Data Science & Machine Learning Course in Jaipur
Move from raw data to explainable analysis and evaluated machine-learning models. This path combines Python, statistics, visualisation and predictive methods, making it distinct from Data Analytics, which focuses more heavily on reporting, dashboards and business analysis.
- Practical learning
- Project practice
- Jaipur
Data Science & Machine Learning
Build your foundation in Data Science & Machine Learning
Move from raw data to explainable analysis and evaluated machine-learning models. This path combines Python, statistics, visualisation and predictive methods, making it distinct from Data Analytics, which focuses more heavily on reporting, dashboards and business analysis.
Begin with Python and Jupyter Notebook, then use NumPy, Pandas, SQL, statistics and visualization to understand and prepare data. The learning path connects exploratory analysis with supervised and unsupervised modelling.
Build and compare regression, classification and clustering models with Scikit-learn. Practise feature engineering, validation, error analysis and Power BI reporting, with introductory deep learning, NLP, computer vision, Generative AI and deployment concepts.
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 published learning outline below. Confirm the detailed syllabus and module coverage for your chosen batch with the course team.
Introduction to Data Science
Explore the data science lifecycle, different data sources and how analysis supports decisions.
- Introduction to Data Science
- Data Science Lifecycle
- Role of a Data Scientist
- Data Science vs Data Analytics
- Types of Data
- Structured and Unstructured Data
- Data Collection and Data Sources
- Data-Driven Decision Making
- Data Science Career Opportunities
- Setting Up the Data Science Environment
Python Fundamentals
Begin with Python and Jupyter Notebook, then practise variables, control flow, functions, collections and files.
- Introduction to Python
- Installing Python and Jupyter Notebook
- Variables and Data Types
- Operators and Expressions
- Conditional Statements
- Loops
- Functions
- Lists, Tuples and Sets
- Dictionaries
- Working with Files
Python for Data Science
Use NumPy and Pandas to import, select, clean and prepare tabular datasets.
- Introduction to NumPy
- NumPy Arrays
- Array Operations
- Indexing and Slicing
- Introduction to Pandas
- Series and DataFrames
- Importing CSV and Excel Data
- Data Selection and Filtering
- Data Cleaning with Pandas
- Handling Missing Values
Exploratory Data Analysis
Investigate dataset structure, missing values, duplicates, outliers, distributions and relationships.
- Introduction to Exploratory Data Analysis
- Understanding Dataset Structure
- Descriptive Statistics
- Identifying Missing Values
- Detecting Duplicate Records
- Outlier Detection
- Data Distribution Analysis
- Correlation Analysis
- Feature Relationships
- Exploratory Data Analysis Project
Data Visualization
Create and interpret charts using Matplotlib and Seaborn, then apply them in a visualization project.
- Introduction to Data Visualization
- Matplotlib Fundamentals
- Line Charts
- Bar Charts
- Histograms
- Scatter Plots
- Box Plots
- Introduction to Seaborn
- Advanced Data Visualization
- Data Visualization Project
Statistics and Probability
Use descriptive statistics, probability, distributions and correlation to interpret data.
- Introduction to Statistics
- Types of Data and Variables
- Mean, Median and Mode
- Measures of Dispersion
- Variance and Standard Deviation
- Probability Fundamentals
- Probability Distributions
- Normal Distribution
- Correlation and Covariance
- Statistical Analysis for Data Science
SQL for Data Science
Query relational data with joins, aggregates, subqueries, CTEs and window functions.
- Introduction to SQL and Databases
- Understanding Relational Databases
- SELECT and WHERE Statements
- Sorting and Filtering Data
- Aggregate Functions
- GROUP BY and HAVING
- SQL Joins
- Subqueries and CTEs
- Window Functions
- SQL Data Analysis Project
Machine Learning Fundamentals
Understand learning types, features and targets, training and testing, and Scikit-learn workflows.
- Introduction to Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning Overview
- Machine Learning Workflow
- Features and Target Variables
- Training and Testing Data
- Overfitting and Underfitting
- Introduction to Scikit-learn
Data Preprocessing and Feature Engineering
Prepare model inputs using imputation, encoding, scaling and feature selection or extraction.
