Certification Program in

Data Analytics

With

Eligibility

Undergraduates

Application deadline

March 26, 2025

About this program

Comprehensive Learning Path

Case-Based and Interactive Learning

Industry-Standard Tools & Technologies

120+ Hours of Live Learning

8+ Industry-Level Projects

Our recipe for your success

Batch starts

March 2025

Duration

5 months

Learning Hours

200+ hrs

Comprehensive Learning Path

Case-Based and Interactive Learning

Industry-Standard Tools & Technologies

120+ Hours of Live Learning

8+ Industry-Level Projects

Practical Exposure and Masterclasses

Doubt Resolution and Peer Support

Generative AI for Data Analytics

Experience the difference with live learning

Real time engagement withlive classes

Learn at your pace, on your schedule

Enhanced outcomes through more than 80% live and experiential learning

Our holistic learning philosophy

Live learning

Interactive classes with industry experts for hands-on learning

Case studies

Real-world tasks to solidify knowledge and prep for industry challenges.

1:1 counselling

Personalized mentorship for career navigation and professional decisions.

Seasoned faculty

Guidance from leading industry experts to ensure top-tier learning.

Capstone projects

Final projects that blend learning with real impact in your field.

Doubt clearing sessions

Targeted sessions to ensure clarity and readiness for next steps.

Curriculum

Self-paced: Introduction to Analytics

What is Analytics
Why Analytics
Types of Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
Data and Data Sources for Analytics: Small Data, Big Data, Traditional Data and Non-Traditional Data Sources
Analytics in Business
Introduction to Problem Solving using Data
Managing Analytics Projects: CRISP-DM
Analytics Tools: What Tools to Use for which Type of Problems

Programming Basics

Installation and Set-up, IDE, etc.
Python Basics: Syntactics, Variable types, Operators, For/While Loops, Conditional Statements
Functions: Built-in, Library and Custom, Arguments, Return
Data Structures and Operations: Lists, Tuple
Data Structures and Operations: Dictionaries, Sets

Complex Loops and Lambdas

Implement nested loops and control mechanisms (break, continue, pass)
Create and use lambda functions for concise data processing
Combine loops and lambda functions for efficient data manipulation

File Handling

File Handling: i/o functions, open(), read, write, append
More focus on CSV files

Maths for Data Analytics

Linear Algebra: Vectors, Matrices
Calculus: Differentiation, Integration
Probability and Statistics: Mean, Median, Mode, Variance, Standard Deviation

MS Excel Basics

Navigation and Shortcuts, Cells and Ranges, Entering Data, Applying Filters, and Formatting
Data Handling Using Functions: if-else, Concat, Deduplication, Missing Value Imputation, etc.
Data Summarizing using Functions: Countif, Sumif, Sumproduct, iferror, Average, Correlation, etc.
Visualization: Basic Charts/Graphs/Tables and related Customizations

MS Excel Advance

Lookup Functions: vlookup (exact and approx), hlookup (exact and approx), Match, Index, Advanced Sort and Filter, Advanced Charting
Text Functions: Right, Left, Mid, Concatenate, Upper, Lower, Proper, Len
Date Functions: Datevalue, Timevalue, Today, Now, Networkdays, Networkdays.intl
Data Forecast, Data Consolidation
Pivot Tables: Create Table, Grouping, Slicer
Data Security, what-if analysis, and Data Validation

Macros and VBA

Record and Play Macro
Cell Reference
Copy Paste
Variable
Loops-for, do while, do until
if-if else, if elseif else, if elseif else with and operator, if else using for loop, select case
Userform-message box, input box, command button, radio button

SQL Basics

Set up and install SQL-related software on your local machine
Examine why SQL databases are a preferred choice for storing data
Explain the need for a client-server architecture in a database server
Appreciate why SQL is used as a query language to pull data from a database
Fetch data from one or some columns in a table
Use the WHERE clause to filter subsets of data
Use ORDER BY clause to sort data
Use GROUP BY clause with aggregations to summarize data
Use GROUP BY with HAVING Clause to filter grouped data
Use CASE statements to write conditional logic like IF ELSE statements
Work with DATE functions to perform date related operations
Create Databases and Tables
Use INSERT command to add rows to the table

