Python for data analysis training teaches professionals to use Python, pandas and visualisation libraries to import, clean, transform, analyse and chart data in Jupyter notebooks. It suits analysts who have outgrown spreadsheets, covering reproducible workflows and report automation, with an introduction to using AI coding assistants responsibly.
- Recommended: 4 days in person, or 8 × 3-hour live virtual sessions
- Level: Intermediate · In person · Live virtual · Blended
- Designed with the ADDIE model and customised to your context
- Offered by Bodhih since 2008 to 2,000+ organisations across 7 regions
Also known as: Python for data analysis course, pandas training, Python training for analysts, Python corporate training
Where and how: Python for data analysis training as an in-person workshop in Bengaluru, Mumbai, Delhi NCR, Gurugram, Hyderabad, Chennai, Pune, Kolkata, Ahmedabad and Jaipur; as a live online course; or delivered overseas in Dubai, Singapore and across the Middle East, Asia and Africa.
When spreadsheets stop scaling
Large files, repeated manual steps and complex merges push spreadsheets to their limits. Analysts spend more time wrangling data than interpreting it.
Python for data analysis training gives teams a reproducible alternative. A notebook records every step, so an analysis can be rerun, reviewed and shared, and routine reports can be automated.
The World Economic Forum reports AI and big data as the fastest-growing skills to 2030.
What participants will be able to do
Write Python confidently
Use variables, data types, lists, dictionaries, loops and functions to solve small data tasks.
Wrangle data with pandas
Load, filter, clean, reshape, merge and group data using pandas DataFrames.
Analyse and summarise
Calculate descriptive statistics, pivot summaries and time-based trends for business questions.
Visualise clearly
Create readable charts with matplotlib and seaborn and choose the right chart for the message.
Automate recurring reports
Turn a manual monthly report into a reusable script that reads inputs and writes formatted Excel output.
Work reproducibly and safely
Organise notebooks, document steps and follow data privacy rules when using sensitive data or AI coding assistants.
Who should attend
- Analysts who already use Excel heavily and want to scale up
- MIS and reporting teams automating recurring outputs
- Finance, risk and operations professionals in BFSI
- GCC analytics and business intelligence teams
- Engineers and quality professionals in manufacturing working with production data
Program outline
Recommended design, customised to your context after a short needs analysis.
01Python foundations for analystsModule 1 · 4 hours+
- Setting up and using Jupyter notebooks
- Data types, lists, dictionaries and loops
- Writing simple functions
- Reading error messages without panic
02pandas essentialsModule 2 · 4 hours+
- Series and DataFrames
- Reading CSV and Excel files
- Selecting, filtering and sorting
- From Excel formulas to pandas equivalents
03Cleaning and transforming dataModule 3 · 4 hours+
- Missing values, duplicates and data types
- String and date handling
- Merging, joining and reshaping tables
- Lab: combining regional sales files with inconsistent formats
04Analysis and aggregationModule 4 · 3 hours+
- groupby, pivot_table and crosstabs
- Descriptive statistics and distributions
- Time series resampling and rolling metrics
05VisualisationModule 5 · 3 hours+
- matplotlib and seaborn basics
- Choosing charts for comparisons, trends and distributions
- Formatting charts for business audiences
06Automation and good practiceModule 6 · 3 hours+
- Scripting a monthly report end to end
- Writing results to formatted Excel files
- Using AI coding assistants and checking their code
- Data privacy do's and don'ts with real datasets
07Capstone projectModule 7 · 3 hours+
- Answer a business question from raw data
- Peer code review
- Present findings and next steps
How we deliver it
Excel as the bridge
Concepts are introduced by comparison with familiar Excel operations, which lowers the barrier for people who have never coded before.
Notebook-based labs
Every session is hands-on in Jupyter notebooks, with guided exercises followed by open challenges on industry datasets.
Environment readiness
We work with your IT team in advance on installation or a cloud notebook option, so no lab time is lost to setup.
ADDIE-based tailoring
We analyse the data tasks your teams handle, then choose datasets and capstones that match finance, operations, HR or engineering needs.
Tailored versions
For finance and BFSI analysts
Uses transaction, portfolio and collections data, with emphasis on reconciliations, ageing and variance analysis, and strict masking of customer identifiers throughout the labs.
For manufacturing and quality teams
Works with sensor, production and defect data, covering time series, control charts and shift-level summaries.
Foundations bootcamp
A slower-paced version for professionals with no coding background, adding extra practice on Python basics and a buddy system between sessions.
How we measure impact
Following the Evaluate stage of ADDIE, participants take a hands-on pre and post coding assessment on AssessAll covering pandas, analysis and charting. The capstone is scored against a rubric for correctness, readability and insight. At 30, 60 and 90 days we track scripts in active use, hours of manual reporting replaced and manager feedback on analysis quality, and we offer a clinic for code questions from live work.
Pair this program with AssessAll, Bodhih’s AI assessment platform, for pre- and post-program skill measurement.
Frequently asked questions
What is Python for data analysis training?
It is a hands-on program that teaches professionals to use Python and libraries such as pandas and matplotlib to clean, analyse and visualise data. Python for data analysis training focuses on practical business tasks, like combining files and automating reports, rather than software development.
Can Excel users learn Python for data analysis?
Yes, and they often learn quickly because they already think in tables, filters and formulas. The program maps familiar Excel operations to pandas equivalents. No prior programming experience is required for the foundations variant, although comfort with Excel is strongly recommended.
What is pandas used for?
pandas is a Python library for working with tabular data. It lets you read files, filter and clean rows, reshape tables, merge datasets and calculate summaries with a few lines of code. It is the core tool for most data analysis work in Python.
How long does it take to learn Python for data analysis?
Core skills can be built in about four days of structured, hands-on training. Bodhih recommends four days in person or eight live virtual sessions, followed by a capstone and on-the-job practice. Fluency develops over the following months as analysts automate their own tasks.
Is Python better than Excel for data analysis?
Each has its place: Excel is excellent for quick, visual, ad hoc work, while Python is better for large datasets, complex cleaning, repeatable processes and automation. Many analysts use both, preparing data in Python and sharing final outputs in Excel or Power BI.
Can participants use AI coding assistants during the program?
Yes, in a guided way. We show how to ask an AI assistant to explain or draft code and, crucially, how to test and verify that code. We also cover which data must never be pasted into external tools, in line with your organisation's policy.
What software do participants need?
Participants need Python with Jupyter and the pandas, matplotlib and seaborn libraries, usually installed through a standard distribution, or access to an approved cloud notebook service. We share a setup checklist with your IT team two weeks before the program and run a short connection test, so the first session starts with real analysis on business data, not with installation problems.