Brolly Academy | Data Science
Data Scientist Course Syllabus
Module-wise Data Science Syllabus (2026)
A complete, module-wise data scientist course syllabus for 2026. See every data science subject, the tools you will use, the projects you should build, a 6-month learning plan, eligibility rules and a checklist to compare courses before you join.
The complete data scientist course syllabus: every module and subject, tools, projects, a 6-month learning plan, eligibility and how to choose a course.
By Brolly Academy Data Science Team · Brolly Academy · Updated 21 October 2026
Table of Contents
Quick answer
A good data scientist course syllabus takes you from Python and statistics to machine learning, deep learning and deployment, and ends with real projects. In 2026 a complete syllabus usually has these parts: Python programming, statistics and probability, maths for machine learning, SQL, data cleaning and exploratory data analysis (EDA), data visualization with Power BI or Tableau, machine learning, deep learning, natural language processing (NLP), basics of generative AI and large language models (LLMs), big data and cloud basics, MLOps and model deployment, and a capstone project.
- Core subjects: Python, statistics, SQL, machine learning
- Advanced subjects: deep learning, NLP, GenAI, big data, MLOps
- Typical length: 4 to 6 months for a job-focused certificate course; 3 to 4 years for a BSc or BTech degree
- Eligibility: most certificate courses accept any graduate or final-year student; basic school maths helps
Key takeaways
- Python, statistics and SQL come first. Every later module depends on them.
- Machine learning is the heart of the syllabus. Spend the most time on it.
- A modern syllabus includes GenAI and deployment. If a course stops at machine learning theory, it is out of date.
- Projects matter as much as topics. Look for at least one end-to-end capstone project.
- You do not need a coding background to start. You need steady practice, about 2 to 3 hours a day.
1. What a data science course syllabus covers
Short answer: A data science syllabus covers four layers: foundations (Python, statistics, maths, SQL), data handling (cleaning, EDA, visualization), modelling (machine learning, deep learning, NLP, GenAI) and production (big data, cloud, MLOps, projects). A strong course moves through these layers in order.
Data science is the work of collecting data, cleaning it, finding patterns in it and using those patterns to make decisions or predictions. A data scientist uses programming, statistics and business sense together. That is why the syllabus looks wide. It is not one subject. It is a set of connected skills.
Think of the syllabus as a pipeline. First you learn to read and write code. Then you learn to pull data out of databases and clean it. Then you learn to understand it with statistics and charts. After that you learn to build models that predict things. Finally, you learn to put those models into an app or a dashboard that other people can use.
Data science syllabus at a glance
Not every course teaches every module to the same depth. A 4-month course may only introduce big data and GenAI. A master's degree may go deep into maths and research. The order, though, stays the same almost everywhere.
How the syllabus follows the data science lifecycle
Every project you do at work follows roughly the same steps. A good syllabus is built around them:
- Define the problem. What business question are you answering?
- Collect data. From databases (SQL), files, APIs or web scraping.
- Clean and prepare data. Fix missing values, wrong types and duplicates.
- Explore the data. Use statistics and charts to find patterns.
- Build models. Train machine learning or deep learning models.
- Evaluate. Check the model with the right metric on unseen data.
- Deploy and communicate. Share results in a dashboard, report or app, and monitor the model over time.
When you read any course syllabus, map each module to one of these steps. If a step is missing, the course has a gap.
2. Module-by-module data scientist course syllabus
Short answer: Below is the full data scientist syllabus, module by module. For each module you get the topics, the tools and what you should be able to do when you finish. Use it as a study plan or as a checklist to compare courses.
This is the most detailed part of the guide. If you are a beginner, do not worry about the length. You will learn these modules one at a time, and each one builds on the one before.
Module 1: Introduction to data science
This short module sets the scene. You learn what data science is, how it grew out of statistics and computing, and where it is used.
Topics:
- What data science is and why companies use it
- Data science vs data analytics vs machine learning vs AI
- The data science lifecycle from problem to deployment
- Job roles: data analyst, data scientist, machine learning engineer, data engineer
- Uses in banking, healthcare, e-commerce, telecom, agriculture and government
- Ethics: bias, fairness, privacy and responsible use of data
Tools: none yet. This is a concept module.