SQL Advance

Use SQL Data Definition Language Statements to define the database structure or schema
Use Data Manipulation Language Statements to manage data within objects
Establish relationships in data to combine data from different tables
Analyze data after combining two or more tables using JOINS
Get insights on data using nested queries

Relating data using SQL

Establish relationships in data to combine data from different tables
Analyze data after combining two or more tables using JOINS
Get insights on data using nested queries

SQL Analytics

Implement Windows Analytic function in Data Science
Analyze the usage of the OVER clause with PARTITION FRAME and ORDER FRAME
Use Aggregate, Ranking, and Windows Analytical Functions for analysis and insights

SQL and Excel

Data manipulation over excel from SQL

Data Analysis Using Pandas and Numpy

Introduction to Pandas and NumPy Modules
Pandas Basics: Data File Handling, Row/Columns Handling, Slicing, Drop, Sort, New Variable Creation, Observing Frequency Count
Pandas Advanced: Multiple Datasets Handling, Merge/Append, Multivariable GROUP BY/crosstab summaries

Datetime and Indexing

Working with Datetime Series Data, Dataframe
Working with Indexes and Multi-level Indexing

Data Analysis and Visualization

Dashboard Making

Performing Descriptive Statistics with Python

Central Tendency: Mean (Arithmetic, Weighted and Geometric), Median, Mode
Dispersion: Ranges, Variance, and Standard Deviation

Data Visualization with Python

Importance of Visualizing Data through Charts
Types of Charts and their Best Uses
Basics of Visualization in Python using Matplotlib and Seaborn
Components of a Plot, Subplots, Functionalities of a Plot
Plotting Data Distributions, Univariate Distributions, and Bivariate Plots

Probability Distributions

Binomial
Poisson
Chi-Square

Hypothesis Testing

Hypothesis Testing
Hypothesis Testing in the Industry for Statistical Testing
Core Statistical Concepts: p-value and Others

ANOVA, Hypothesis Tests, and p-values

One/two way ANOVA
T tests
Chi Square
Non Parametric

Probability Basics and Frequency Distributions

Probability Basics: Definition of Probability, Mutually Exclusive Events, Independent Events, Relative Frequency
Idea of Distributions: Discrete and Continuous
Computing Distributions from Data and Deriving Insights

Introduction to Power BI

Apply filters on columns to restrict the data and display charts
Create various charts to present a Power BI report
Perform joins and relationships to calculate and display correct information in reports

Data Slicing and Dicing Using DAX Query

Develop advanced DAX measurements and computed columns for advanced computations
Use graphics like column, line, pie, combination, scatter, treemap, funnel, gauges, and others to present data
Clean up messy data, model it as per the need, and then format the report to make a professional presentation
Make sophisticated reporting tables and matrices
Convey a story through visuals, turn data to provide insights, and data into interactive visuals
Discover how to use advanced features such as the report page, tooltips, and bookmarks in the data visualization process

Dashboarding and Storytelling with Power BI

Explore basics of storytelling and ABT framework
Create stories from data
Build good data presentation
Make a meaningful data visualization using appropriate charts
Deliver data storytelling

Comprehensive Data Analytics Project

Complete Analytics project where data needs to be cleaned, processed, stored, analysed and visualized

Business Analytics Method

Business Analytics Methods (SWOT, PESTLE, Value Chain, etc.)

Forecasting Techniques

Predictive Analytics (Forecasting Techniques)
Prescriptive Analytics (Optimization, Decision Making)
Sensitivity Analysis

Data Driven Decisions

Business Process Improvement and Data-Driven Decision Making

Case Studies

Case Studies in Business Analytics

GenAI Data Analysis

Analysis through ChatGpt
Create Python Code through ChatGPT
Big Data analytics through Gemini

Self-Paced: Agile Project Management for Data Analytics

Introduction to Agile Methodologies
Agile Frameworks
Agile in Data Analytics
Project Initiation and Planning
Execution and Delivery
Collaboration and Communication
Monitoring and Adapting
Challenges and Best Practices

Capstone Discussion: Part IV

Discussion on Capstone

Unlock valuable insights

5-star rating by 97% of students.