- Data Preprocessing Workflow
- Handling Missing Values
- Encoding Categorical Data
- Feature Scaling
- Normalization and Standardization
- Feature Selection
- Feature Extraction
- Creating New Features
- Training and Test Data Preparation
- Data Preprocessing Project
Supervised Machine Learning
Train regression and classification models using trees, forests, nearest neighbors and support vector machines.
- Introduction to Supervised Learning
- Linear Regression
- Multiple Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- K-Nearest Neighbors
- Support Vector Machines
- Model Training and Prediction
- Supervised Learning Project
Unsupervised Machine Learning
Explore clustering, dimensionality reduction and customer segmentation, then evaluate the groups.
- Introduction to Unsupervised Learning
- Clustering Concepts
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN Overview
- Dimensionality Reduction
- Principal Component Analysis
- Customer Segmentation
- Cluster Evaluation
- Unsupervised Learning Project
Machine Learning Model Evaluation
Compare models with validation methods, classification and regression metrics, and error analysis.
- Introduction to Model Evaluation
- Train-Test Split
- Cross Validation
- Classification Metrics
- Accuracy and Error Analysis
- Precision and Recall
- F1 Score
- Confusion Matrix
- Regression Evaluation Metrics
- Model Optimization Basics
Advanced Data Science Concepts
Review introductory neural networks, NLP, computer vision, Generative AI, deployment and responsible use.
- Introduction to Deep Learning
- Neural Network Fundamentals
- Introduction to Natural Language Processing
- Text Data Processing
- Introduction to Computer Vision
- Introduction to Generative AI
- Model Deployment Concepts
- APIs for Data Science Applications
- Responsible AI and Ethics
- Data Science Industry Best Practices
Power BI for Data Science
Prepare data with Power Query and build Power BI models, DAX calculations, reports and dashboards.
- Introduction to Power BI
- Connecting Data Sources
- Data Transformation with Power Query
- Data Modeling
- Creating Visualizations
- DAX Fundamentals
- Calculated Columns and Measures
- Interactive Reports and Dashboards
- Publishing Power BI Reports
- Business Intelligence Project
Data Science Live Projects
Apply analysis and modelling through prediction, segmentation, sentiment analysis and dashboard projects.
- Sales Prediction Project
- Customer Churn Prediction
- House Price Prediction
- Customer Segmentation Project
- Marketing Analytics Project
- Financial Data Analysis
- Sentiment Analysis Project
- Machine Learning Classification Project
- Power BI Analytics Dashboard
- Final Data Science Capstone Project
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.
Sales or house-price prediction
Build a regression project and document data preparation, evaluation and prediction results.
Customer churn and segmentation
Explore classification and clustering workflows for customer data, with appropriate model evaluation.
Sentiment analysis study
Apply the introductory text-processing workflow to a sentiment analysis project and review its limitations.
Power BI analytics dashboard
Prepare and model data, create DAX calculations and present findings through an interactive dashboard.
Final Data Science capstone
Combine data preparation, analysis, modelling and reporting in a documented final project.
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?
Data Analytics focuses mainly on querying, reporting, dashboards and communicating business findings. Data Science adds deeper statistical modelling and machine-learning methods for prediction and pattern discovery.
How does this course relate to the Artificial Intelligence and Generative AI courses?
This learning path focuses on data analysis, predictive modelling and machine learning. The Artificial Intelligence course covers a broader AI workflow, including NLP, computer vision and deployment. The dedicated Generative AI course covers prompt engineering and LLM application work. Compare the outlines to choose your learning path.
Can beginners join this Data Science course?
The source learning path starts with Python and analytics fundamentals before progressing to machine learning and advanced concepts. Basic mathematics and regular programming practice can help you work through the modelling topics.
Which tools are included?
The source curriculum includes Python, Jupyter Notebook, NumPy, Pandas, SQL, Matplotlib, Seaborn, Scikit-learn and Power BI.
Are practical projects included?
Yes. The curriculum includes prediction, segmentation, analytics, sentiment analysis and dashboard projects, followed by a final capstone.
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.
Ready to discuss Data Science & Machine Learning?
Ask the course team about syllabus coverage, current batch timings, fees, duration and learning modes before you decide.