Data Scientist Roadmap 2026-27: From Beginner to Job-Ready

01

The plan

Data scientist roadmap at a glance

This is a month-by-month plan for a part-time learner starting from zero. It covers about 9 months at 10–12 hours a week. Every stage gives you something you can show — a notebook, a model, a dashboard or a deployed app.

Month-by-month plan — part-time estimate (10–12 hours a week)
Month	Learn	Practise / Build	Milestone you can show
Month 1	Python programming + Git	Write small Python scripts, push to GitHub, solve basic coding problems	Python repo with clean commits and a README
Month 2	Maths & statistics for data science	Implement basic operations in NumPy; solve probability problems by hand	Notes and a notebook with worked examples
Month 3	SQL and data handling (pandas)	Pull data with SQL, clean it in pandas, answer 5 business questions	Data analysis notebook with SQL + pandas
Month 4	Data visualisation & storytelling	Build Matplotlib/Seaborn charts and a one-page insight summary	Visualisation notebook with a written summary
Month 5–6	Machine learning with scikit-learn	Train regression and classification models, compare metrics	ML project with evaluation and model comparison
Month 6–7	Deep learning (PyTorch or TensorFlow) and NLP basics	Train an image classifier and a text classifier with transfer learning	Deep learning project with accuracy report
Month 8	LLMs & Gen AI for data scientists (concepts, APIs, evaluation)	Build a RAG app over your own documents with an LLM API	LLM-powered app with citations and evaluation notes
Month 8–9	Model deployment basics + MLOps awareness	Wrap a model in FastAPI, containerise with Docker, deploy to a cloud host	Deployed ML service with a live URL and logs
Month 9	Portfolio (4–5 projects), Kaggle-style practice, interviews	Polish 4–5 projects, write READMEs, prepare interview answers	Portfolio with deployed projects and a one-page resume

Time estimate note: 9 months part-time is an estimate for someone studying 10–12 hours a week. A fast-track learner at 20+ hours a week can finish in about 5–6 months. Estimates are not guarantees — your pace depends on prior coding and maths experience.

Visual roadmap: A designer-built SVG timeline of all nine stages, with a downloadable one-page PDF summary, is planned for this page. The text version above and the stage sections below cover the same content in accessible HTML.

02

Stage 1

Python programming + Git

Python is the language of data science, and Git is how every team shares code. This stage makes you comfortable writing Python scripts and pushing them to GitHub.

Topics checklist
Topic	What to cover
Python basics	Variables, data types, loops, conditionals, functions, list comprehensions, error handling
Data structures	Lists, tuples, dictionaries, sets, when to use each
Modules and environments	Importing modules, virtual environments, pip, requirements.txt
Jupyter notebooks	Cells, markdown, running code, exporting notebooks
Git and GitHub	Commits, branches, pull requests, README files, .gitignore

Free official resources: Python documentation (docs.python.org), Jupyter documentation, GitHub Skills for Git.

Mini project: Write a Python script that reads a CSV file, cleans a few columns and saves a summary. Commit it to GitHub with a README.

You are ready for the next stage when: you can write a Python script from scratch, debug it yourself and push it to GitHub without help.

03

Stage 2

Maths & statistics for data science

You do not need to be a mathematician, but you cannot skip the maths that makes ML work. This stage gives you enough intuition to understand what a model is doing and why it fails.

Topics checklist
Topic	What to cover
Linear algebra	Vectors, matrices, matrix multiplication, dot products, eigenvalues — why they matter in ML
Calculus intuition	Derivatives, partial derivatives, gradients, chain rule, gradient descent
Probability	Distributions, conditional probability, Bayes theorem, expectation, variance
Statistics	Descriptive statistics, hypothesis testing, confidence intervals, correlation vs causation
Practical tools	NumPy for linear algebra, SciPy for statistics

Free official resources: Khan Academy (linear algebra, probability, statistics), 3Blue1Brown (Essence of Linear Algebra, Essence of Calculus), MIT OpenCourseWare 18.06.

Mini project: Implement gradient descent for a simple linear regression from scratch using NumPy. Plot the loss curve and compare it with scikit-learn's result.

You are ready for the next stage when: you can explain what a gradient is, what a probability distribution is and why matrix multiplication matters in a neural network.

04

Stage 3

SQL and data handling (pandas)

Most of a data scientist's time goes into data, not models. This stage teaches you how to pull data from a database and clean it in pandas.