After this module you can: explain what a data scientist does, describe the steps of a project and tell the difference between related roles.
A quick way to remember the differences: data analytics mainly explains what happened in the past. Data science also predicts what will happen and builds systems that act on it. Machine learning is the set of methods that lets computers learn patterns from data. AI is the wider field that includes machine learning.
Module 2: Python programming for data science
Python is the main language of data science. Its syntax is simple, and it has free libraries for almost every task.
Topics:
- Installing Python, Jupyter Notebook and using Google Colab
- Variables, data types and operators
- Strings, lists, tuples, sets and dictionaries
- Conditions (if, elif, else) and loops (for, while)
- Functions, lambda functions and modules
- File handling: reading and writing CSV, text and JSON files
- Exception handling with try and except
- Basics of object-oriented programming (classes and objects)
- NumPy arrays: creation, indexing, slicing, broadcasting
- pandas Series and DataFrames: loading, filtering, grouping and merging data
Tools: Python 3, Jupyter Notebook, Google Colab, VS Code, NumPy, pandas.
After this module you can: write small Python programs, load a CSV file into pandas, filter and group data, and save the results.
Some courses also offer R as an optional language. R is strong in statistics and has the popular ggplot2 charting package. Most job postings in India ask for Python, so learn Python first and pick up R only if your target role needs it. The official Python tutorial is a free, reliable reference while you practise.
Module 3: Statistics and probability
Statistics tells you whether a pattern in data is real or just chance. Interviewers test this subject often, so do not skip it.
Topics:
- Types of data: numerical, categorical, ordinal
- Descriptive statistics: mean, median, mode, range, variance, standard deviation
- Shape of data: skewness and kurtosis
- Percentiles, quartiles and box plots
- Probability basics, conditional probability and Bayes' theorem
- Probability distributions: normal, binomial, Poisson, uniform
- Sampling methods and the central limit theorem
- Confidence intervals
- Hypothesis testing: null and alternative hypothesis, p-value, Type I and Type II errors
- Common tests: z-test, t-test, chi-square test, ANOVA
- Correlation vs causation
- A/B testing basics
Tools: Python (SciPy, statsmodels), Excel for quick checks.
After this module you can: summarise any dataset, test whether two groups are really different and design a simple A/B test.
Here is a simple example. An online store in India changes its checkout button colour. Sales go up by a small amount the next week. Statistics helps you answer the real question: is that rise big enough to trust, or could it be normal week-to-week noise? That is hypothesis testing in action.
Module 4: Maths for machine learning
You do not need to be a maths topper. You do need to understand the ideas behind the algorithms, so that you can tune them and explain them.
Topics:
- Linear algebra: vectors, matrices, matrix multiplication, dot product
- Eigenvalues and eigenvectors (used in PCA)
- Matrix factorisation basics
- Calculus basics: derivatives, partial derivatives, the chain rule
- Optimisation: what a loss function is and how gradient descent works
- Basic probability for models: likelihood
Tools: NumPy for matrix operations.
After this module you can: explain how a model "learns" by reducing error step by step, and why scaling your features matters.
If maths worries you, here is the good news. Libraries do the calculations for you. Your job is to understand what is happening. Learn the intuition first, then the formulas.
Module 5: SQL and databases
Most company data lives in databases. SQL (Structured Query Language) is how you get it out. Many data science interviews in India start with SQL questions.
Topics:
- Relational databases, tables, primary and foreign keys
- SELECT, WHERE, ORDER BY, LIMIT
- Aggregate functions: COUNT, SUM, AVG, MIN, MAX with GROUP BY and HAVING
- Joins: inner, left, right, full and self join
- Subqueries and common table expressions (CTEs)
- Window functions: ROW_NUMBER, RANK, running totals
- Data modelling basics: normalisation, star schema
- Connecting Python to a database and loading query results into pandas
Tools: MySQL or PostgreSQL, SQLite, a SQL client such as MySQL Workbench or DBeaver.
After this module you can: write queries that join several tables, calculate monthly totals and rankings, and pull the result into Python for analysis.
