At the end of this module, every participant is expected to understand the basic statistics in data analysis, with appropriate knowledge of data types, and identify methods and techniques that will be most suitable for each category of data at their disposal.
Curriculum
- 6 Sections
- 25 Lessons
- 16 Weeks
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- Module 1: Introduction to Data Analytics10
- 1.1a. Data Science vs. Data Analytics
- 1.2b. Data Literacy
- 1.3c. Data Types
- 1.4Introduction to Statistics I
- 1.5a. Meaning and understanding of statistics
- 1.6b. Descriptive statistics
- 1.7c. Inferential statistics
- 1.8Introduction to Statistics II
- 1.9a. Parametric Test (In-dept explanations) T-test Pearson correlation Regression Analysis etc.
- 1.10b. Non-Parametric Test (In-dept explanations) Chi-Square Spearman’s rank correlations Kruskal Wallis test etc.
- Data Analysis with IBM SPSS0
- Module 2: Setting Up Data in SPSS5
- 3.1a. Overview of SPSS
- 3.2b. Data Entry In SPSS
- 3.3c. Saving Data In SPSS
- 3.4d. Exploring Data using Descriptive statistics (Mean, Standard deviation, etc.) and exploratory data analysis (Bar Chart, Pie Chart, Histogram, Boxplot, Scatter plot, etc.)
- 3.5e. Analyzing your data using Inferential statistics I (Practical in SPSS) Identify the data Describing the data/Graph Question formulation Setting up hypothesis Checking for normality of the data Selecting appropriate test
- Module 3: Inferential Statistics II-Mean Comparison I (Practical in SPSS)3
- Module: Inferential Statistics III-Predictive Analytics (Practical in SPSS)4
- Module 4: Inferential Statistics IV-Mean comparison II (Practical in SPSS)4
