Course	Foundation of Data Science and Analytics
Code	NS-0
Course Starting Date	21-Jan-2026
Last Date To Apply	18-Jan-2026
Course Coordinator	R.C.Bhanusree Mob:8500422223

Course Preamble

This course provides a comprehensive foundation in data science, programming, and analytical techniques, designed to prepare learners for personal, academic, and professional applications in data-driven environments. It equips students with both theoretical knowledge and practical skills necessary for handling, analyzing, and interpr eting data effectively using modern programming tools and statistical methods.

The course is divided into six core modules:

1. Fundamentals of Data Science – Covers the definition, scope, applications, and workflow of data science, including data acquisition, preprocessing, exploratory data analysis, modeling, and deployment.
2. Foundational in Python Programming – Introduces Python programming for data science, including syntax, control structures, key libraries (NumPy, Pandas), and use of data analytics tools (Jupyter, Anaconda).
3. Machine Learning Fundamentals – Focuses on core machine learning principles, supervised and unsupervised learning, foundational algorithms (linear regression, decision trees, clustering), and model evaluation techniques.
4. Foundational in R Programming – Covers R programming basics, hands-on sessions in R Studio, data handling, importing, and sampling for statistical analysis.
5. Data Analysis Concepts and Techniques – Teaches exploratory data analysis (EDA), data visualization using Matplotlib and Seaborn, statistical and time series analysis, sentiment analysis, and text classification with NLP techniques.
6. Case Studies and Industry Applications of Data Science – Explores real-world applications of data science in finance, healthcare, and e-commerce, along with ethical considerations and data privacy issues.

Each module includes theory and practical sessions, ensuring a balance of conceptual understanding and hands-on experience.

Total Learning Hours:

120 hours (45 hours theory + 75 hours practical).

Upon successful completion, learners will have developed essential skills in data handling, programming, machine learning, data visualization, and analysis, enabling them to perform effectively in analytics, data science support, and IT-enabled roles.

Proposed Occupation:

Data Analyst / Junior Data Scientist

Awarding & Certifying Body:

National Institute of Electronics and Information Technology (NIELIT)

Course Objective

The primary objective of the course “Foundation of Data Science and Analysis” is to enable learners to:
• Understand the fundamental concepts of data science, including its scope, workflow, and applications across industries.
• Develop proficiency in Python and R programming for data handling, manipulation, and analysis using essential libraries and tools.
• Gain knowledge of core machine learning principles, including supervised and unsupervised learning, foundational algorithms, and model evaluation techniques.
• Acquire practical skills in exploratory data analysis (EDA), data visualization, statistical analysis, and time series analysis for informed decision-making.
• Apply text analytics and natural language processing (NLP) techniques for sentiment analysis and classification tasks.
• Explore real-world applications of data science in sectors such as finance, healthcare, and e-commerce, along with ethical and data privacy considerations.
• Develop practical competency through hands-on exercises and mini-projects, fostering analytical thinking, problem-solving, and technical competence.
• Prepare participants for careers in data analytics, business intelligence, IT support, and data-driven roles, demonstrating employability skills and industry readiness.

Course Outcome

After completing the Course on Data Science, learners will be able to:

• Understand the fundamentals of Data Science, including its scope, applications, and differences from related fields such as Data Analytics and Machine Learning, and explain the Data Science workflow from data acquisition to deployment.
• Develop proficiency in Python programming for data science, including syntax, data types, control structures, and the use of key libraries such as NumPy and Pandas for data manipulation and analysis.
• Apply data pre-processing and analytics techniques to clean, normalize, and standardize data, and utilize tools like Jupyter Notebook and Anaconda to perform hands-on data analysis tasks.
• Understand core machine learning principles, implement foundational algorithms such as linear regression, decision trees, and clustering, and evaluate model performance using appropriate metrics and validation techniques.
• Gain proficiency in R programming, including basic commands, data handling, and visualization, and apply these skills in hands-on sessions using R Studio for data analysis projects.
• Perform exploratory data analysis (EDA), statistical analysis, time series analysis, and visualization using Python and R, and apply text mining and sentiment analysis techniques for meaningful insights.
• Analyze real-world case studies from domains such as finance, healthcare, and e-commerce, considering ethical practices and data privacy, and demonstrate the ability to apply data science concepts to solve practical problems.