Module 6: Data wrangling, preprocessing and EDA
Real data is messy. Cleaning and exploring it often takes most of the time in a project. This module teaches you to do it well.
Topics:
- Collecting data from files, databases, APIs and web scraping
- Handling missing values: dropping, filling with mean, median or mode, or flagging
- Removing duplicates and fixing wrong data types
- Detecting and treating outliers (IQR method, z-scores)
- Encoding categorical variables: one-hot encoding, label and ordinal encoding
- Feature scaling: normalisation and standardisation
- Feature engineering: creating new columns from dates, text and ratios
- Feature selection: variance threshold, correlation, recursive feature elimination
- Dimensionality reduction: PCA, with t-SNE for visual exploration
- Univariate, bivariate and multivariate analysis
- Writing clear EDA findings
Tools: pandas, NumPy, Matplotlib, Seaborn, scikit-learn preprocessing tools.
After this module you can: take a raw dataset, clean it, find the main patterns and prepare it for a model without leaking test data into training. The pandas user guide covers every cleaning function you will use here.
Module 7: Data visualization and BI
A model is useless if nobody understands the result. Visualization helps you find patterns and explain them to managers who do not read code.
Topics:
- Choosing the right chart: bar, line, histogram, scatter, box, heatmap
- Matplotlib and Seaborn for static charts
- Plotly for interactive charts
- Dashboard design: layout, filters, key numbers
- Power BI: loading data, Power Query, data modelling, DAX basics, reports
- Tableau basics as an alternative BI tool
- Small web apps with Streamlit or Dash
- Data storytelling: one chart, one message
Tools: Matplotlib, Seaborn, Plotly, Power BI, Tableau, Streamlit.
After this module you can: build a dashboard that a sales or operations manager can use, and present your findings in a short, clear story.
Module 8: Machine learning
This is the core of the data scientist course syllabus. Plan to spend the most time here, and use Google's free Machine Learning Crash Course for extra practice.
Topics:
- What machine learning is: supervised, unsupervised and reinforcement learning
- Train-test split and cross-validation
- Regression: linear, multiple and polynomial regression; Ridge and Lasso
- Classification: logistic regression, k-nearest neighbours (KNN), Naive Bayes, support vector machines (SVM)
- Tree models: decision trees, random forests
- Boosting: gradient boosting, XGBoost, LightGBM
- Clustering: k-means, hierarchical clustering, DBSCAN
- Association rules: Apriori for market basket analysis
- Anomaly detection basics
- Evaluation metrics: MAE, RMSE, R² for regression; accuracy, precision, recall, F1-score, ROC-AUC and the confusion matrix for classification
- Overfitting, underfitting and the bias-variance trade-off
- Hyperparameter tuning: grid search and random search
- Handling imbalanced data
- Model explainability: feature importance, SHAP basics
- Time series basics: trend, seasonality, moving averages, ARIMA and SARIMA
Tools: scikit-learn, XGBoost, LightGBM, statsmodels.
After this module you can: build, compare and tune models for prediction, grouping and forecasting, and pick the right metric for each problem.
A typical machine learning workflow in scikit-learn looks like this. Notice that scaling happens inside a pipeline, so the test data never leaks into training:
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y)
model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
model.fit(X_train, y_train)
print(classification_report(y_test, model.predict(X_test)))
The scikit-learn user guide explains every algorithm in this module with examples. Keep it open while you study.
Module 9: Deep learning
Deep learning uses neural networks with many layers. It works best on images, audio, text and very large datasets.
Topics:
- Neurons, layers, weights and biases
- Activation functions: ReLU, sigmoid, tanh, softmax
- Forward propagation, loss functions and backpropagation
- Optimisers: SGD, Adam; learning rate
- Avoiding overfitting: dropout, early stopping, regularisation
- Artificial neural networks (ANN) for tabular data
- Convolutional neural networks (CNN) for images
- Recurrent neural networks (RNN) and LSTM for sequences and time series
- Transfer learning with pre-trained models
- Autoencoders for compression and anomaly detection
- Introduction to the transformer architecture
Tools: TensorFlow and Keras, or PyTorch; Google Colab or Kaggle notebooks for free GPU time.
