TTFA Academy · A fellowship for people at work
For analyst working in Excel and SQLPython Data Analyst
Python for the analysis you already do: cleaning, exploring and charting data you would otherwise fight in Excel. From analyst working in Excel and SQL to Python Data Analyst, taught by people who do the job.
Designed for AI-native analyst roles: the Generative AI for Exploratory Analysis module teaches prompting for pandas, explaining results with an assistant, and checking every line it writes.
Curriculum updated
EDA with Python is a live online program for the analyst working in Excel and SQL: the next step is being seen as a Python Data Analyst. Python for the analysis you already do: cleaning, exploring and charting data you would otherwise fight in Excel. Classes run over 12–16 weeks on weeknight evenings and weekends in IST and are recorded the same day, so a late shift doesn't cost you the week. You work in Python, Pandas, NumPy, Matplotlib, Seaborn and Plotly on projects modelled on the mentors' production work, each reviewed 1:1, with doubt-solving on the LMS between classes. A counsellor confirms the batch, the schedule and the fee on one call; nothing is charged on the call.
Why this program
Results, your career, the part of the job you do by hand today, and who teaches it.
Results-focused
Cleaning, exploring and charting real datasets with pandas, Matplotlib and Seaborn, and an analysis note a manager can read.
Career-focused
The next tool for an analyst who has hit Excel's limits. The programme covers Python the way analysts use it, not the way developers do.
Pain-point focused
Too many rows, too many sources. You learn the pandas that handles them and the charts that explain them, on your own kind of data.
Premium and professional
Mentors who build this in production, projects modelled on their own, 1:1 review of every build, recordings the same day. For analysts who want to be taken seriously, not for anyone collecting certificates.
Who this is for
You already do part of this job. The program closes the gap between where you are and being seen as a Python Data Analyst.
Probably not for you, yet
If you haven't worked in the domain at all, a counsellor will say so on the call and point you to a better starting program. If you want a certificate more than a portfolio, this isn't it.
What you'll build
Each project is modelled on the mentors' production work and reviewed 1:1. Together they are your portfolio.
Project 1
Modelled on the mentor's production work, with the messy parts left in, reviewed 1:1.
Project 2
Brief, build, review: the way a manager would hand it to you, with the checks that stop it breaking next month.
Project 3
Published, documented and defended, with the notes a senior would give you.
Curriculum
12 to 16 weeks, depending on your pace. Each module ends with an assignment a mentor reviews.
4 modules · 30 topics · pick a module to see what it covers
Module 1 of 42 new
Equip learners with strong Python and EDA foundations to analyze, clean, visualize, and prepare data for analytics and machine learning.
Python Basics
Why Python for Data Analytics · Python syntax, keywords, and naming conventions · Comments, documentation strings, and indentation
Variables, Data Types & Operators
Variables and memory concepts · Numeric, string, and boolean data types · Operators and type casting · Python input and output operations
Control Flow & Functions
Conditional statements and loops · Built-in and user-defined functions · Function arguments (positional, default, keyword) · Lambda functions and functional programming concepts
Modules & Libraries
Using Python modules and packages · Introduction to commonly used libraries
Core Data Structures
Lists, tuples, sets, and dictionaries · Indexing, slicing, and list comprehensions · String operations and manipulation
File Handling & Exception Management
Reading and writing files · Error and exception handling techniques · Working with regular expressions
Data Manipulation & Analysis
Data grouping and aggregation · Sorting, filtering, and joining datasets · Handling missing values and outliers
pandas 3 EssentialsNew
Copy-on-Write, and why chained assignment no longer works · The string data type
SQL from PythonNew
Querying a database from a notebook · Loading query results into a DataFrame
Data Visualization & EDA Libraries
Data visualization concepts · Using NumPy, Pandas, Matplotlib, and Seaborn for analysis and visualization
Tools
You use each one on the kind of data your mentors handle at work, not on a toy example.
Read, clean and reshape data in Python the way an analyst does, without a software-engineering detour.
DataFrames, joins, grouping and vectorised calculations on files you would otherwise fight in Excel.
