This course provides a comprehensive exploration of AI and Data Science, emphasizing their relevance and applications in modern contexts. Participants will delve into the scope of Data Science, types of data and analysis techniques, essential skills for data scientists, the data science life cycle, Python programming tailored for data science applications, object-oriented programming (OOP) principles, fundamentals of database management systems (DBMS), popular machine learning (ML) algorithms, and practical demonstrations showcasing real-world applications.

Intended audience

Data Scientist, Data Analyst, Business Analyst, Data Engineer, Machine Learning Engineer, Research Analyst and Students interested in Data Science

Prerequisites

Any Diploma or Degree

Assessment & certification

Assessment fee: Included — no extra fee
Assessment mode: Online

NCrF level: 6.5 (NCrF credit-eligible)

Syllabus

Week 1: Introduction to AI and Data Science
Week 2: Justification and importance of AI and Data Science in contemporary environments
Week 3: Overview of the scope and applications of Data Science across industries
Week 4: Types of Data and Analysis Techniques
Week 5: Classification of data types: structured, unstructured, and semi-structured data
Week 6: Techniques for data analysis: exploratory data analysis (EDA), statistical analysis, and predictive modeling
Week 7: Skills for Data Scientists
Week 8: Essential skills and competencies required for success in Data Science roles
Week 9: Training in critical thinking, problem-solving, and communication skills for data-driven decision-making
Week 10: Data Science Life Cycle
Week 11: Phases of the data science life cycle: data acquisition, cleaning, exploration, modeling, evaluation, and deployment
Week 12: Best practices and methodologies for each phase of the data science process
Week 13: Python Programming for Data Science
Week 14: Introduction to Python programming language and its relevance in Data Science
Week 15: Python libraries and frameworks for data manipulation, visualization, and analysis
Week 16: Hands-on exercises and projects using Python for data science applications
Week 17: Object-Oriented Programming (OOP) and DBMS Fundamentals
Week 18: Principles of object-oriented programming (OOP) and their application in software development
Week 19: Introduction to database management systems (DBMS) and relational database concepts
Week 20: SQL fundamentals for data retrieval, manipulation, and management
Week 21: Machine Learning Algorithms
Week 22: Overview of popular machine learning algorithms: supervised, unsupervised, and reinforcement learning
Week 23: Applications of ML algorithms in data prediction, classification, clustering, and anomaly detection
Week 24: Application Demonstrations in Data Science
Week 25: Real-world examples and case studies showcasing the application of Data Science techniques
Week 26: Practical demonstrations of ML algorithms and their implementation in solving business challenges