Course overview: Foundation course in statistics tailored for beginners.
Key Objectives: Learning to create, summarize data using graphical and numerical techniques; understanding uncertainty and probability; and focusing on applications over theory.
Target Audience: Anyone with class 10 level math.
Key Learning Objectives
Create and manipulate datasets.
Present and describe data using appropriate graphical and numerical techniques.
Understand uncertainty through probability and application of random variables.
Course Structure
Duration: 12 weeks, divided into 3 modules:
Basics of Data and Summarization (Weeks 1-4)
Introduction to Probability (Weeks 5-7)
Random Variables and Distributions (Weeks 8-12)
Week-by-Week Breakdown
Weeks 1-4: Basics of Data
Week 1: Introduction to Data
Understanding data collection, variables, and observations.
Classifying data: Quantitative vs Qualitative, Numerical vs Categorical.
Week 2: Categorizing and Summarizing Categorical Data
Framing questions and finding answers from data.
Using frequency tables and appropriate graphical techniques.
Week 3: Summarizing Numerical Data
Numerical summaries like mean and variability.
Graphical summaries like histograms and box plots.
Week 4: Associations Between Variables
Understanding relationships between variables using contingency tables and scatter plots.
Weeks 5-7: Introduction to Probability
Week 5: Principles of Counting
Understanding permutations and combinations for real-life applications.
Weeks 6-7: Basic Probability Concepts
Uncertainty in real life and introduction to set algebra.
Understanding simple/compound events, mutually exclusive events, and independent events.
Weeks 8-12: Random Variables and Distributions
Weeks 8-10: Discrete Random Variables
Concept of random variables, expectation, and variance.
Binomial distribution and its applications (e.g., guessing in MCQ exams).
Weeks 11-12: Continuous Random Variables
Concepts of continuous variables and probability density function.
Focus on normal distribution and empirical rule.
Summary and Expectations
By the end of the course, students should:
Understand and manipulate data sets.
Classify and summarize variables.
Formulate questions based on data and find appropriate summaries.
Understand basic probability and its applications.
Differentiate between and work with discrete and continuous random variables.
Conclusion
Course focuses on a practical understanding of statistics and its applications rather than theoretical proofs.
Students should gain a strong conceptual foundation in dealing with data and uncertainty.
Miscellaneous Information
Example discussed in the lecture
University admissions data:
Captured fields: Name, gender, DOB, class 10 and 12 marks, board, mobile number.
Questions that can be asked from such data sets include: proportion of female students, distribution by regions, average marks, etc.