Free · 88 Topics · No Signup
Data Analytics Notes
Excel, SQL, Python, Power BI & statistics for a job-ready data analyst career — written by CodingNow 2.0's mentors. Free to read, structured to actually help you learn.
Data Analytics notes by CodingNow 2.0 cover 88 topics — from master's program in data analyst (2026): complete roadmap to graph analytics & network data — each explained with short definitions, syntax and runnable code examples. They are 100% free, need no signup, and work as quick revision for college exams, Data Analytics interviews and CodingNow 2.0's mentor-led Data Analytics course in Pitampura, Delhi.
Master's Program in Data Analyst (2026): Complete Roadmap
A complete Master's Program in Data Analyst roadmap — 270+ learning hours, 25+ projects and 10+ tools across Excel, SQL, Python, Statistics and Power BI.
Course Introduction
What the data analytics path covers and how to use it.
Data Analytics Overview
The discipline, its goals and its outputs.
Dealing with Different Types of Data
Structured, semi-structured and unstructured data.
Data Visualization for Decision Making
Turning numbers into clear visual insight.
Data Science vs Data Analytics vs ML
How the three overlap and differ.
Analytics Methodology
A repeatable process from question to answer.
Data Analytics in Different Sectors
Analytics in retail, finance, health and more.
Analytics Framework & Latest Trends
Modern frameworks and where the field is going.
Generative AI in Analytics
Using LLMs to accelerate analysis.
LLM-Powered Business Insights
Summarising and explaining data with AI.
Introduction to Business Analytics
Analytics fundamentals inside Excel.
Formatting & Essential Functions
Conditional formatting and the functions you'll use daily.
Analyzing Data with Pivot Tables
Summarising large tables in seconds.
Excel Dashboarding
Building clean, interactive dashboards.
Business Analytics with Excel
Answering real business questions.
Data Analysis Using Statistics
Descriptive stats without code.
Power BI Integration
Moving from Excel to Power BI.
Excel Copilot AI Features
AI-assisted formulas and analysis.
Python in Excel
Modern Python inside spreadsheets.
Dynamic Arrays & LAMBDA
New Excel formula power.
SQL Fundamentals
Statements and how a query runs.
Restore & Backup
Protecting and recovering databases.
Filtering with WHERE
Selecting the rows you need.
Ordering Results
Sorting with ORDER BY.
SQL Aliases
Naming tables and columns for readability.
Aggregate Commands
COUNT, SUM, AVG, MIN and MAX.
GROUP BY
Aggregating by category.
Conditional Statements
CASE expressions in SQL.
SQL Joins
Combining tables correctly.
Subqueries
Queries inside queries.
Views & Index
Reusable queries and faster reads.
String Functions
Cleaning and transforming text.
Mathematical Functions
Numeric operations in SQL.
Date & Time Functions
Working with timestamps.
Pattern Matching with LIKE
Wildcard searches.
User Access Control
Permissions and security.
Window Functions
Ranking and running totals.
CTEs & Recursive Queries
Readable, self-referencing queries.
SQL for Big Data (Spark SQL)
Querying large datasets at scale.
Python Basics
Syntax, variables and types.
Python Data Structures
Lists, dicts, sets and tuples.
Programming Fundamentals
Control flow and functions.
Working with Data in Python
Loading and inspecting datasets.
NumPy Arrays
Fast numerical arrays.
Introduction to Visualization Tools
Plotting libraries overview.
Basic & Specialized Visualization
Common and advanced chart types.
Advanced Visualization Tools
Interactive and statistical plots.
Maps & Geospatial Visualization
Plotting location data.
Python Environment Setup
Virtual envs, Jupyter and packages.
Statistical Computing
Stats with Python.
Mathematical Computing with NumPy
Linear algebra and maths.
Data Manipulation with Pandas
Cleaning and reshaping data.
Intro to Model Building
Your first predictive model.
Polars High-Performance DataFrames
Faster dataframes than pandas.
Streamlit Analytics Dashboards
Data apps in pure Python.
AI & ML with Scikit-learn
Classic ML made simple.
Introduction to Statistics
Why analysts need statistics.
Sample vs Population Data
The distinction that drives inference.
Descriptive Statistics
Summarising data.
Central Tendency, Asymmetry & Variability
Mean/median/mode, skew and spread.
Distributions
Shape and probability.
Estimators & Estimates
Guessing population values from samples.
Confidence Intervals
Ranges with stated certainty.
Hypothesis Testing
Deciding if an effect is real.
Regression Analysis
Modelling relationships.
Assumptions of Linear Regression
When the model is valid.
Dealing with Categorical Data
Encoding and testing categories.
Bayesian Statistics
Updating beliefs with evidence.
A/B Testing & Experimentation
Designing valid tests.
Causal Inference
Separating cause from correlation.
Get & Prep Data
Importing and shaping data.
Develop Your Data Skills
Modeling and relationships.
Reports & Dashboards
Building interactive visuals.
Tips, Tricks & Capstone
Putting it all together.
Copilot in Power BI
AI-assisted report building.
Microsoft Fabric & OneLake
The modern Microsoft data stack.
DAX Studio & Performance
Optimising measures and models.
AI-Augmented Data Analysis
Using AI across the analysis workflow.
Prompt Engineering for Analysts
Prompts that produce SQL, Python and summaries.
LLMs & GPT APIs for Data Tasks
Automating analysis with language models.
dbt & Data Mesh Architecture
Modular, version-controlled transformations.
Real-Time Streaming with Kafka
Analytics on live data.
Vector Databases & Embeddings
Semantic search for analysts.
MLOps & Model Monitoring
Keeping models healthy.
DataOps & CI/CD Pipelines
Automating the data workflow.
Responsible AI & Data Ethics
Privacy, bias and governance.
Graph Analytics & Network Data
Analysing relationships and networks.
Ready to go from notes to a real career?
Join CodingNow 2.0's Data Analytics course — live mentorship, hands-on projects, and 100% placement support in Delhi NCR.
Enroll Now — Free Demo AvailableData Analytics Notes – FAQs
What students search before reading Data Analytics notes.