After this module you can: train a neural network, build an image classifier with transfer learning and know when deep learning is better than classic machine learning.
Module 10: Natural language processing (NLP)
NLP teaches computers to work with human language. In India this includes customer reviews, support chats and text in many languages.
Topics:
- Text cleaning: lowercasing, removing punctuation and stop words
- Tokenization, stemming and lemmatization
- Bag of words and TF-IDF
- Word embeddings: Word2Vec, GloVe
- Text classification and sentiment analysis
- Named entity recognition (NER)
- Topic modelling basics
- Transformer models such as BERT for classification
- Simple chatbots
Tools: NLTK, spaCy, scikit-learn, Hugging Face Transformers.
After this module you can: classify product reviews as positive or negative, pull names and places out of text and fine-tune a small pre-trained language model.
Module 11: Generative AI and LLM basics
Generative AI is now part of daily work for many data teams. A 2026 syllabus should at least introduce it. You do not need to build an LLM from scratch. You need to use one well and safely.
Topics:
- What large language models are and how they generate text
- Prompt engineering: clear instructions, examples, output formats
- Calling an LLM through an API from Python
- Embeddings and vector databases
- Retrieval-augmented generation (RAG): answering questions from your own documents
- Using LLMs to speed up data cleaning, SQL writing and analysis
- Limits: wrong answers (hallucination), cost, privacy and bias
- Responsible AI: checking outputs, protecting personal data
Tools: an LLM API, Hugging Face, LangChain or LlamaIndex, a vector database such as FAISS or Chroma.
After this module you can: build a simple RAG chatbot over a set of PDFs and explain where LLMs help and where they fail.
Module 12: Big data and cloud basics
Some datasets are too big for one laptop. Companies process them on clusters and in the cloud. Most data scientist roles need you to understand the basics, even if a data engineer does the heavy work.
Topics:
- What big data is: volume, velocity and variety
- Hadoop and HDFS overview
- Apache Spark and PySpark DataFrames
- Working in Databricks notebooks
- Cloud basics: storage, compute and managed machine learning services
- Overview of AWS, Microsoft Azure and Google Cloud data and AI services
Tools: PySpark, Databricks, one cloud platform (AWS, Azure or Google Cloud).
After this module you can: run a PySpark job on a large dataset and describe how a cloud platform stores data and trains models.
Module 13: MLOps and model deployment
A model in a notebook helps nobody. Deployment puts it in front of real users. MLOps keeps it working over time.
Topics:
- Saving and loading models with joblib or pickle
- Building a prediction API with Flask or FastAPI
- Simple web apps with Streamlit or Gradio
- Git and GitHub for version control
- Virtual environments and requirements files
- Docker basics: packaging your app
- CI/CD ideas for machine learning pipelines
- Experiment tracking with MLflow
- Monitoring: data drift, model drift and retraining
Tools: Git, GitHub, FastAPI or Flask, Streamlit, Docker, MLflow.
After this module you can: turn a trained model into a working web app or API and explain how you would keep it accurate.
Module 14: Capstone and real-world projects
The capstone ties everything together. You pick a real problem and solve it from start to finish, just like you would at work.
Topics:
- Choosing a problem with real business value
- Finding and cleaning data
- EDA, modelling and evaluation
- Deployment as a dashboard or app
- Writing a clear README and project report
- Presenting results to a non-technical audience
Tools: everything from earlier modules.
After this module you can: show recruiters a complete project, explain every decision you made and answer follow-up questions with confidence.
3. Data science subjects list
Short answer: The main data science subjects are programming (Python), statistics and probability, mathematics (linear algebra and calculus), databases and SQL, data wrangling, data visualization, machine learning, deep learning, NLP, big data and cloud, MLOps, and data ethics. The first four are foundation subjects; the rest are applied subjects.
When people search for "data science subjects" or "data science course subjects", they usually want a short list they can check against a course or degree. Here it is.
Data ethics deserves a special mention. In India, the Digital Personal Data Protection Act, 2023 sets rules on how organisations collect and use personal data. You do not need to be a lawyer, but you should know the basics: collect only what you need, protect it, and remove names and IDs when you can.
