The Master's Program in Data Analyst at Coding Now Tech Institute Gurukul of AI is a project- and placement-driven track that takes you from absolute beginner to job-ready data analyst. Across 270+ learning hours you build 25+ real-world projects and master 10+ industry tools — Excel, SQL, Python, Statistics and Power BI — plus the latest 2025–26 skills like AI-augmented analytics, dbt and real-time streaming.
Program at a Glance
| Metric | Detail |
|---|---|
| Learning hours | 270+ |
| Projects | 25+ portfolio-ready, real-world builds |
| Tools covered | 10+ (Python, SQL, Power BI, Excel, Statistics & more) |
| Modules | 7 structured modules |
| Mode | 100% online / offline classroom |
| Placement | 100% placement assistance |
What Is a Data Analyst?
A data analyst turns raw, messy data into clear, actionable business insight. Companies are drowning in data but starving for understanding — the analyst is the person who cleans it, explores it, visualises it and answers the questions leadership actually cares about: which customers are churning? which route is slowest? which product should we recommend?
It is the most accessible entry point into the data field: you do not need a computer-science degree or heavy maths to start — you need structured thinking, curiosity and the right tool stack. That is exactly what this program builds.
Why Become a Data Analyst?
- A core business function — a company cannot run on raw data alone; analysts who sift through and interpret it are invaluable to every decision.
- Jobs on the rise — demand for data analysts is far outpacing supply, which keeps the role rewarding and secure.
- High salaries — because demand exceeds supply, compensation is strong and climbs quickly with experience.
- Work across every industry — retail, finance, healthcare, logistics, marketing, manufacturing and social media all hire analysts.
The Roles of a Data Analyst
- Collect, clean and structure data from multiple sources.
- Explore data (EDA) to find patterns, trends and outliers.
- Build dashboards and reports that non-technical stakeholders can act on.
- Run statistical analysis and A/B tests to back decisions with evidence.
- Automate recurring analysis with SQL, Python and BI tools.
Tools & Technologies Covered
| Tool | What you'll use it for |
|---|---|
| Python | Data wrangling, analysis and visualisation (pandas, NumPy, Matplotlib, Seaborn) |
| SQL | Querying, joins, aggregations, subqueries and window functions |
| Power BI | Interactive dashboards and business intelligence |
| Excel | Pivot tables, functions, dashboards and Power Query |
| Statistics | Distributions, hypothesis testing and regression |
| Data analytical tools | dbt, Apache Kafka, Streamlit, Copilot and more |
Curriculum — 7 Modules
Module 01 — Introduction to Data Analytics
- Course Introduction
- Data Analytics Overview
- Dealing with Different Types of Data
- Data Visualization for Decision Making
- Data Science, Data Analytics & Machine Learning
- Data Science Methodology
- Data Analytics in Different Sectors
- Analytics Framework & Latest Trends
- Generative AI in Analytics ★ NEW
- LLM-Powered Business Insights ★ NEW
Module 02 — Excel
- Introduction to Business Analytics
- Formatting, Conditional Formatting & Important Functions
- Analyzing Data with Pivot Tables
- Dashboarding
- Business Analytics with Excel
- Data Analysis Using Statistics
- Power BI Integration
- Excel Copilot — AI-Powered Features ★ NEW
- Python in Excel ★ NEW
- Dynamic Arrays & LAMBDA Functions ★ NEW
Module 03 — SQL
- Fundamentals of SQL Statements
- Restore and Backup
- Selection Commands: Filtering & Ordering
- Alias, Aggregate & Group By Commands
- Conditional Statements, Joins and Subqueries
- Views and Index
- String, Mathematical & Date-Time Functions
- Pattern (String) Matching & User Access Control
- Window Functions (Advanced) ★ NEW
- CTEs & Recursive Queries ★ NEW
- SQL for Big Data — Spark SQL ★ NEW
Module 04 — Python
- Python Basics, Data Structures & Programming Fundamentals
- Working with Data in Python
- NumPy Arrays & Mathematical Computing
- Data Manipulation with Pandas
- Statistical Computing
- Basic, Specialized & Advanced Visualization Tools
- Creating Maps & Geospatial Data Visualization
- Intro to Model Building
- Polars — High-Performance DataFrames ★ NEW
- Streamlit — Build Analytics Dashboards ★ NEW
- AI & ML with Scikit-learn ★ NEW
Module 05 — Statistics Essentials
- Sample vs Population Data
- Descriptive Statistics: Central Tendency, Asymmetry & Variability
- Distributions, Estimators and Estimates
- Confidence Intervals & Inferential Statistics
- Hypothesis Testing (with practical examples)
- Regression Analysis & Its Assumptions
- Dealing with Categorical Data
- Bayesian Statistics Fundamentals ★ NEW
- A/B Testing & Experimentation Design ★ NEW
- Causal Inference Techniques ★ NEW
Module 06 — Power BI
- Get and Prep Data
- Developing Reports and Dashboards
- Tips, Tricks & Capstone Project
- Copilot in Power BI — AI Features ★ NEW
- Microsoft Fabric & OneLake Integration ★ NEW
- DAX Studio & Performance Optimization ★ NEW
Module 07 — Latest 2025–26 Topics ★
- AI-Augmented Data Analysis
- Prompt Engineering for Analysts
- LLMs & GPT APIs for Data Tasks
- dbt (Data Build Tool) & Data Mesh Architecture
- Real-Time Streaming with Apache Kafka
- Vector Databases & Embeddings
- MLOps & Model Monitoring
- DataOps & CI/CD Pipelines
- Responsible AI & Data Ethics
- Graph Analytics & Network Data
Real-World Projects
You build 25+ projects across industries, including:
- Logistics & Transportation — Route Optimization: analyse traffic and delivery data to cut delivery times and fuel cost.
- Manufacturing — Predictive Maintenance: use sensor data to forecast equipment failures and minimise downtime.
- Retail — Product Recommendation Engine: analyse purchase history to suggest relevant products and lift sales.
- Marketing — Customer Segmentation: group customers by demographics and behaviour for targeted campaigns.
- Finance — Fraud Detection: apply machine learning to flag suspicious transactions in real time.
- Social Media — Sentiment Analysis: extract public sentiment about a brand from social data.
Career Outcomes
CodingNow 2.0 is one of India's most trusted project- and placement-driven learning platforms. Across programs the institute reports 1000+ students placed, 200+ hiring partners, a ₹34 LPA highest package and a 68% average salary hike. Every learner is assigned a Program Manager who supports you toward your career objective — from resume building and mock interviews to referrals.
Who Should Join
- Students and freshers wanting a high-growth tech career without a CS degree.
- Working professionals switching into data from any background.
- Business, marketing and finance roles that work with data and want to go pro.
Frequently Asked Questions
Do I need a coding or maths background?
No. The program starts from fundamentals in Excel and SQL and builds Python and statistics step by step — it is designed for motivated beginners.
Is the placement support real?
Yes. You get 100% placement assistance with resume building, mock interviews, portfolio reviews and access to 200+ hiring partners.
Online or offline?
Both. Choose 100% online live classes or the classroom in Pitampura, Delhi — same curriculum and placement support.
How long does it take?
The full track is 270+ learning hours across 7 modules, delivered over a few months with flexible schedules.
