Post Graduate Program in Data Science and A.I.

In collaboration with  Microsoft_logo_

4.8/5 (1600+ ratings)

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


01 July


04 July


06 July


08 July

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Live Training Hours

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Tools Covered


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Course Curriculum

Designed by Top Industry Leaders and Domain Experts.

Preparatory Course

  • You will get to learn about the Mathematical, Logical, Statistical, Financial, Text, Datetime, Lookup functions, Data Cleaning, Feature Engineering, Linear Regression, What if analysis, scenario management, Forecasting, Data Visualizations, Pivot Tables, and Dashboards.
  • You will get to learn about the Charts, Maps, Data visualization, Data cleaning, Table calculations, Level of detail expressions, Calculated fields, Aggregations, Granularity, Data Extracts, Filters, Dashboards, and Storyboards in Tableau and Power BI.
  • You will get to learn about the Charts, Maps, Data visualization, Data cleaning, Table calculations, Level of detail expressions, Calculated fields, Aggregations, Granularity, Data Extracts, Filters, Dashboards, and Storyboards in Tableau and Power BI.

Data Analysis Course

  • You will get to Learn about the concept of Descriptive Statistics, Inferential Statistics, Normal Distribution, Basic Probability, Conditional Probability, Basics of Hypothesis Testing, Student T test, Z test, Anova Test, Chi squared test, P-value etc.
  • You will get to Learn about Missing Values treatment, Outliers treatment, Categorical encoding techniques, Data Manipulation functions, Data transformations, Data Processing, Feature engineering, Data Splitting, and Feature Scaling.
  • You will get to Learn about the most important libraries that are Numpy for Advanced Mathematical Operations, Pandas for any kind of Data and Data Frame Manipulations, and Seaborn/Plotly/Matplotlib for Any kind of Data Visualizations, Interactive and 3D Visualizations.

Machine Learning Course

  • You will learn about the concept of Clustering analysis, KMeans clustering algorithm, Elbow Method, Hierarchical Clustering, DBSCAN Clustering, Concept of Dimensionality Reduction, Principal Component Analysis, Linear Discriminant Analysis.
  • You will get to Learn about Linear Regression, Concept of Regularization, Logistic Regression, KNN, SVM, Decision Trees, Random Forests, Concept of Bagging & Boosting, Boosting algorithms such as Gradient Boosting, AdaBoost, XGBoost.
  • You will get to learn about the basic concepts of Time series forecasting, and Time series forecasting Models such as AR, MA, ARMA, ARIMA, SARIMA, ARIMAX, SARIMAX, Introduction to Recommender systems, Content based and collaborative based filtering etc.

Big Data and SQL Course

  • You will learn how to access databases using python programming language, how to Load Tables in python, running sql queries in Python, Introduction to Query Analysis for Python Data Frames using Logical Operators and comparison operators etc.
  • You will get to learn Databases fundamentals, Concept of Relational Databases, Introduction to MySQL, Basic SQL Commands, Introduction to DDL, DCL, and DML Queries, concept of Joins, Types of Joins, Sub queries, and set operations.
  • You will get to learn about the history of Big and Data, Concept of Map Reduce, Introduction to Spark, Understanding the architecture of Spark, Big Data Analysis using Spark, Handling Live Data, Implementing Machine Learning algorithms on Big Data.

Model Deployment and Artificial Intelligence

  • You will get to learn about the Fundamentals of Model Deployment using Rest APIs, and AWS EC2. You will get to learn about the Rest APIs and AWS EC2, S3 in detail. You will also get to learn about the Machine Learning Pipelines and Best Practices for Machine Learning Model Deployment.
  • You will get to learn about the Fundamentals of Natural Language Processing. Concepts such as Text Cleaning, Lemmatization, Stemming, Text Processing, and Text Analysis. You will get an understanding about Sentiment analysis, Topic Modeling, Named Entity Recognition etc.
  • You will get to learn about the Fundamentals of Deep Learning. You will get to learn about the Artificial Neural Networks, and Convolutional Neural Networks in detail. You will get hands-on knowledge on most popular Deep Learning Libraries such as Tensorflow and PyTorch.
Program Highlights

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



Hours of Content

Live Sessions


Live Sessions




Industry Projects


Industry Projects





Case Studies

Mock Tests


Mock Tests

Domain Specialization & Electives


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


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


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


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


Keyword analysis and generation for google ads

Optimize search engine marketing campaigns by identifying relevant keywords for Google Ads to improve ad targeting and increase visibility, clicks, and conversions.