Topics checklist
Topic	What to cover
SQL	SELECT, WHERE, JOIN, GROUP BY, subqueries, window functions
pandas	DataFrames, indexing, filtering, groupby, merge, handling missing values
NumPy	Arrays, broadcasting, vectorised operations, basic linear algebra
Data cleaning	Handling missing values, outliers, duplicates, type conversion
Feature engineering	Creating new columns, encoding categorical variables, scaling

Free official resources: pandas documentation, NumPy documentation, SQLBolt, Kaggle Learn (Pandas).

Mini project: Take a public dataset (for example a CSV of sales or survey data), load it into SQLite, query it with SQL, then clean and analyse it in pandas.

You are ready for the next stage when: you can join two tables in SQL, clean a messy dataset in pandas and explain what you did to someone else.

05

Stage 4

Data visualisation & storytelling

A chart that nobody understands is not analysis. This stage teaches you how to choose the right chart and turn it into a short, clear story.

Topics checklist
Topic	What to cover
Matplotlib	Line, bar, scatter, histogram, subplots, styling
Seaborn	Relational, categorical and distribution plots, pair plots
Choosing charts	Matching the chart to the question; when not to use a pie chart
Storytelling	One message per chart, ordering charts, a short written summary
Dashboard basics	Optional: Power BI or Tableau for stakeholder views

Free official resources: Matplotlib documentation, Seaborn documentation, Microsoft Learn (Power BI basics).

Mini project: Take a dataset from Stage 3 and build 5 charts that answer 5 business questions. Write a one-page summary of the insights.

You are ready for the next stage when: a non-technical reader can look at your charts and summary and understand the key finding without you explaining it.

06

Stage 5

Machine learning with scikit-learn

Classical ML is still the right tool for most tabular problems. This stage teaches you the standard algorithms, how to evaluate them properly and how to avoid overfitting.

Topics checklist
Topic	What to cover
Supervised learning	Linear regression, logistic regression, decision trees, random forests, gradient boosting
Unsupervised learning	K-means clustering, hierarchical clustering, PCA
Evaluation	Train/test split, cross-validation, accuracy, precision, recall, F1, ROC-AUC, confusion matrix
Feature engineering	Handling missing values, encoding categorical variables, scaling, feature selection
Overfitting and regularisation	Bias-variance trade-off, L1/L2 regularisation, early stopping
Pipelines	scikit-learn pipelines, column transformers, saving models with joblib

Free official resources: scikit-learn user guide, Kaggle Learn (Intro to Machine Learning, Intermediate Machine Learning).

Mini project: Build a classification model on a public dataset (for example loan default or churn). Compare at least three algorithms and report precision, recall and F1 for each.

You are ready for the next stage when: you can explain why you chose one model over another, and you can interpret a confusion matrix without help.

07

Stage 6

Deep learning (PyTorch or TensorFlow) and NLP basics

Deep learning is what powers modern AI — image recognition, speech, language models. This stage teaches you how to build, train and debug neural networks, plus the NLP basics you need for text data.

Topics checklist
Topic	What to cover
Neural network basics	Perceptrons, activation functions, loss functions, backpropagation, optimisers
PyTorch or TensorFlow	Tensors, autograd, nn.Module or Keras, DataLoader, training loops, GPU basics
CNNs	Convolution, pooling, padding, stride, architectures (ResNet, VGG), transfer learning
Sequence models	RNNs, LSTMs, GRUs, when to use them, their limitations
NLP basics	Tokenisation, embeddings, text classification, sentiment analysis, Hugging Face transformers

Free official resources: PyTorch tutorials, TensorFlow documentation, Hugging Face course, deeplearning.ai (audit for free).

Mini project: Train an image classifier with transfer learning on a public dataset, and a text classifier using a Hugging Face model.

You are ready for the next stage when: you can train a CNN from a pre-trained model, fine-tune a text classifier and debug a training loop that is not converging.

08

Stage 7

LLMs & Gen AI for data scientists (concepts, APIs, evaluation)

Large language models are now part of most AI products. This stage teaches you how to call LLM APIs, build a simple RAG app and evaluate whether it actually works.

Topics checklist
Topic	What to cover
LLM basics	Tokens, context windows, temperature, hallucinations, limitations
LLM APIs	OpenAI, Anthropic, Gemini APIs — request structure, cost, error handling
Prompt engineering	System vs user prompts, few-shot examples, structured JSON output
RAG basics	Embeddings, chunking, vector stores, retrieval, citations
Evaluation	Task success, hallucination checks, cost per query, when RAG helps vs hurts

Free official resources: OpenAI API documentation, Hugging Face course, LangChain documentation (concept level).

Mini project: Build a RAG app over your own PDFs — for example course notes or a small document set. It should answer questions and show which chunk each answer came from.

