Data Science Training in Amritsar
Data Science Training & Applied Machine Learning — our Data Science course in Amritsar, 16 Weeks. Master the complete data science pipeline. From statistical analysis and machine learning to deep learning and production deployment, build the skills top analytics teams demand.
Course Overview
Data science is not about knowing 50 algorithms. It is about asking the right question, finding the right data, and building something that actually works in production. We start with Python: NumPy vectorization, Pandas data wrangling, and the kind of messy data cleaning that takes 80% of your time on real projects. Not toy datasets. Real CSVs with encoding errors, missing values, and columns that should be dates but are strings.
Statistics is where most bootcamp graduates are weak. We fix that. You will understand probability distributions, hypothesis testing, p-values, and confidence intervals. Not as formulas to memorize, but as tools to make decisions. Then we move into machine learning with scikit-learn: linear regression, random forests, XGBoost. You will learn cross-validation properly, understand why accuracy is a terrible metric for imbalanced data, and use SHAP values to explain your models.
Deep learning is not magic, and we teach it that way. You will build neural networks from scratch in PyTorch, understand backpropagation by doing it by hand, then move to transfer learning with pre-trained models. NLP covers everything from TF-IDF baselines to transformers with Hugging Face. And you will deploy your model as an API with FastAPI, because a model that sits in a notebook is worthless.
The final stretch is big data and production. Spark for datasets that do not fit in memory. Airflow for pipelines that run on schedule. Cloud deployment on AWS. And a capstone project that goes from raw data to a deployed model with a dashboard. Sixteen weeks, and you can do what most data scientists do after two years on the job.
Curriculum Blueprint
Weekly module breakdown covering data wrangling, machine learning, deep learning, and big data pipelines.
- Python for data: NumPy arrays, vectorization vs loops, broadcasting rules, and why a 10x speedup comes from removing for-loops
- Pandas fundamentals: Series, DataFrame, indexing, filtering, and the difference between loc, iloc, and at
- Data loading: CSV, Excel, JSON, SQL databases, and handling messy real-world files with encoding issues
- Pandas deep dive: groupby, merge, join, concat, pivot tables, and why your merge produces 3x more rows than expected
- Data cleaning: missing value strategies (drop, impute, interpolate), duplicate detection, and type conversion pitfalls
- Feature engineering: binning, encoding categorical variables, scaling (MinMax, Standard, Robust), and datetime features
- Matplotlib: figure/axes model, subplots, customizing styles, and why the default plots look bad and how to fix them
- Seaborn: statistical visualizations, pair plots, heatmaps, facet grids, and choosing the right chart for your data
- Plotly: interactive charts, dashboards, hover information, and when interactivity adds value vs when it is just noise
- EDA workflow: statistical summaries, correlation matrices, distribution analysis, and asking the right questions of your data
- Outlier detection: IQR method, Z-score, isolation forest, and deciding whether outliers are errors or insights
- First project: load a messy dataset, clean it, visualize it, and tell a story with your findings
Tools Covered
Prerequisites
- Fundamental understanding of mathematics (algebra, basic calculus, statistics)
- Basic exposure to Python or any programming language
- Comfortable working with spreadsheets and data tables
- Willingness to learn both theoretical concepts and practical implementation
Lab Access
- Cloud sandbox environment
- Real production server access
- Local AI model playground
- 24/7 Git repository access
Quick Inquiry
Interested? Drop your details and we will reach out.
Common Questions
The things people ask us on the phone before they enrol — answered the same way we would answer them there.
There is no separate price for it. WebPrims charges one fee — ₹4,500 / month — and it covers any course in the catalogue, so the Data Science course works out at about 4 months — around ₹18,000 in total. No admission fee, no registration fee, and the certificate is included.
Monday to Saturday, with batches starting at 11:00 AM, 1:00 PM, 3:00 PM and 5:00 PM. Each slot runs up to two hours — sometimes a session finishes early, never late. That works out at roughly 192 hours of class time across the 16 Weeks. Pick whichever time fits around college or work, and move to another later if your timetable changes; it is the same Data Science syllabus in each. We are closed on Sunday.
16 Weeks of taught material, which is about 4 months of Data Science classes. That is the pace of the syllabus; how long you actually take depends on how often you turn up, and nobody is pushed to keep up with the batch.
No formal background, but be ready for maths. This track is heavier on statistics than the analytics one, by design.
Models that leave the notebook: a trained and evaluated model, honestly measured, deployed behind an API where it takes real input and can be wrong in public.
Analytics if you want to answer business questions, and most students find it the more employable of the two locally. Data science if you want to build the models themselves and do not mind the maths. We will say which suits you after the demo class rather than sell you the longer one.
Yes. Book a free demo class and sit in a real Data Science session — one that was running anyway, not a presentation arranged for you. Write some code on one of our machines, ask the students already in it what it is like, and decide afterwards. Nothing to pay and no obligation.
Something here not answered? Ask us directly or book a free demo class and put it to the Data Science mentor in person.
Weighing this up against something similar? Data Science or Data Analytics? Courses in Amritsar sets them side by side and says which suits whom.