4. Data science syllabus by programme type
Short answer: A certificate or job-focused course is short and practical, built around tools and projects. A BSc, BTech or MSc in data science covers the same core subjects but adds deeper maths, computer science theory, electives and research. Exact subjects differ by university, so always read the official syllabus of the college you are considering.
You will find data science taught in several formats in India. The core subjects overlap, but the depth, length and focus change a lot.
Certificate course syllabus
A certificate course skips most theory that is not needed for a job. It focuses on the modules in section 2: Python, statistics, SQL, EDA, Power BI or Tableau, machine learning and an introduction to deep learning and NLP. A good one adds projects, resume help and mock interviews. This is the fastest route if you already have a degree.
BSc data science syllabus (general pattern)
A BSc programme usually spreads the subjects across six semesters. Early semesters cover programming, discrete maths, calculus, linear algebra, probability and statistics, and database systems. Middle semesters add data structures, data visualization, machine learning and big data. Final semesters often include deep learning, NLP or other electives, and a project or internship. Many universities also include language and general courses.
BTech data science syllabus (general pattern)
A BTech in CSE with data science, or in AI and data science, includes the full engineering core: programming, data structures and algorithms, operating systems, computer networks and database systems. On top of that you study statistics, machine learning, deep learning, big data analytics and cloud computing. Expect labs, mini projects and a final-year major project.
MSc data science syllabus (general pattern)
An MSc goes deeper into the theory. Expect advanced statistical inference, optimisation, machine learning theory, deep learning, and often Bayesian methods or time series. Most programmes end with a dissertation or industry project. It suits you if you want research, analytics leadership or roles that need strong maths.
Whatever programme you pick, check that it has hands-on projects. A degree gives you depth. Projects show employers you can apply it.
5. Month-by-month learning plan (6 months)
Short answer: In six months you can go from zero to job-ready if you study about 2 to 3 hours a day. Spend month 1 on Python, month 2 on statistics and SQL, month 3 on EDA and visualization, month 4 on machine learning, month 5 on deep learning, NLP and GenAI, and month 6 on deployment, projects and interviews.
This plan assumes you are a beginner with no coding background. If you already know Python, you can compress the first month. If you are working full time, stretch it to 8 or 9 months. Steady daily practice matters more than long weekend sessions.
A simple weekly routine
- Weekdays: 1 hour learning a new topic, 1 to 2 hours practising with code
- Saturday: work on your current project
- Sunday: revise the week, solve interview questions, update GitHub
If you want to know which skills employers check first, our guide to data science skills explains them in the order freshers usually learn them.
How much maths should you study each month?
Do not try to finish all the maths first. Learn it alongside the modules that need it. Study descriptive statistics and probability in month 2. Learn linear algebra and gradient descent just before machine learning in month 4. Revise the chain rule and backpropagation before deep learning in month 5. This "just in time" approach keeps maths connected to real use, which makes it far easier to remember.
6. Tools you will learn
Short answer: A data scientist course teaches Python, Jupyter, pandas, NumPy, Matplotlib, Seaborn, scikit-learn, SQL, Power BI or Tableau, TensorFlow or PyTorch, Hugging Face, PySpark, Git and a deployment tool such as Streamlit or FastAPI. Almost all of them are free to use.
You do not need to master every tool. Learn the core tools deeply and know what the others do. Most freshers' interviews focus on Python, SQL, pandas and scikit-learn.
For a fuller explanation of what each tool does and when you need it, see our data science tool list.
7. Projects included in a good syllabus
Short answer: A good syllabus includes 4 to 6 projects that grow in difficulty: one or two EDA projects, two or three machine learning projects, one NLP or deep learning project and one deployed capstone. Each project should use real data, answer a clear question and end with a result you can explain.
Projects turn a syllabus into proof of skill. When a recruiter opens your GitHub, they look for real data, clean code, honest evaluation and a clear write-up. Here are the kinds of projects a strong course includes, mapped to modules.
What a capstone project should include
Your capstone is the project you will talk about most in interviews. Make sure it covers:
- A real problem. For example, predicting loan defaults for a lender or forecasting crop demand from public data.