You are ready for the next stage when: your RAG app answers questions from documents it has never seen, cites its sources and handles a question the documents do not answer.

09

Stage 8

Model deployment basics + MLOps awareness

A model in a notebook is not a data product. This stage teaches you how to wrap a model in an API, containerise it and deploy it to the cloud.

Topics checklist
Topic	What to cover
FastAPI	Building REST endpoints, request/response models, validation, error handling
Docker	Writing a Dockerfile, building images, running containers, docker-compose basics
Cloud deployment	Deploying to AWS, Azure or GCP; managed ML services (SageMaker, Azure ML, Vertex AI)
Model monitoring	Logging predictions, tracking latency, detecting data drift, retraining triggers
MLOps awareness	Experiment tracking, model registry, CI/CD for models, versioning data and models

Free official resources: FastAPI documentation, Docker documentation, AWS SageMaker developer guide, MLflow documentation.

Mini project: Take the model from Stage 5 or 6, wrap it in a FastAPI service, containerise with Docker and deploy it to a cloud host. Add logging so you can see each prediction.

You are ready for the next stage when: you have a live URL that returns predictions, and you can explain how you would detect if the model started failing.

10

Stage 9

Portfolio (4–5 projects), Kaggle-style practice, interviews

The final stage turns learning into a portfolio. Four to five deployed, evaluated projects are what get you interviews — not the number of certificates you collected.

What to build and prepare
Item	What it involves
4–5 portfolio projects	One tabular ML project, one deep learning project, one NLP or RAG app, one visualisation dashboard and one deployed service
Kaggle-style practice	Compete in a beginner-friendly Kaggle competition to practise end-to-end modelling
Resume	One-page resume that leads with projects, not coursework
Interview prep	Python, ML concepts, deep learning, SQL, statistics, project walkthroughs, failure cases

Interview prep: Be ready to walk through your projects end to end — the data, the model choices, the evaluation, what failed and how you fixed it. Expect questions on overfitting, bias-variance, precision vs recall and how you would deploy a model.

You are ready to apply when: you have 4–5 deployed data science projects on GitHub, each with a README, an evaluation report and a live demo link.

11

Portfolio

Projects to build along the way

Each stage produces one project. By the end, these projects are your portfolio — they prove you can build, evaluate and ship data science systems, not just talk about them.

Stage → project → what it proves
Stage	Project	Tools	What it proves
Stage 1	CSV cleaning script	Python, pandas, Git	You can write working Python and use version control
Stage 2	Gradient descent from scratch	NumPy, Matplotlib	You understand the maths behind ML
Stage 3	SQL + pandas analysis	SQLite, pandas	You can pull, clean and analyse data
Stage 4	Visualisation + insight summary	Matplotlib, Seaborn	You can turn data into a clear story
Stage 5	Tabular ML project	scikit-learn, XGBoost	You can train, compare and evaluate models
Stage 6	Image classifier + text classifier	PyTorch or TensorFlow, Hugging Face	You understand deep learning and NLP basics
Stage 7	RAG app over your documents	LLM API, embeddings, vector store	You can build an LLM-powered application with citations
Stage 8	Deployed ML service	FastAPI, Docker, cloud	You can take a model to a live URL
Stage 9	4–5 portfolio projects	All of the above	You can design, build, evaluate and ship data science systems

12

Pace

Fast-track vs part-time plan

The roadmap is the same either way — only the pace changes. Choose the plan that fits your available hours, not the one that looks impressive.

Time estimates — starting from zero
Plan	Weekly effort	Estimated months	Best for
Fast-track	20+ hours a week	5–6 months	Full-time learners, career switchers with free time
Part-time	10–12 hours a week	8–9 months	Working professionals, students with college

Estimate note: These are estimates, not guarantees. Your pace depends on how much Python and maths you already know and how much time you can protect each week.

13

Starting points

Data Scientist roadmap for different backgrounds

01

Fresher (any degree)

Start with Python and maths if you have never coded. Then follow all nine stages. Your portfolio projects matter more than your degree — build the tabular ML project and the RAG app first.

02

Non-IT / commerce background

Spend extra time on Stage 1 (Python) and Stage 2 (maths). Use the visualisation project to build confidence early. A strong portfolio can open doors even without a CS degree.

03

Working professional

Go part-time (10–12 hours a week). Compress Stage 1 if you already write code. Focus on Stages 5–9 and ship three strong projects rather than five unfinished ones.

04

Career-gap returner

Treat the portfolio as your re-entry proof. Build the four to five Stage 9 projects with clear READMEs and evaluation numbers. Add a short write-up of what you learned at each stage.

