Syllabus

Unit 1 Conceptual Introductions
• What Is Data Science? Where Do We See Data Science? How Does Data Science Relate to Other Fields? The Relationship between Data Science and Information Science
• Data Data Types, Data Collections, Data Pre-processing
• Techniques: Data Analysis and Data Analytics, Descriptive Analysis, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics, Exploratory Analysis, Mechanistic Analysis

Unit 2 Tools for Data Science
• MS Excel
o Interface, Data entry, working, Formatting – Auto, conditional and rule based, Merging data, Filtration, Lookup, Data solver, Charts and Diagrams, LPP and Financial Functions
• UNIX
o Getting Access to UNIX, Connecting to a UNIX Server, Basic Commands, Editing on UNIX, Redirections and Piping, Solving Small Problems with UNIX
• Python
o Getting Access to Python, Basic Examples, Control Structures, Statistics Essentials
• R
o Getting Access to R, Getting Started with R, Graphics and Data Visualization, Statistics and Machine Learning
• MySQL
o Getting Started with MySQL, Creating and Inserting Records, Retrieving Records, Searching in MySQL, Accessing MySQL with Python and R, Introduction to Other Popular Databases

Unit III: Machine Learning for Data Science
• Machine Learning Introduction and Regression: Introduction, What Is Machine Learning? Regression, Gradient Descent,
• Supervised Learning: Logistic Regression, Softmax Regression, Classification with kNN, Decision Tree, Random Forest, Naïve Bayes, Support Vector Machine (SVM)
• Unsupervised Learning: Agglomerative Clustering, Divisive Clustering, Expectation Maximization (EM), Introduction to Reinforcement Learning

Unit IV: Applications, Evaluations, and Methods
• Hands-On with Solving Data Problems: Collecting and Analyzing Social Networking Site Data, Collecting and Analyzing YouTube Data, Analyzing Yelp Reviews and Ratings
• Data Collection, Experimentation, and Evaluation:
o Data Collection Methods, Surveys, Survey Question Types, Survey Audience, Survey Services, Analyzing Survey Data, Pros and Cons of Surveys, Interviews and Focus Groups, Why Do an Interview?, Why Focus Groups?, Interview or Focus Group Procedure, Analyzing Interview Data, Pros and Cons of Interviews and Focus Groups, Log and Diary Data, User Studies in Lab and Field
o Picking Data Collection and Analysis Methods, Introduction to Quantitative Methods, Introduction to Qualitative Methods, Mixed Method Studies
o Evaluation, Comparing Models, Training–Testing and A/B Testing, Cross-Validation

To understand Data Science and Computation thinking, Data types, major data sources, and formats and how to perform basic data cleaning and transformation., introduction to correlation and regression

To know the basics of the Data analysis tools like MS Excel, UNIX, Python, R, MySQL environment, Running commands, utilities, and operations

To use and practice the various Machine Learning tools and techniques

To be acquainted with Primary and Secondary data analysis with socioeconomics research issues at hand.

To be prepared for Development sector Practitioners and experts with appropriate Evaluation methodologies

Fundamental Knowledge about theories and tools of Data Science

Skill acquisition with respect to computation, report preparation and draft making suitable for national and international agencies.

CHIRAG SHAH, A Hands-On Introduction to Data Science, Cambridge University Press , 2020, DOI: 10.1017/9781108560412

Indira Gandhi National Open University School of Engineering & Technology, MIO - 002 : SMART TECHNOLOGIES (HARDWARE AND SOFTWARE) BLOCK 3: AI AND MACHINE LEARNING, School of Engineering and Technology (SOET), Indira Gandhi National Open University, New Delhi , 2022

Indira Gandhi National Open University, School of Computer and Information Sciences (SOCIS), MCS-201, Block 3, Introduction to python programming, Indira Gandhi National Open University, School of Computer and Information Sciences (SOCIS) , 2021