# Python for Data Analysis Training > Python for data analysis training that teaches pandas, visualisation and automation to analysts, with hands-on notebooks on business data. Upskill your team. - Source: https://bodhih.com/python-for-data-analysis-training/ - Publisher: Bodhih Training (Bengaluru, India; since 2008) - Last updated: 2026-10-15 - Contact: solutions@bodhih.com · +91 99000 11601 --- Python for data analysis training that gives analysts a faster, more repeatable way to work with data. Starting from spreadsheet thinking, participants learn to load, clean, analyse, visualise and automate with Python and pandas in notebooks, using business datasets from their own industry. ## At a glance - **Duration:** Recommended: 4 days in person, or 8 × 3-hour live virtual sessions - **Who it’s for:** Analysts, MIS, finance and operations professionals comfortable with Excel - **Level:** Intermediate - **Format:** In person · Live virtual · Blended - **Track record:** Since 2008 · 2,000+ companies Python for data analysis training teaches analysts to use Python, pandas and visualisation libraries to import, clean, transform, analyse and chart data in Jupyter notebooks. It suits Excel users in MIS, finance and operations, and covers reproducible workflows, report automation and responsible use of AI coding assistants. Bodhih runs it over 4 days in person or eight 3-hour virtual sessions. - 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](https://bodhih.com/corporate-training-in-bangalore/), [Mumbai](https://bodhih.com/corporate-training-in-mumbai/), [Delhi NCR](https://bodhih.com/corporate-training-in-new-delhi/), [Gurugram](https://bodhih.com/corporate-training-in-gurugram/), [Hyderabad](https://bodhih.com/corporate-training-in-hyderabad/), [Chennai](https://bodhih.com/corporate-training-in-chennai/), [Pune](https://bodhih.com/corporate-training-in-pune/), [Kolkata](https://bodhih.com/corporate-training-in-kolkata/), [Ahmedabad](https://bodhih.com/corporate-training-in-ahmedabad/) and [Jaipur](https://bodhih.com/corporate-training-in-jaipur/); as a live online course; or delivered overseas in [Dubai](https://bodhih.com/corporate-training-in-dubai/), [Singapore](https://bodhih.com/corporate-training-in-singapore/) and across the Middle East, Asia and Africa. Last reviewed 15 Oct 2026 by [Siddharthan VGJ](https://bodhih.com/siddharthan-vgj/), Founder · Bodhih Training, Bengaluru ## 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. Source: [World Economic Forum, Future of Jobs Report 2025](https://www.weforum.org/publications/the-future-of-jobs-report-2025/in-full/3-skills-outlook/) ## 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. ### **Python foundations for analysts**Module 1 · 4 hours - Setting up and using Jupyter notebooks - Data types, lists, dictionaries and loops - Writing simple functions - Reading error messages without panic ### **pandas essentials**Module 2 · 4 hours - Series and DataFrames - Reading CSV and Excel files - Selecting, filtering and sorting - From Excel formulas to pandas equivalents ### **Cleaning and transforming data**Module 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 ### **Analysis and aggregation**Module 4 · 3 hours - groupby, pivot\_table and crosstabs - Descriptive statistics and distributions - Time series resampling and rolling metrics ### **Visualisation**Module 5 · 3 hours - matplotlib and seaborn basics - Choosing charts for comparisons, trends and distributions - Formatting charts for business audiences ### **Automation and good practice**Module 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 ### **Capstone project**Module 7 · 3 hours - Answer a business question from raw data - Peer code review - Present findings and next steps ## Sample training plan: 4-day hands-on design This four-day lab moves analysts who live in Excel into Python, pandas and notebooks, using Excel as the bridge. Participants code from the first hour on familiar business data: regional sales files in inconsistent formats, a collections ledger and plant production logs. They clean and merge messy files, rebuild their favourite pivot tables in pandas, chart trends for a business audience and automate a monthly report that writes formatted Excel output. The capstone answers a business question from raw data with peer code review. 80% hands-on practice · 27 contact hours · Cohort: 10–20 participants, each with a laptop and a working notebook environment · In person · Live virtual ### Day 1: Python foundations for analysts Participants can write basic Python in notebooks, read errors calmly and load a first data set. 1. 09:30–10:00 · Excel confessions (Energiser) 2. 10:00–11:00 · Notebooks and first code (Demo & practice) 3. 11:15–12:30 · Lists, dictionaries and loops (Hands-on lab) 4. 13:15–14:00 · Reading errors without panic (Group challenge) 5. 14:00–15:00 · Write your own functions (Skill drill) 6. 15:15–16:45 · First DataFrame (Hands-on lab) 7. 16:45–17:30 · Day one reflection (Reflection) ### Day 2: pandas essentials and cleaning Participants can clean, transform, merge and reshape messy business data. 1. 09:30–10:00 · Formula to pandas relay (Energiser) 2. 10:00–11:00 · Selecting, filtering and new columns (Hands-on lab) 3. 11:15–12:30 · Missing values, types and dates (Hands-on lab) 4. 13:15–14:45 · Combine regional sales files (Hands-on lab) 5. 14:45–15:30 · Reshaping (Demo & practice) 6. 15:45–16:45 · Clean your own report data (Build sprint) 7. 16:45–17:30 · Day two reflection (Reflection) ### Day 3: Analysis and visualisation Participants can summarise data, analyse trends and chart findings for a business audience. 1. 09:30–10:00 · Guess the chart (Energiser) 2. 10:00–11:00 · groupby and pivot\_table (Hands-on lab) 3. 11:15–12:00 · Describing data honestly (Concept burst) 4. 12:00–13:00 · Time series and rolling metrics (Hands-on lab) 5. 13:45–15:00 · Charts for the business (Hands-on lab) 6. 15:15–16:30 · Analyse your own report (Build sprint) 7. 16:30–17:30 · Day three reflection (Reflection) ### Day 4: Automation, good practice and capstone Participants can automate a recurring report, use AI coding help safely and answer a business question end to end. 1. 09:30–10:00 · The scariest workbook (Energiser) 2. 10:00–11:15 · Script the monthly report (Hands-on lab) 3. 11:30–12:15 · AI coding assistants, checked (Case clinic) 4. 12:15–13:00 · Tidy notebooks (Skill drill) 5. 13:45–15:15 · Capstone: answer the business question (Build sprint) 6. 15:30–16:30 · Peer code review and readout (Presentation) 7. 16:30–17:30 · My 90-day automation plan (Action planning) Recommended: 4 days in person (09:30–17:30), or 8 × 3-hour live virtual sessions, with a code clinic and script review at 30, 60 and 90 days. A sample design: every Bodhih program is customised after a short needs analysis. ### How long is Python for data analysis training? The recommended design is four days in person, 09:30 to 17:30, or eight live virtual sessions of three hours. Participants code on business data from the first hour. A code clinic and script reviews at 30, 60 and 90 days help them apply it to live work. ### What activities are included in Python for data analysis training? Participants translate Excel formulas into pandas, fix broken code in a team race, combine five inconsistent regional sales files, rebuild their own pivot tables, chart production trends, script a monthly report that writes formatted Excel output and answer a business question from raw data in a capstone. ## 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. AA Pair this program with [AssessAll](https://assessall.com), 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](https://bodhih.com/live-virtual-classes/) 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](https://bodhih.com/power-bi-training/). ### 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.