working with complex or sensitive data.
  3. Generalizability of skills: While specializing in a particular domain can be beneficial, it is also important to have a strong foundation in the fundamentals of data science. This will allow you to apply your skills to a wide range of problems and industries.

Ultimately, the decision to specialize in a particular domain will depend on your individual goals and interests. If you are interested in a particular field, specializing in that area can help you gain a competitive advantage and stand out to potential employers. However, if you are more interested in developing general data science skills, it may be better to focus on building a strong foundation in the core concepts and techniques.

Data science can be a good career option for freshers or beginners who are interested in working with data and using it to solve complex problems. Here are some reasons why:

  1. High demand for data scientists: Data science is a rapidly growing field, with a high demand for skilled professionals. This means that there are plenty of job opportunities for freshers and beginners who are looking to get started in the field.
  2. Good salary potential: Data scientists are often well-compensated for their skills and expertise, with competitive salaries and benefits packages.
  3. Variety of industries and applications: Data science is used in a wide range of industries and applications, from healthcare and finance to marketing and e-commerce. This means that there are many different career paths available for those who are interested in data science.
  4. Opportunities for growth and advancement: As you gain experience and expertise in data science, there are many opportunities for growth and advancement within the field. This could include moving into more senior roles, taking on leadership positions, or starting your own data science consulting business.

Overall, data science can be a rewarding and fulfilling career option for freshers and beginners who are passionate about working with data and solving complex problems. However, it is important to note that data science is a highly competitive field, and it may take time and effort to build the skills and experience needed to land your dream job.

In any case, If you fail to complete the course in a specified time of 11 Months, You can take extra time for completing your course. If you are stuck with the Projects or assignments, Our Teaching assistants and Instructors will help you to complete them as soon as possible.

Apart from that, If in the middle of the program, you meet with some uncertain circumstances, such as an accident or any kind of medical emergency then we will provide you a fresh batch as per your convenience.

Or, If you are unable to focus on the Lectures due to work pressure in the office or some personal reasons such as Marriage/Vacations/Family functions etc, we will provide you with Recorded Lectures. If you get stuck with the recorded lectures, you can avail personalized doubt sessions as per your convenience.

A case study and a project are both common methods used in various fields to solve problems, but there are differences between the two:

  1. Purpose: A case study is usually done to analyze a specific situation, event or phenomenon in order to understand it better and draw conclusions. A project, on the other hand, typically involves solving a problem or creating something tangible.
  2. Scope: A case study is often focused on a single organization, individual, or event. A project can have a broader scope and can involve multiple organizations or individuals.
  3. Methodology: A case study typically involves extensive research and analysis of data collected through interviews, observations, and documentation. A project often involves designing and implementing a solution, and may include tasks such as coding, testing, and implementation.
  4. Outputs: The outputs of a case study are typically reports or papers that present the findings of the research. The outputs of a project can include a wide range of deliverables, such as software, reports, presentations, or prototypes.

Overall, the main difference between a case study and a project is the purpose and scope of the work. While a case study is focused on understanding a situation or phenomenon, a project is focused on solving a problem or creating something tangible.

In this Program, You will get to work on both Projects, and Case studies. You will also get to work on domain specialized Projects and Case studies which will help you with enormous clarity into the subject.

There are many advantages to becoming a data analyst or a data scientist, including:

  1. High demand: Data analysts and data scientists are in high demand in many industries, including healthcare, finance, and technology. This means that there are plenty of job opportunities available and a strong job market.
  2. Competitive salaries: Due to the high demand for data analysts and data scientists, salaries in these fields are typically quite competitive, especially for those with specialized skills or advanced degrees.
  3. Variety of job roles: Data analysts and data scientists can work in a variety of roles, including as consultants, researchers, or software developers. This means that there is plenty of room for growth and advancement within the field.
  4. Opportunities for creativity: Data analysis and data science involve solving complex problems using data, which can be a very creative and rewarding process. It also provides opportunities to work on new projects and explore new areas of research.
  5. Ability to make an impact: By using data to inform decision-making, data analysts and data scientists can make a significant impact on the organizations they work for. This can be especially rewarding for those who are passionate about making a positive difference in their work.

Overall, becoming a data analyst or a data scientist can offer a rewarding and fulfilling career with many opportunities for growth, creativity, and impact.

We Offer Live Training of more than 650 Hours by Domain Experts and Industry Leaders. Meritshot provides unique Problem solving sessions during the weekdays to understand the important practical and core concepts in depth and details. You not only get personalized mentoring sessions, soft skill sessions, but also get personalized evaluations on your Tests, Assignments, and Mock-ups.

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