Yuvijen
Business Analytics for Decision Making
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I : R and Python
Welcome
Authors
Live Analytics Lab
Syllabus
I : R and Python
R Basics
1
Overview of R and R Studio
2
Data Structures
3
Functions
4
Statements and Looping
Python Essentials
5
Understanding Python
6
Data types
7
Operators
8
Numpy
9
Pandas
10
Scipy
II : Foundations of Business Analytics
Introduction to Business Analytics
11
Understanding Business Analytics
12
Types of Business Analytics
III : Descriptive Analytics
Data Collection and Preparation
13
Data Collection Methods
14
Sampling Techniques and Sample Size Determination
15
Data Cleaning and Preparation
Descriptive Analytics
16
Introduction to Descriptive Analytics
17
Measures of Central Tendency
18
Measures of Dispersion
19
Measures of Skewness
20
Measures of Kurtosis
Data Visualization Using R Graphics and R Commander/R Deducer
21
Introduction to Data visualization
22
Graphical Presentation – Scatter plot, Histogram
23
Diagrammatic Presentation – Bar Charts
24
Pie charts 2D and 3D
25
Box plots
26
Line plots
IV : Inferential Statistics
Probability and Estimation
27
Introduction to Probability
28
Probability Distributions
29
Sampling Distributions and the Central Limit Theorem
30
Confidence Intervals and Estimation
Test Selection Framework
31
Introduction to Diagnostic Analytics
32
Parametric vs Non-Parametric Tests
33
Choose your Test for Data Analysis
Nominal Tests
34
Introduction to Nominal Tests
35
Binomial Test
36
Mc Nemar’s Test
37
Cochran’s Q test-post-hoc test
38
Chi-square test
39
Phi-Coefficient of Correlation
Scale Tests (Parametric Tests)
40
Introduction to Parametric Tests
41
T-tests
42
One-Sample T-Test
43
Two-Sample T-Test (Independent Samples)
44
Paired-Samples T-Test
45
ANOVA
46
One-Way ANOVA
47
Two-Way ANOVA
48
Post-Hoc Tests for ANOVA
49
Repeated Measures ANOVA
50
Karl Pearson’s Coefficient of Correlation
Ordinal Tests (Non-parametric Tests)
51
Introduction to Non-parametric Tests
52
Wilcoxon Signed Rank Test
53
Mann-Whitney U Test
54
Kruskal-Wallis Test
55
Friedman Tests and related Post-hoc Tests
56
Spearman Rank Correlation
V : Predictive Analytics
Regression Analysis
57
Introduction to Regression Analysis
58
Simple Linear Regression
59
Multiple Linear Regression
60
Regression Diagnostics and Model Evaluation
References
I : R and Python
Module I · 10 Topics
R Basics
1
Overview of R and R Studio
2
Data Structures
3
Functions
4
Statements and Looping
Python Essentials
5
Understanding Python
6
Data types
7
Operators
8
Numpy
9
Pandas
10
Scipy
Syllabus
1
Overview of R and R Studio