Profile a dataset, find the gaps and outliers, and form the questions worth answering before any model.
Charts that answer a question, in Matplotlib, Seaborn and Plotly, from a quick look to a shareable figure.
Distributions, central tendency and spread, read correctly and explained plainly.
Present a finding and its caveats so a manager can act on it.
TTFA Academy is an independent training provider. It is not affiliated with, endorsed by or a partner of Microsoft, Salesforce or Alteryx. Microsoft, Power BI, Excel and Fabric are trademarks of the Microsoft group of companies. Tableau is a trademark of Salesforce, Inc. Alteryx is a trademark of Alteryx, Inc. Names are used only to identify the tools taught.
LMS EXPERIENCE
Learn at your pace, practice through real-world projects, build your skills, earn certifications, and discover opportunities that match your potential.
Complete industry-relevant courses designed to build practical skills and prepare you for real-world applications.
Put your learning into practice with assignments and hands-on projects designed to build practical skills and help you grow with confidence.
Practise real-world tasks with interactive simulators that help you apply your knowledge, sharpen your skills, and prepare for the workplace.
Upload and enhance your resume, then discover job and freelance opportunities matched to your skills, experience, and career goals.
Complete projects, build your skills, and unlock certifications at each level as you progress through your learning journey.
Your mentors
Names, employers and photos appear only with their written permission.
Jitendra Singh
EDA with Python
Jitendra Singh
Lead Instructor
Jitendra leads instruction at TTFA across the analytics stack: Alteryx workflows, Tableau and Power BI dashboards, SQL and Python on real company data, and the GenAI layer on top of them, from agents and MCP to RAG. He teaches the job the way it is done at work: real data, in the order you meet it, with the mistakes left in.
Arvind Tiwary
EDA with Python
Arvind Tiwary
Senior analytics consultant
Arvind trains the analyst's working stack: Python and Alteryx for preparing and automating data, SQL for querying it, and Excel for the models and reports that run on it. He teaches the consultant's habits as well: scoping the work with the person who will use it, and handing it over so it survives without you.
Sarvdeep Singh
EDA with Python
Sarvdeep Singh
Data & BI mentor
Sarvdeep mentors across the Data & BI programs: Power BI dashboards, SQL for the questions behind them, and Excel for the analysis most teams still start in, with the reporting discipline that makes an analyst's numbers trusted on a Monday morning.
How the weeks run
A counsellor confirms the exact days and times for your batch on the call.
Two evenings a week with the mentor, on the work of the week.
Project time with the mentor in the room: build, get stuck, get unstuck.
Every class recorded the same day; 1:1 doubt-solving whenever you're stuck.
Your projects reviewed like production work, with notes you can act on.
Data & BI
Programs next to this one. A counsellor will tell you which fits what you do today.
MIS, ops or finance reporting in Excel
Data Analyst
Data or MIS analyst
BI Developer
Analyst doing manual reporting
BI & Automation Consultant
Questions
Anything else: ask on the call.
Yes. The program assumes you know your work, not the tool. If you've never worked in the domain, a counsellor will point you to a better start.
Every class is recorded the same day, and 1:1 slots exist for exactly this. Most people who fall behind catch up in a week.
There is career support: mock interviews, profile reviews and referrals where we have them. Nobody honest can promise you a role; your projects make the case for you.
Both. EDA with Python is VILT (virtual instructor-led training): live online classes in the evenings and on weekends, in IST, recorded the same day so you can rewatch them. It runs 12–16 weeks, depending on your pace.
Designed for AI-native analyst roles: the Generative AI for Exploratory Analysis module teaches prompting for pandas, explaining results with an assistant, and checking every line it writes.
Python, Pandas, NumPy, Matplotlib, Seaborn and Plotly, the way they are used at work, on projects modelled on the mentors' own. The curriculum section lists every module and topic.
Fees are not published on the site. A counsellor confirms the fee, GST and the instalment options in writing after one call; nothing is charged on the call.
A counsellor looks at what you do today and tells you, straight, whether EDA with Python is the right next step.
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