Statistics for Data Analytics

Programme outline

Learning objectives

1. Understand the principles of deductive and inductive reasoning within the context of data science.
2. Differentiate between various sampling methods and their respective applications.
3. Analyse data by employing descriptive statistics, encompassing measures of central tendency, dispersion, association, and asymmetry.
4. Understand concept of probability distribution, the central limit theorem, estimation, and confidence intervals, along with their practical implications.

Day 1

• Statistics and data science
• Common reasonings in data science – Deductive and inductive reasonings
• Populations and samples
• Sampling methods – Non-probability and probability sampling
• Treatment and control groups
• Between and within-subject designs
• Descriptive statistics – Measures of central tendency, measures of dispersion, measures of association, measures of asymmetry.
• Types of data and levels of measurements

Day 2

• Probability – General concept of probability, conditional probability
• Random variables and probability distribution – Types of continuous and discrete probability distributions
• Populations and samples – Central Limit Theorem
• Estimation and confidence intervals

Mode of assessment

• Quiz