Skills: Data Analysis, Data Visualization, Data Cleaning, Feature Engineering


Quora Insincere Questions Classification analysis

Enhance content moderation on Quora by using ML algorithm to classify and filter out insincere questions to maintain the integrity of the platform and provide a better user experience.

Skills: Text Data Cleaning, Data Processing, Natural Language Processing, Deep Learning


Cabs Trip and Travelling duration Prediction

Predict the duration of cab trips using machine learning to optimize route planning, improve customer satisfaction, and enhance operational efficiency in the transportation industry.

Skills: Exploratory Data Analysis, Feature Engineering, Machine Learning, Evaluation Metrics


Climate Change Impact on Global Food Supply Chain

 Understand the Impact of Climate Change on the Global Food Supply Chain. Frequent Climate change and irregularities are big challenging environmental issues.

Skills: Data Visualization, Statistical Analysis, Data Manipulation, Feature Engineering

Product Prices

Product Prices Suggestions for Online Sellers

Provide online sellers with data-driven price suggestions using machine learning to optimize pricing strategies, increase sales, and maximize profits in the competitive e-commerce market.

Skills: Statistical Analysis, Data Manipulation, Feature Engineering, Machine Learning

Demand Forecasting for an E-commerce Giant

Forecast demand for a giant ecommerce, enabling effective inventory management, reducing stockouts and overstocks, customer satisfaction, and optimizing supply chain operations.

Skills: Time Series, Statistical Analysis, Data Visualization, Machine Learning

Recommend products

Recommend products to most suitable customers

Utilize personalized recommendations based on user behavior and preferences using ML to improve user experience, increase engagement, and drive sales for retail and e-commerce.

Skills: Recommender Systems, Deep Learning, Content based filtering, collaborative based filtering

Classification cnn

Traffic Signs Classification using CNN

Accurately classify traffic signs using ML to enhance road safety, assist in autonomous driving, and improve traffic management and enforcement in transportation systems

Skills: Convolutional Neural Networks, CNN Architectures, Image Data Processing, Image Augmentation.

Case Studies

Data Analysis & visualization

Estimation of Credit Risk using Data Analysis and Visualization. In this Case study, You will use the concept of Data Analysis and Visualization on a Banking Dataset to find out the Credit Risk associated with each of the borrowers. By performing this case study you can help the bank to calculate the risk associated with Loan borrowers.

Data Cleaning & Enrichment

Data Cleansing and enrichment for a very big Job portal. In this case study you will use the concept of Data Cleaning and processing on a Job Portal Dataset so that it could be used for further analysis, and training ML or DL Models. By performing this case study you will help the job portal to make data driven decision making and implement a variety of other AI algorithms to get detailed and deep insights.

Dimensionality Reduction

Comparison of Dimensionality reduction methods on a Healthcare Dataset with very high dimensionality. In this case study you will use the concept of dimensionality reduction and apply various algorithms to check the information loss in each of the cases, and evaluate if the concept of dimensionality reduction is helpful or not.

Languages and Tools Covered

Outcomes of this Program

  • 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 Machine Learning and Deep Learning algorithms.
  • Develop powerful Predictive models using Machine learning and Deep Learning algorithms.
  • Great understanding of NLP and Time series concepts.

Alumni Highlights



Global Companies


Average CTC

34.7 LPA

Highest CTC


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 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

  • 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


Problem solving sessions (Mon-Fri) 60 minutes


Live Sessions (Sat-Sun) 90 minutes.


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


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

Program flow

9 Months

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

2 Months

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

1 Month

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

Program Certificates


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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