Fireblaze Self-Paced Course
Python for Data Science & Analytics: Learn From Zero
This is the Python you need for data work, not Python for its own sake. You learn just enough of the language to be dangerous, then spend most of the course on pandas, visualisation, statistics and a first machine learning model, using real datasets. You finish with a certificate and a graded notebook you can open and explain in an interview.
- 18 structured lessons, start any time
- 1 graded capstone project with written feedback
- Doubt-solving chat support for one month from first login, replies within 1 working day
- Hands-on with pandas, NumPy, Matplotlib, Seaborn and scikit-learn
- Certificate of completion, and access for as long as the platform runs
Self-paced and fully online. No batch timings, no scheduled classes.
Python programming for data science and analytics is the use of Python and its data libraries to load, clean, analyse and visualise data, and to build first predictive models. This self-paced course covers it in 18 lessons: Python fundamentals, NumPy, pandas, data cleaning, Matplotlib and Seaborn, descriptive statistics, hypothesis testing, SQL from Python and an introduction to machine learning with scikit-learn. It is built for beginners, so no programming background is needed. The fee is ₹1,999 inclusive of 18% GST, and it includes one graded project, one month of doubt-solving chat support starting at first login, with replies within 1 working day, a certificate of completion, and access for as long as the platform runs.
What You Can Do Afterwards
Skills That Carry Into Analyst and Data Science Work
Everything in the course is picked because a data role uses it, and the order follows how an analysis is really done.
Write working Python
Use variables, loops, functions and data structures well enough to read other people's analysis code and write your own.
Wrangle data with pandas
Load files, fix missing values and types, filter, group, merge and reshape tables without clicking through a spreadsheet.
Explore and visualise
Plot distributions and relationships with Matplotlib and Seaborn, and spot what is odd in a dataset before modelling it.
Back claims with statistics
Summarise data properly, check correlations, and run basic hypothesis tests while knowing what they do and do not show.
Query databases from Python
Pull data from SQL databases straight into pandas so your analysis starts from the source, not a downloaded copy.
Build a first model
Split data, train a regression or classification model in scikit-learn, and evaluate it honestly.
The Capstone
You Finish With an Analysis You Can Explain, Not Just Code That Runs
Copying a notebook that works is easy. The graded project asks you to choose questions, make decisions about messy data and defend them.
Graded capstone project
Analyse a real dataset end to end in a Jupyter notebook and report what you found
You take a real public dataset, clean it, explore it, test at least one claim and fit a simple model. Then you write up what you found and what you would not trust.
What you build
- A documented data-cleaning step in pandas
- Exploratory analysis with clear visualisations
- At least one statistical test or comparison, with its assumptions stated
- A baseline machine learning model with an honest evaluation
- A notebook written so another person can follow it
How it is graded
- Code and data handling: is the analysis correct and reproducible
- Method: are the choices, tests and metrics appropriate
- Reasoning: a short written report on the findings, the limits and what you would do next
You receive written feedback on the submission. Being able to explain why you made a choice is exactly what an interviewer will probe, so the write-up matters as much as the code.
Curriculum
All 18 Lessons
Ordered the way a data project runs: learn the language, wrangle the data, understand it, then model it.
Python foundations
- Setting up Python and Jupyter, variables and data types
- Control flow and functions
- Lists, tuples, dictionaries and sets
- Files, errors and modules
NumPy and pandas
- NumPy arrays and vectorised operations
- pandas Series and DataFrames
- Cleaning data: missing values, duplicates and types
- Selecting, filtering, groupby and aggregation
- Merging, joining and reshaping data
- Working with dates and time series
Visualisation and statistics
- Plotting with Matplotlib
- Exploratory data analysis with Seaborn
- Descriptive statistics and correlation in Python
- Hypothesis testing basics with SciPy
Applying it
- SQL from Python: reading database tables into pandas
- Introduction to machine learning with scikit-learn
- Evaluating a model: train-test split and metrics
- Capstone workflow: from raw data to a written analysis
Honestly
Why Pay When Python Tutorials Are Free?
A fair question, and worth answering directly rather than pretending free material does not exist.
| Free videos and blog posts | This course | |
|---|---|---|
| Coverage | Excellent on Python itself, scattered on data work | One ordered path from basics to a first model |
| Focus | General-purpose Python, much of it you will not use | Only the parts a data role uses |
| Real data | Often toy datasets | A real dataset in the graded project |
| Your own work reviewed | No | Graded project with written feedback |
| When you get stuck | Comments section, maybe | Doubt chat for a month, answered in 1 working day |
| Proof you did it | Nothing verifiable | Graded project plus a certificate |
If you have the discipline, every library here is documented for free. What is hard to find for free is someone reading your notebook and telling you which of your analytical choices do not hold up. That is what you are paying for.