Discover the outcomes of the program.

Explore the custom-curated learning path.

Master industry-relevant tools

Launch projects, leap forward

Blinkit Customer Insights

Leverage data-driven insights to predict customer loyalty, minimize churn, and boost sales. Gain hands-on experience with real-world datasets, mastering advanced analytics techniques to enhance retention, identify opportunities, and accelerate growth effectively.

Hero Fincorp Loan Optimization and Risk Management Initiative

Analyze loan portfolios to improve risk management strategies using SQL, Power BI, and Excel. Identify high-risk loans, streamline approval processes, and enhance financial outcomes through data-driven insights, improving creditworthiness evaluations and decision-making frameworks.

Reducing Machine Downtime at Tesla with Predictive Maintenance

Utilize advanced data analytics to predict machine breakdowns and recommend proactive maintenance schedules. Use SQL and Power BI to enhance operational efficiency, reduce repair costs, increase equipment lifespan, and ensure seamless Tesla operations.

Confused About What You’ll Learn?

Let’s Break It Down!

Curriculum

Tools

Project

I’m new to data analytics — will I be able to keep up?

Absolutely! This program is built for beginners and career switchers. No need to be a coding pro or have a math degree to get started. We ease you in with:

📌 What you’ll start with:

How businesses use data to make decisions.
What terms like descriptive, predictive, and prescriptive analytics actually mean.
The basics of Python and SQL — from zero to functional.

💡 It’s a gradual climb:

From understanding simple Excel reports to building machine learning models.
At every step, you’ll have plenty of examples, walkthroughs, and practice sessions to make sure nothing feels overwhelming.

💡 Think of it like learning to drive:

We start in an empty parking lot before hitting the highway.

Will I be buried under too much information all at once?

Not at all. We’ve divided the program into 5 clear phases so you learn in bite-sized chunks:

📌 Phase 1: Foundations of Business Analytics & Data Science

Learn how businesses actually use data.
Master the basics of Python, SQL, Excel, and Power BI.

📌 Phase 2: Data Cleaning & Exploratory Analysis

Handle messy data and find trends using Pandas, NumPy, Matplotlib & Seaborn.

📌 Phase 3: Machine Learning & Predictive Analytics

Build your first prediction models with Scikit-Learn, and explore AI with TensorFlow & Keras.

📌 Phase 4: Big Data & Cloud Computing

Work with huge datasets using Spark, Hadoop, and real-time tools like Kafka.

📌 Phase 5: Real-World Applications & Capstone

Work on projects solving real business problems — from sales forecasting to fraud detection.

💡 You’ll build confidence layer by layer:

No giant leaps, just steady progress.

Is this all theory, or will I actually work with data?

This is a hands-on program — we want you solving real problems, not memorizing definitions. Expect to:

📌 What you’ll practice:

Clean up raw data from e-commerce and finance companies.
Create dashboards that actually help businesses make decisions.
Build machine learning models to predict sales, detect fraud, and segment customers.

💡 End result:

By the end, you’ll have a full portfolio of projects to show off to employers.

I’ve never written a line of code. Will I struggle?

Not at all. We assume you’re starting from scratch — so we teach Python and SQL step by step.

💡 We make coding friendly and frustration-free:

Simple, real-world examples — no abstract computer science talk.
Code-along sessions where you follow step by step.
Exercises to practice without pressure.

💡 Your progress:

You’ll go from writing your first Python script to building end-to-end analysis projects.

Will I learn to handle messy data? (Because real data is never perfect.)

Absolutely. Cleaning data is half the job — and we make sure you’re good at it.

📌 What you’ll practice:

Fixing missing values and typos.
Combining messy spreadsheets into clean datasets.
Preparing unstructured data like customer reviews for analysis.

💡 Why it matters:

Clean data = better insights = smarter business decisions. It’s a skill recruiters love to see.

What’s Exploratory Data Analysis (EDA), and why does it matter?

Think of EDA as detective work for data. Before you build fancy models, you need to know what’s hiding in the data.

