Data Analytics with Statistics

Course Overview

Welcome to our course Data Analytics with Statistics! 👋

Note

Note that this schedule will be updated as the seminar progresses.

Nr.	Topic	Literature	Slides	Links	Code	Activity
1	Introduction
2	Data driven decision making		📑			Lecture
4	Data Science Lifecycle Overview		📑			Lecture
5	Use case identification		📑			Self-study
6	Frame the Problem		📑			Self-study
7	Identify Variables		📑			Self-study
8	Define Metrics		📑			Self-study
9	Data
10	First Data Analysis	📚	📑		💻	Lecture
11	Data basics	📚	📑	☑️		Lecture
12	How to obtain data		📑			Optional
13	Data wrangling: Pandas lab	📚	💻			Application Exercise
14	Data analysis: Survey lab		💻			Optional
15	Study Design
16	Population and sample	📚	📑	☑️		Self-study
17	Sampling methods	📚	📑	☑️		Self-study
18	Experiments	📚	📑	☑️		Self-study
19	Observations	📚	📑	☑️		Self-study
20	EDA with categorical data
21	Loans data	📚	📑		💻	Self-study
22	Contingency tables	📚	📑		💻	Lecture
23	Contingency tables with proportions	📚	📑		💻	Self-study
24	Simple bar chart	📚	📑		💻	Lecture
25	Stacked bar plot	📚	📑		💻	Lecture
26	Standardized bar plot	📚	📑		💻	Lecture
27	Pie chart	📚	📑		💻	Self-study
28	EDA with numerical data
29	Scatterplot	📚	📑		💻	Self-study
30	Dot plot mean median and mode	📚	📑		💻	Lecture
31	Histogram	📚	📑		💻	Lecture
32	Box Plot	📚	📑		💻	Lecture
33	Comparing numerical data across groups	📚	📑		💻	Lecture
34	Variance and standard deviation	📚	📑			Lecture
35	Kernel density plot	📚			💻	Self-study
36	Robust statistics and transformations	📚	📑			Self-study
37	Models
38	Statistical Learning, Machine Learning		📑			Lecture
39	Types of Models		📑			Lecture
40	Linear Regression models
41	Correlation	📚	📑	☑️	💻	Lecture
42	Sales and ads		📑		💻	Lecture
43	Mean squared error		📑			Application exercise
44	Fitting a line and residuals	📚	📑	☑️	💻	Self-study
45	Least squares regression	📚	📑		💻	Self-study
46	R squared	📚	📑		💻	Lecture
47	Categorical predictors with two levels	📚	📑		💻	Lecture
48	Outliers	📚	📑			Self-study
49	Multiple predictors regression 1	📚	📑	☑️	💻	Lecture
50	Multiple predictors regression 2				💻	Lecture
51	Multiple predictors regression 3				💻	Lecture
52	Linear Regression with Data Splitting
53	Regression example happier		📑		💻	Self-study
54	Main model challenges		📑			Self-study
55	Data splitting		📑		💻	Self-study
56	Sales prediction		📑		💻	Self-study
57	Sales prediction with data splitting				💻	Lecture
58	Advanced Linear Regression models
59	Regression splines	📚			💻	Optional
60	Generalized additive models	📚			💻	Optional
61	Adjusted R squared	📚	📑			Optional
62	Regression diagnostics				💻	Optional
63	Model Selection Methods
64	Model selection methods	📚	📑			Optional
65	Implicit model selection	📚			💻	Optional
66	Lasso regression	📚			💻	Optional
67	Filter model selection	📚			💻	Optional
68	Wrapper model selection	📚			💻	Optional
69	Classification models
70	Classification		📑			Lecture
71	Precision recall and F1 score	📚	📑			Lecture
72	ROC Curve and AUC	📚	📑			Lecture
73	Probability of an event	📚	📑		💻	Self-study
74	Logistic regression in Python				💻	Lecture
75	Statistical Inference
76	introduction	📚	📑		💻	Optional
77	Cases and confidence	📚	📑			Optional
78	Decision errors	📚	📑			Optional
79	Single proportion	📚	📑			Optional
80	Single proportion hypothesis testing	📚	📑			Optional
81	Two proportions	📚	📑			Optional
82	Two proportions hypothesis test	📚	📑			Optional
83	Two proportions mammogram	📚	📑			Optional
84	Two way tables	📚	📑			Optional
85	Two means	📚	📑			Optional
86	Inference regression	📚	📑			Optional
87	Probability and Bayes
88	Probability and Bayes	📺	📑			Self-study
92	Optional content
93	Data storytelling					Optional
94	Generative AI		💻			Optional