Question 1

Module 1: Data Visualisation

Accepted Answer

1.1 What is data? 1.2 What is data visualisation? 1.3 Python for data visualisation 1.4 Data frames 1.5 Bar charts 1.6 Pie charts 1.7 Scatter plots 1.8 Line charts

Question 2

Module 2: Descriptive Statistics

Accepted Answer

2.1 Survey sampling 2.2 Measures of center 2.3 Measures of variability 2.4 Box plots 2.5 Histograms 2.6 Violin plots

Question 3

Module 3: Probability and Counting

Accepted Answer

3.1 Introduction to probability 3.2 Addition rule and complements 3.3 Multiplication rule and independence 3.4 Conditional probability and Bayes' Theorem 3.5 Combinations and permutations

Question 4

Module 4: Probability Distributions

Accepted Answer

4.1 Introduction to random variables 4.2 Properties of discrete probability distributions 4.3 Binomial distribution 4.4 Hypergeometric distribution 4.5 Poisson distribution 4.6 Properties of continuous probability distributions 4.7 The normal distribution 4.8 The Student's t-Distribution 4.9 The F-distribution 4.10 Chi-square distribution

Question 5

Module 5: Inferential Statistics

Accepted Answer

5.1 Confidence intervals 5.2 Confidence intervals for population means 5.3 Confidence intervals for population proportions 5.4 Hypothesis tests 5.5 One-sample hypothesis tests for population means 5.6 One-sample z-test for population proportions 5.7 Two-sample hypothesis tests for population means 5.8 Two-sample z-test for population proportions 5.9 Analysis of variance (ANOVA) 5.10 Chi-square tests for categorical variables

Question 6

Module 6: Linear Regression

Accepted Answer

6.1 Introduction to simple linear regression (SLR) 6.2 SLR assumptions 6.3 Correlation and coefficient of determination 6.4 Interpreting SLR models 6.5 Testing SLR parameters 6.6 Multiple regression 6.7 Categorical predictors and non-linear relationships

Question 7

Module 7: Time Series

Accepted Answer

7.1 What is a time series? 7.2 Time series patterns and stationarity 7.3 Moving average and exponential smoothing forecasting 7.4 Forecasting using regression

Question 8

Module 8: Monte Carlo Methods

Accepted Answer

8.1 What is a Monte Carlo simulation? 8.2 Building simulations 8.3 Optimisation and forecasting

Question 9

Module 9: Data Mining

Accepted Answer

9.1 What is data mining? 9.2 Data preparation 9.3 Analysing results 9.4 Supervised learning 9.5 Unsupervised learning

Question 10

Module 10: Ethics

Accepted Answer

10.1 Misleading statistics 10.2 Abuse of the p-value 10.3 Data privacy 10.4 Ethical guidelines

Question 11

Module 11: Appendix

Accepted Answer

11.1 t-distribution table 11.2 z-distribution table 11.3 Chi-squared distribution table

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