- Real data. Public sources such as Kaggle, the UCI Machine Learning Repository or data.gov.in work well.
- Clean EDA. Show how you fixed missing values, outliers and wrong types.
- A baseline and a better model. Compare them with the right metric.
- Deployment. A simple app or dashboard that anyone can try.
- A clear README. Problem, data, approach, results and how to run it.
- A short presentation. Five slides that a manager could follow.
Need ideas with datasets and code? Our list of data science projects with source code has project ideas from beginner to advanced level.
Get the full Data Science course syllabus
Brolly Academy's Data Science Course covers Python, statistics, SQL, Power BI, machine learning, deep learning and NLP across 20 modules, with capstone projects and placement support. Book a free demo class to see the syllabus and batch timings.
8. Eligibility and prerequisites
Short answer: For most certificate courses, any graduate or final-year student can join, from any stream. You do not need prior coding knowledge. Class 10 and 12 level maths is enough to start; you will learn the rest during the course. Degree programmes (BSc, BTech, MSc) have their own eligibility rules, usually set by the university.
Data science attracts people from many backgrounds: BTech, BSc, BCom, BCA, MBA and even arts graduates. That is possible because the syllabus starts from the basics.
Eligibility by programme
Always confirm the exact rules on the official admission page of the university or institute. Criteria change from year to year.
Prerequisites that help
You can start without them, but these make the journey smoother:
- Basic maths: percentages, averages, simple algebra and graphs
- Logical thinking: breaking a problem into small steps
- Computer basics: installing software, managing files, using a browser
- Excel basics: sorting, filtering and simple formulas
- English reading: most documentation and error messages are in English
- Curiosity: the habit of asking "why did this happen?"
Data science syllabus for freshers and career switchers
If you are a fresher, focus on Python, SQL, statistics, machine learning and three or four good projects. These cover most entry-level interview rounds. If you are switching careers, use your past domain. A banking professional can build fraud or loan-default projects. A marketing professional can build churn or segmentation projects. Domain knowledge plus data skills is a strong combination. Pick projects that match the roles you want to move into.
9. How to choose a course by its syllabus
Short answer: Choose a course whose syllabus covers Python, statistics, SQL, machine learning and at least an introduction to deep learning, NLP and GenAI; includes real projects and a capstone; teaches deployment; and offers live doubt-clearing and interview preparation. Ask for the full module list and a demo class before you pay.
Many course pages list impressive topics. The real test is depth, practice and support. Use this checklist when you compare courses.
Syllabus checklist
- Foundations are complete. Python, statistics, probability and SQL each have their own module.
- Machine learning is deep, not just a list. Look for evaluation metrics, cross-validation, tuning and imbalanced data.
- Visualization includes a BI tool. Power BI or Tableau is common in Indian job postings for analyst and data science roles.
- Modern topics are present. At least an introduction to deep learning, NLP and GenAI or LLMs.
- Deployment is taught. Streamlit, Flask or FastAPI, plus Git and GitHub.
- Projects use real data. Not only the same toy dataset in every class.
- There is a capstone. An end-to-end project you build yourself, with feedback.
- The trainer has real experience. Ask about the trainer's industry background.
- You get practice and support. Assignments, doubt sessions and recorded classes for revision.
- Career help is built in. Resume building, mock interviews and placement assistance.
Red flags in a data science syllabus
- Only theory and slides, with no coding assignments
- No SQL module
- No mention of model evaluation or overfitting
- Projects that are copied notebooks with no original work
- Tools from many years ago and nothing about GenAI or deployment
- Promises of a guaranteed job or a fixed salary
Questions to ask before you join
- Can I see the full, module-wise syllabus?
- How many hours of live training and how many hours of practice?
- How many projects will I complete, and are they reviewed?
- Can I attend a free demo class first?
- What placement support do you give, and for how long?
10. How Brolly Academy helps
- Our Data Science Course in Hyderabad is a 4-month programme with 20 modules covering Python, statistics and probability, data cleaning, pandas and NumPy, data visualization, SQL and advanced SQL, Power BI, supervised and unsupervised machine learning, model evaluation, neural networks (ANN, CNN, RNN) and NLP.