14

Path choice

Data analyst first or straight to data science?

Both paths are valid. A data analyst first can be a faster route into a job, with data science as the next step.

Decision table — which path fits you
Factor	Data Analyst first	Straight to Data Science
Time to first job	Faster — 5–6 months part-time	Longer — 8–9 months part-time
Core skills	Excel, SQL, Power BI, basic statistics	Python, maths, ML, deep learning, deployment
Maths depth	Light to moderate	Moderate to strong
Best for	People who want a job soon and can continue learning on the job	People with time to build a full portfolio before applying
Next step	Data Science roadmap once you are working	Data Scientist, ML Engineer or Data Analyst roles

15

Avoid these

Common mistakes on this roadmap

Skipping the maths — it catches up with you during deep learning and evaluation.
Collecting certificates instead of shipping projects — employers interview on the portfolio.
Never deploying a model — a data scientist who only works in notebooks is half-finished.
Skipping evaluation — a model without precision, recall and F1 is a guess.
Jumping to LLMs before classical ML — you miss the fundamentals that make LLMs make sense.
Working alone — join a study group or find a mentor to review your projects.

16

After the roadmap

Jobs you can apply for at the end

At the end of this roadmap you can apply for Data Scientist, ML Engineer and Data Analyst roles. Indeed reports an average Data Scientist salary of ₹12,09,022 (428 salaries, updated 14 Aug 2026) and Glassdoor reports an average base of ₹12.0L (Oct 2026).

Data Scientist salary — public sources (fetched 13 Oct 2026)
Source	Figure	Sample size	Date
Indeed — national average	₹12,09,022; Pune ₹13.66L, Mumbai ₹13.64L, Gurgaon ₹13.30L, Chennai ₹12.83L, Bengaluru ₹12.54L	428 salaries	14 Aug 2026
Glassdoor — average base	₹12.0L; range ₹7L–₹16.1L, average total pay ₹13.35L	—	Oct 2026

Note: Salaries vary by city, company, skills and experience. Uncodemy does not guarantee any salary. Uncodemy's own Data Science course page does not carry a verified salary table, so we do not print one here.

17

Guided learning

Follow this roadmap with a trainer

Uncodemy's Data Science program covers the core of this roadmap — Python, statistics, machine learning, deep learning, NLP, SQL, Excel, Tableau and Power BI, with 7 projects. Fee and duration are verified on the course page.

Uncodemy Data Science program
Program	Details
Data Science	Data Science — ₹17,500 + GST, 8–9 months. 150+ hours, 90+ live sessions, 15+ tools, 10 modules, 7 projects. Eligibility: graduates from any stream.

Ready to explore? Book a free counselling session, try a 2-class trial or use the AI-powered career guidance tool.

18

FAQs

Data Scientist roadmap FAQs

Q1 How many months does it take to become a data scientist? +

For a fresher studying 10–12 hours a week, about 8–9 months. The roadmap covers Python and Git (month 1), maths (month 2), SQL and pandas (month 3), visualisation (month 4), ML (months 5–6), deep learning and NLP (months 6–7), LLMs (month 8), deployment (months 8–9) and portfolio (month 9). A fast-track learner at 20+ hours a week can finish in 5–6 months.

Q2 Is maths compulsory for data science? +

Yes, at a working level. You need linear algebra, probability and statistics to understand what a model is doing and why it fails. You do not need to derive everything by hand, but you cannot skip the concepts entirely — especially before deep learning.

Q3 Can I learn data science after B.Com or BA? +

Yes. Uncodemy's Data Science program eligibility is graduates from any stream, and a CS or maths background helps but is not required. B.Com and BA graduates often bring strong business context. The main work is building Python and maths fundamentals early.

Q4 Should I learn TensorFlow or PyTorch? +

Learn PyTorch first. It is widely used in research and increasingly in industry, and its syntax is easier to debug. TensorFlow and Keras are still used, especially in production teams — once you know one, the other is easier to pick up.

Q5 Do I need a master's degree to be a data scientist? +

No. A master's helps for some research-heavy roles, but most entry-level data scientist roles accept graduates with a strong portfolio. Uncodemy's Data Science program eligibility is graduates from any stream. Projects and demonstrable skills matter more than the degree.

Q6 Which data science projects get interviews? +

Four to five projects work best: a tabular ML project with model comparison, a deep learning project with transfer learning, an NLP or RAG app, a visualisation dashboard and a deployed ML service. Each should have a deployed demo, a README and an evaluation report.