Free courses in the order an analyst actually picks the tools up: spreadsheets first, because that is where the data is, then SQL to get it out of a database, then Python with NumPy and Pandas to do what a spreadsheet cannot. Every lesson runs in your browser and the Excel, SQL, Python, Pandas and R courses end with a free certificate.
Overview
Syllabus
- Learn Excel — Cells, ranges, formulas, lookups, pivot tables and the functions analysts use every day. Most of the world's data lives in spreadsheets and most analyses are first shared in one, so this is where the path begins.
- Learn SQL — Select, filter, join, group and aggregate real tables, then window functions and subqueries. SQL is the most requested skill in data analyst job postings and the fastest one to become useful in.
- Learn Python — Variables, control flow, functions and collections - just the two sections an analyst needs before NumPy and Pandas make sense. Classes and objects can wait; a working analyst rarely writes them.
- Numpy Fundamentals — Arrays, vectorized arithmetic, indexing, aggregation and broadcasting. NumPy is the engine under Pandas and every scientific Python library, and understanding it is what makes DataFrame operations stop being magic.
- Pandas Analytics — DataFrames, cleaning, filtering, grouping, merging and reshaping real datasets. This is the step where Python overtakes the spreadsheet: everything you did in Excel, repeatable on data too large to open.
- Python JSON — Read and write files, parse JSON, call a web API and handle what goes wrong. Real data is never handed over as a clean table; an analyst who can fetch their own data is not waiting on an engineer.
- Learn R Programming — Optional. The same analysis ideas in the language of statistics teams: vectors, data frames and data manipulation. Skip it unless a role you want lists R, then take it knowing every concept transfers from the Python steps.
Courses
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In this course, you will learn about JSON and dive into the json library in Python, and master it!
Prerequisites:
- Knowledge in Python
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In this course you will learn how to handle tables in python using pandas
Prerequisites:
- Numpy Fundamentals
- SQL Knowledge
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In this course you will learn everything about file handling in Python, from create, read and write to append and delete.
Prerequisites:
- Basic knowledge in Python
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Learn How To Fetch And Post to API Server using Python
Prerequisites:
- Good knowledge of Python
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Learn to master powerful numerical computations in Python for data science with this concise, hands-on course.
Prerequisites:
- Python Fundamentals
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A free, interactive online Excel course. You work in a real spreadsheet on every lesson - typing formulas, not watching them - with a free certificate when you finish.
- Write formulas with absolute and relative cell references
- Use SUM, AVERAGE, IF, COUNTIF, and the text functions
- Look up and match data with VLOOKUP, XLOOKUP, and INDEX/MATCH
- Sort, filter, and summarize a dataset into a readable report
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A free, interactive online R course. You write R on every lesson, with AI hints when a factor surprises you, and a free certificate when you finish.
- Manipulate vectors, lists, and data frames
- Filter, summarize, and transform real datasets
- Plot data with base graphics
- Apply core statistical functions and tests
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A free, interactive online Python course. You write code on every lesson, get AI hints when you're stuck, and earn a free certificate when you finish.
- Master Python syntax, variables, types, and control flow
- Write reusable functions and work confidently with lists, dicts, and sets
- Build object-oriented Python programs with classes, inheritance, and methods
- Read, manipulate, and structure real data the Pythonic way
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A free, interactive online SQL course. You run real SQL queries against live tables on every lesson, with AI hints when a query doesn't return what you expect, and a free certificate when you finish.
- Write SELECT, JOIN, and aggregate queries on real data
- Filter, group, and sort with WHERE, GROUP BY, and HAVING
- Compose subqueries and CTEs for complex questions
- Design simple relational schemas with the right keys and types