- You work with Python, Jupyter, scikit-learn, Matplotlib, Seaborn, Plotly, SQL, Power BI and Tableau, with an introduction to TensorFlow, Keras, Hadoop and Spark.
- Capstone projects include predictive sales analytics, customer segmentation, a financial risk dashboard, big data processing with Hadoop and Spark, BI reporting and a recommendation engine.
- Choose classroom training in Hyderabad or live online batches, and start with a free demo class.
- Get 100% placement assistance with resume building, mock interviews and access to 75+ hiring partners.
Frequently asked questions
What is the syllabus for a data scientist course?
A data scientist course syllabus covers Python, statistics and probability, maths for machine learning, SQL, data cleaning and EDA, data visualization with Power BI or Tableau, machine learning, deep learning, NLP, GenAI basics, big data and cloud basics, MLOps and deployment, and a capstone project.
What are the main subjects in data science?
The main data science subjects are Python programming, statistics and probability, linear algebra and calculus, databases and SQL, data wrangling, data visualization, machine learning, deep learning, NLP, big data and cloud, MLOps, and data ethics.
Is 6 months enough to learn data science?
Six months is enough to become job-ready for entry-level roles if you study about 2 to 3 hours a day and build projects. You will cover the core syllabus, but you will keep learning advanced topics on the job.
Is 3 months enough for data science?
Three months is enough for the basics: Python, pandas, core statistics, SQL and a few machine learning models. It is usually too short for deep learning, NLP, deployment and a strong project portfolio, so plan to keep practising after that.
Can I learn data science without a coding background?
Yes. Most certificate courses start from Python basics and assume no coding knowledge. Expect the first month to feel slow. With daily practice, coding becomes comfortable within a few weeks.
Do I need strong maths for data science?
You need school-level maths to start. As you move into machine learning and deep learning, you will learn basic statistics, linear algebra and calculus. Focus on understanding the ideas; libraries handle the heavy calculations.
Is there a data science syllabus PDF?
Many institutes share their syllabus as a PDF. You can also save this page as a PDF from your browser and use the module tables as a study checklist. To get Brolly Academy's latest course syllabus, book a free demo class.
Which is better for data science: a degree or a certificate course?
It depends on where you are. If you are after Class 12, a BSc or BTech gives you depth and a recognised degree. If you already have a degree, a practical certificate course with projects is the faster way to a data science job.
What is the data science course syllabus for beginners?
A beginner data science course syllabus starts with Python, statistics and SQL, because every later module depends on them. After that it moves to data cleaning, EDA and visualization, then machine learning. Advanced topics such as deep learning, NLP, GenAI basics and deployment come later. The course should end with a capstone project. You do not need a coding background to start, only steady daily practice.
What is the data science syllabus after 12th?
After Class 12, data science is usually studied as a BSc or a BTech degree. A BSc in data science takes 3 years, and some universities offer 4-year honours. A BTech in CSE with data science, or in AI and data science, takes 4 years. Both cover programming, maths, statistics and machine learning, but the exact subjects differ by university, so read the official syllabus of each college.
What are the data science course subjects in the first year?
In the first year of a BSc in data science, early semesters usually cover programming, discrete maths, calculus, linear algebra, probability and statistics, and database systems. Many universities also include language and general courses. In a job-focused certificate course, the first month is usually Python basics, NumPy, pandas and Jupyter, followed by statistics and SQL in the second month.
Which subjects are required to become a data scientist?
To become a data scientist you need four foundation subjects: Python programming, statistics and probability, linear algebra and calculus, and databases with SQL. On top of these you learn applied subjects like data wrangling, data visualization and machine learning. Advanced subjects such as deep learning, NLP, GenAI, big data and MLOps come next. Data ethics and business knowledge help you use data well.
How long does it take to complete a data scientist course syllabus?
A job-focused certificate course usually takes about 4 to 6 months. A BSc or BTech degree takes 3 to 4 years, and an MSc or MTech takes 2 years. If you follow a self-study plan and study about 2 to 3 hours a day, six months is enough to cover the core syllabus. Working professionals may need 8 or 9 months.