Fit
Who This Is For
A good fit if you
- Have never programmed, or only dabbled, and want a path into data work
- Are a graduate, analyst or professional moving from Excel to Python
- Want to move toward data analyst or data science roles
- Prefer to learn on real datasets rather than abstract exercises
Not the right starting point if you
- Want general software development, web apps or automation scripting beyond data work
- Are looking for deep learning or advanced machine learning, which this course only introduces
- Want a no-code tool and do not want to write any code
- Want live scheduled classes with an instructor, which our career programs provide instead
You do not need any programming background or a powerful computer. The lessons use free tools: Python, Jupyter and open-source libraries.
Where Next
After Python, Where Does This Lead?
Python is the base for most data science work. These are the usual next steps.
Enrol
One Price, Everything Included
No subscription, no upsell at checkout, and no separate charge for the project review or the support window. The number below is the number you pay.
Payment is processed securely through Razorpay. You get access immediately after payment, and you can start the first lesson the same day.
Course Fee
₹1,999
Inclusive of 18% GST. No hidden charges.
- All 18 lessons, self-paced
- Graded capstone project with written feedback
- Doubt-solving chat support for one month from first login, replies within 1 working day
- Certificate of completion
- Access for as long as the platform runs
- Uses only free, open-source tooling throughout
Questions first? Ask on WhatsApp
FAQ
Frequently Asked Questions
What is Python for data science and analytics?
It is the use of Python and libraries such as NumPy, pandas, Matplotlib, Seaborn and scikit-learn to load, clean, analyse and visualise data and build first predictive models. This course teaches that toolkit from the language basics upward.
Do I need programming experience?
No. The course starts from installing Python and writing your first lines of code. It is designed for people who have never programmed, and moves into data libraries once the language basics are in place.
How much does the course cost?
Rs 1,999, inclusive of 18% GST. That covers all lessons, the graded capstone project with written feedback, and doubt-solving chat support for one month. There is no subscription and nothing additional is charged at checkout.
Which libraries does the course use?
NumPy and pandas for data handling, Matplotlib and Seaborn for visualisation, SciPy for basic statistical tests, and scikit-learn for an introductory machine learning model. All are free and open source.
Does the course cover machine learning?
It introduces it. There is a lesson on building a first model with scikit-learn and one on evaluating it, so you understand the workflow. It is not a full machine learning course, and our career programs go much deeper.
Is this enough to become a data scientist?
It gives you the Python foundation that data science roles build on. A data role usually also needs statistics, SQL, machine learning and project experience. This course covers the first steps of each and does not promise a job.
Should I learn Python or Excel first?
If you have never worked with data, Excel is the quicker way to understand tables and summaries. If you already know you want to move toward data science, or your data is too large or repetitive for a spreadsheet, start with Python. Many learners do both, and we have a self-paced Excel course for that.
Does it cover SQL?
Partly. There is a lesson on reading database tables into pandas from Python, but it is not a full SQL course. If you need SQL in depth, see our SQL course.
Is this course live or self-paced?
Fully self-paced and online. There are no batch timings and no scheduled classes. You start whenever you buy and move at your own speed. If you want live instructor-led teaching, our career programs are the better option.
What is the graded project?
You analyse a real public dataset end to end in a Jupyter notebook: cleaning, exploration, at least one statistical test and a baseline model, plus a short written report on what you found and its limits. You receive written feedback.
How does the doubt support work?
You get access to a doubt-solving chat window for one month, counted from your first login rather than from the date of purchase, so buying early and starting later costs you nothing. Questions are answered within 1 working day. It is intended for course and project questions.
What do I need on my computer?
A laptop or desktop that can run Python and Jupyter. The lessons use free tools only, and no GPU or paid software is needed.
Who is this course not suitable for?
People who want general software engineering or web development, people looking for deep learning, and people who want live scheduled classes. It is also not for anyone who wants a no-code tool.
How do I pay, and when do I get access?
Payment is processed securely through Razorpay. Access is granted immediately after successful payment, so you can begin the first lesson the same day.
Do I get a certificate?
Yes. You receive a certificate of completion from Fireblaze AI School once you finish the course and submit the graded capstone project. The project is the part worth showing in an interview, but the certificate is included.
How long do I have access to the course?
There is no expiry date. You keep access for as long as we run the learning platform, so you can revisit lessons later when you meet a similar problem at work. There is no subscription and no renewal.
Who runs this course?
Fireblaze AI School, an AI education institute in Nagpur founded on 31 October 2017 that has trained more than 20,000 learners. The self-paced courses are the online, India-wide arm of the same teaching approach used in our career programs.
Can I ask questions before buying?
Yes. Message us on WhatsApp using the link on this page and we will answer questions about prerequisites, the project or the support window before you enrol.