Data Science & Machine Learning Course in Jaipur
Move from raw data to an analysis you can explain and a model you can evaluate. Combine Python, statistics and visualisation with supervised and unsupervised machine learning through practical datasets.
- Practical learning
- Project practice
- Jaipur
Data Science & Machine Learning
Build your foundation in Data Science & Machine Learning
Move from raw data to an analysis you can explain and a model you can evaluate. Combine Python, statistics and visualisation with supervised and unsupervised machine learning through practical datasets.
Who can join?
Students, graduates and professionals interested in data work. Basic Python and school-level mathematics are useful; review these foundations before the modelling modules.
Skills and tools
Course curriculum
Explore the learning outline below. Confirm the detailed syllabus and module coverage for your chosen batch with the course team.
Python and data preparation
Use Python to work with datasets, identify missing values and prepare usable inputs for analysis.
Statistics and probability
Explore distributions, descriptive statistics, probability and the interpretation of relationships in data.
Exploratory analysis
Use visualisations to investigate patterns, communicate findings and identify data-quality issues.
Supervised learning
Study regression and decision trees and learn how predictions relate to input features.
Unsupervised learning and forecasting
Explore K-means clustering, market basket analysis and introductory time-series concepts.
Evaluation and applications
Compare model results, identify limitations and explore how machine learning and neural networks are applied to real problems.
Project practice ideas
Use these examples to discuss suitable practice work with your trainer.
Exploratory data report
Clean a dataset, produce visualisations and explain the findings and limitations.
Prediction or clustering study
Build a small model and document its inputs, evaluation method and results.
Roles to explore
Build a portfolio and practise explaining your work. Role requirements depend on the employer, your skills and experience.
- Data science trainee
- Junior data analyst
- Machine learning intern
Frequently asked questions
How is data science different from data analytics?
Analytics focuses on answering questions from data and communicating findings. This path adds statistical modelling and machine learning to support prediction and pattern discovery.
Who is this course for?
Students, graduates and professionals interested in data work. Basic Python and school-level mathematics are useful; review these foundations before the modelling modules.
How can I confirm fees, duration and batch timings?
Contact Groot Academy for the current syllabus, fees, duration and available learning modes before enrolling. These details depend on the selected course and batch.