📌 What you’ll learn:

Spotting trends, outliers, and correlations.
Using charts and dashboards to tell clear stories.
Finding the “why” behind the numbers.

💡 The goal:

It’s not just about crunching numbers — it’s about uncovering stories that drive better decisions.

Do I really need to learn machine learning?

You might not need to be a data scientist, but having basic machine learning skills makes you 10x more valuable.

📌 What we cover:

Simple prediction models (like forecasting sales).
Customer segmentation using clustering.
A taste of AI with tools like TensorFlow, Keras & ChatGPT.

💡 Why it’s useful:

Even basic ML knowledge helps you stand out — businesses everywhere are using it.

Is there a final project?

Yes — you’ll wrap up with a Capstone Project.

📌 What you’ll do:

Work on a real business challenge (think sales forecasting or fraud detection) from start to finish.
Clean data, analyze trends, build models, and present your findings.

💡 Why it matters:

It’s the perfect portfolio piece to show future employers.

Get certified

On successful completion of the program, you will be eligible for the Hero Vired certificate that places you in an elite league of professionals.

* Certificates are indicative and subject to change

Our faculty, your mentors

Shakul Malik

Faculty, Hero Vired | 10+ Years

Shakul Malik, a Senior Faculty at Hero Vired, specializes in Python, HTML, CSS, Java, JavaScript, Spring Boot, Docker, Jenkins, and web security. With a Master's in CS from Maharishi Dayanand University, she has also worked with Primed Talent and TCS, delivering full stack development training.

Education

BSc. in Computer Science , Computer Science from MDU

Master’s in Computer Science , Computer Science from MDU

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Upendra

Associate Faculty, Data Science| 15+ Years

Upendra Kumar Tiwari is a Data Scientist specializing in Machine Learning, Data Visualization, NLP, and Financial Analytics, with over 15 years of experience. Currently, he works at Synergistic Compusoft Pvt Ltd, focusing on data analysis and corporate training. Upendra is skilled in deep learning and analytical techniques and is committed to continuous learning and sharing knowledge.

Education

M.Tech from Mahamaya Technical University (formerly UPTU)

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Dr. Prashant Kumar Dey

Associate Program Director, Hero Vired | 7+ Years

Dr. Prashant Kumar Dey has extensive experience with Google, BMW and various startups across France, Singapore and India. He has co-founded AtharvaAI (a deeptech company). He has written 2 books and has published 15+ research papers. He is currently leading the Tech and Data Science domain at HeroVired and Completed Ph.D. from CCU.

Education

Post-Masters (Entrepreneurship), IIT Delhi

Master of Science (Cyber Security), Institut Mines-Télécom (EURECOM)

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Halasya Siva

MBA (Australia), PhD 23+ yrs experience Data Analytics, AI, Digital Transformation

Halasya Siva Subramania is a seasoned Technology Strategist & AI practitioner with over 22 Years in developing analytics solutions across domains like aerospace, automotive, banking, financial services, edutech & large-scale scientific research. Currently, he is the Head of AI & Automation at Telstra, where he has been since July 2019.

Education

Senior Executive MBA in Business Administration and Management from Melbourne Business School

PHD in Physics from University of Houston

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Soumita Mukherjee

Faculty, Hero Vired | 14+ years of corporate experience

Soumita Mukherjee, a marketing and management expert with 15+ years in FMCG, has worked at Amazon, HUL, Pidilite, and Raymond. With an MBA from Goa Institute of Management and a Bachelor's from NIFT Mumbai, she's now a faculty at Hero Vired. Specializing in CRM, branding, product management, and go-to-market strategies, Soumita excels in category management, sales strategy, and driving successful brand growth through new product nurturing.

Education

MBA from Goa Institute of Management and a Bachelor's in Design from NIFT Mumbai

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Sunny Mulchandani

10 years of experience in Data Engineering, Analytics, & Project Management

Sunny Mulchandani holds a Bachelor's degree in Commerce with a focus on Business Entrepreneurship from the University of Pune. With over 10 years of experience in data engineering, analytics, and project management, he has worked with prominent companies like Tata iQ, NielsenIQ, TCS, and eClerx. Proficient in Microsoft Azure, Python, SAS, and machine learning models.

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