What is the BTech data science syllabus?
A BTech in CSE with data science, or in AI and data science, includes the full engineering core: programming, data structures and algorithms, operating systems, computer networks and database systems. On top of that you study statistics, machine learning, deep learning, big data analytics and cloud computing. Expect labs, mini projects and a final-year major project. Subjects vary by university.
What is the MSc data science syllabus?
An MSc data science syllabus goes deeper into theory than a certificate course. It usually covers advanced statistical inference, optimisation, machine learning theory and deep learning, and often Bayesian methods or time series. Most programmes end with a dissertation or an industry project. It suits graduates in maths, statistics, computer science or engineering who want research or roles that need strong maths.
Is SQL included in the data science syllabus?
Yes. SQL is a core module in every good data science syllabus, because most company data lives in databases. You learn SELECT queries, joins, GROUP BY, subqueries, CTEs and window functions, and how to load query results into pandas. Many data science interviews in India start with SQL questions. A course with no SQL module is a red flag.
Does the data science syllabus in 2026 include generative AI?
A modern data science syllabus should at least introduce generative AI. This module covers how large language models work, prompt engineering, calling an LLM API from Python, embeddings, vector databases and retrieval-augmented generation (RAG). It also covers limits such as hallucination, cost, privacy and bias. If a course stops at machine learning theory, it is out of date.
Which tools are covered in a data science course?
A data science course usually covers Python, Jupyter, pandas, NumPy, Matplotlib, Seaborn, scikit-learn, SQL, Power BI or Tableau, TensorFlow or PyTorch, Hugging Face, PySpark, Git and a deployment tool such as Streamlit or FastAPI. Almost all of them are free to use. For freshers, Python, SQL, pandas and scikit-learn matter the most in interviews.
Should I learn Python or R for data science?
Learn Python first. It is the main language of data science, its syntax is simple and it has free libraries for almost every task. Most job postings in India ask for Python. R is strong in statistics and has the popular ggplot2 charting package, so some courses offer it as an optional language. Pick up R only if your target role needs it.
How many projects should a data science course include?
A good data science course includes 4 to 6 projects that grow in difficulty. This usually means one or two EDA projects, two or three machine learning projects, one NLP or deep learning project and one deployed capstone. Each project should use real data, answer a clear question and end with a result you can explain in an interview.
Who is eligible for a data science course in India?
For most certificate courses, any graduate or final-year student can join, from any stream, including BTech, BSc, BCom, BCA, MBA and arts graduates. Working professionals can also join. You do not need prior coding knowledge, and Class 10 and 12 level maths is enough to start. Degree programmes such as BSc, BTech and MSc follow their own university eligibility rules.
What is the difference between a data science and a data analytics syllabus?
A data analytics syllabus mainly teaches you to explain what happened in the past, using SQL, Excel, statistics and BI dashboards. A data science syllabus covers those skills too, but it also teaches machine learning, deep learning and NLP so you can predict what will happen and build systems that act on it. Data science also adds deployment and MLOps.
What are the red flags in a data science course syllabus?
Watch out for a syllabus that is only theory and slides with no coding assignments. Other red flags are no SQL module, no mention of model evaluation or overfitting, and projects that are copied notebooks with no original work. Be careful of old tools with nothing about GenAI or deployment, and of any promise of a guaranteed job or fixed salary.
How many hours a day should I study for data science?
Plan to study about 2 to 3 hours a day. On weekdays, spend 1 hour learning a new topic and 1 to 2 hours practising with code. Use Saturday for your current project and Sunday to revise, solve interview questions and update GitHub. Steady daily practice matters more than long weekend sessions, especially for beginners with no coding background.
What does the Brolly Academy data science course syllabus cover?
Brolly Academy's Data Science Course in Hyderabad is a 4-month programme with 20 modules. It covers Python, statistics and probability, data cleaning, pandas and NumPy, data visualization, SQL and advanced SQL, Power BI, supervised and unsupervised machine learning, model evaluation, neural networks and NLP. Capstone projects include predictive sales analytics, customer segmentation and a recommendation engine. You can start with a free demo class.
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