Course Structure 

Module No.	Module Title	Theory (Hrs)	Practical (Hrs)
1	Fundamentals of Data Science	5	2.5
2	Foundation in Python Programming	10	20
3	Machine Learning Fundamentals	5	10
4	Foundation in R Programming	10	20
5	Data Analysis Concepts and Techniques	10	20
6	Case Studies and Industry Applications	5	2.5
Total		45 Hours	75 Hours

 

 

 

 

 

 

 

 

 

Course Contents

Module 1: Fundamentals of Data Science • Introduction to Data Science: Definition, scope, and applications
• Differences: Data Science vs Data Analytics vs Machine Learning
• Data Science Workflow: Data acquisition, pre-processing, exploratory data analysis (EDA), modeling, deployment
• Practical Exercises: Explore datasets, perform basic data acquisition and cleaning, visualize simple data insights
Module 2: Foundational Python Programming • Basics of Python Programming: Syntax, data types, control structures
• Key Libraries: NumPy, Pandas for data manipulation
• Data Pre-processing Techniques: Handling missing values, normalization, standardization
• Data Analytics Tools: Jupyter Notebook, Anaconda environment
• Practical Exercises: Write Python scripts, clean datasets, perform data manipulations using Pandas, implement basic calculations
Module 3: Machine Learning Fundamentals • Core Principles: Supervised vs unsupervised learning
• Foundational ML Algorithms: Linear regression, decision trees, clustering
• Model Evaluation: Accuracy metrics, performance evaluation techniques
• Practical Exercises: Build simple ML models on datasets, evaluate performance, visualize results
Module 4: Foundational R Programming • Introduction to R: R environment, R Studio setup, and basics
• Data Handling in R: Importing datasets, sampling, basic commands
• Practical Exercises: Write R scripts, perform data import, sampling, and preliminary analysis
Module 5: Data Analysis Concepts and Techniques • Exploratory Data Analysis (EDA): Visualization using Matplotlib and Seaborn
• Statistical Analysis: Probability, time series analysis
• Text Analysis: Sentiment analysis, NLP techniques for text classification
• Practical Exercises: Visualize datasets, perform statistical tests, implement basic text analysis
Module 6: Case Studies and Industry Applications • Real-world Applications: Finance, healthcare, e-commerce
• Ethical Considerations: Data privacy, responsible use of data in projects
• Practical Exercises: Analyze case studies, present findings, address ethical considerations in sample projects

Course Fees

Course Fee: General: Rs. 15000/-
(Including GST) SC/ST: Free

Modular wise Course Fee: Not Applicable for this course

Payment Verification & Registration Information

General & OBC - Candidates / Online Courses for all: After completion of the fee payment, please submit the form available under Apply Now. Our team will review and verify your payment details. Once the verification is successfully completed, we will contact you with further guidance to complete the remaining steps of the registration process.For any queries or assistance, please feel free to contact or message the course coordinator.

SC / ST – Offline Courses: SC/ST offline courses are free of charge. Instead of uploading a payment acknowledgement, please upload your Government-issued Caste Certificate during form submission.

Eligibility Criteria

Important Dates

Next update dates

 

Month	Starting Date of Registration	Last Date of Registration	Welcome Mail Sending Date(Befor 6PM)	Course Starting Date
Jan,2026	11-Nov-2025	18-Jan-2026	19-Jan-2026	21-Jan-2026

Contact

For further information if any, you may Contact the Course Coordinator : R.C.Bhanusree , Mob:8500422223