Excel, SQL, Python, Power BI & statistics for a job-ready data analyst career — written by Aivasu Tech Institute's mentors. Free to read, structured to actually help you learn.
Data Analytics notes by Aivasu Tech Institute 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 Aivasu Tech Institute's mentor-led Data Analytics course in Pitampura, Delhi.
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.
What the data analytics path covers and how to use it.
The discipline, its goals and its outputs.
Structured, semi-structured and unstructured data.
Turning numbers into clear visual insight.
How the three overlap and differ.
A repeatable process from question to answer.
Analytics in retail, finance, health and more.
Modern frameworks and where the field is going.
Using LLMs to accelerate analysis.
Summarising and explaining data with AI.
Analytics fundamentals inside Excel.
Conditional formatting and the functions you'll use daily.
Summarising large tables in seconds.
Building clean, interactive dashboards.
Answering real business questions.
Descriptive stats without code.
Moving from Excel to Power BI.
AI-assisted formulas and analysis.
Modern Python inside spreadsheets.
New Excel formula power.
Statements and how a query runs.
Protecting and recovering databases.
Selecting the rows you need.
Sorting with ORDER BY.
Naming tables and columns for readability.
COUNT, SUM, AVG, MIN and MAX.
Aggregating by category.
CASE expressions in SQL.
Combining tables correctly.
Queries inside queries.
Reusable queries and faster reads.
Cleaning and transforming text.
Numeric operations in SQL.
Working with timestamps.
Wildcard searches.
Permissions and security.
Ranking and running totals.
Readable, self-referencing queries.
Querying large datasets at scale.
Syntax, variables and types.
Lists, dicts, sets and tuples.
Control flow and functions.
Loading and inspecting datasets.
Fast numerical arrays.
Plotting libraries overview.
Common and advanced chart types.
Interactive and statistical plots.
Plotting location data.
Virtual envs, Jupyter and packages.
Stats with Python.
Linear algebra and maths.
Cleaning and reshaping data.
Your first predictive model.
Faster dataframes than pandas.
Data apps in pure Python.
Classic ML made simple.
Why analysts need statistics.
The distinction that drives inference.
Summarising data.
Mean/median/mode, skew and spread.
Shape and probability.
Guessing population values from samples.
Ranges with stated certainty.
Deciding if an effect is real.
Modelling relationships.
When the model is valid.
Encoding and testing categories.
Updating beliefs with evidence.
Designing valid tests.
Separating cause from correlation.
Importing and shaping data.
Modeling and relationships.
Building interactive visuals.
Putting it all together.
AI-assisted report building.
The modern Microsoft data stack.
Optimising measures and models.
Using AI across the analysis workflow.
Prompts that produce SQL, Python and summaries.
Automating analysis with language models.
Modular, version-controlled transformations.
Analytics on live data.
Semantic search for analysts.
Keeping models healthy.
Automating the data workflow.
Privacy, bias and governance.
Analysing relationships and networks.
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