Working with Data Course Syllabus



The Working with Data course will be conducted over 3 consecutive days, covering 7 lectures on data analytics principles and techniques, 4 lab sessions in which participants get to acquire basic data analytics skills and training on the use of the R-Commander statistical package, plenary session for course participants to present and discuss their lab work and a MCQ-based assessment of training outcomes.

Participants are required to bring their own notebook/laptop computer for the lab sessions using R-Commander

(no coding is needed).


Unit 1.    Exploratory Data Analysis

1.1   What is Exploratory Data Analysis?

1.2    How to check for coding errors

1.3    How to tell extreme values (outliers) from coding errors

1.4   Importance of understanding study variables and their distributions

1.5   Types of missing values and how to handle them

Unit 2.      Processing and Summaring Data

2.1   Why the need to process and summarize data

2.2   Descriptive vs Inferential Statistical Methods

2.3    What to know about Descriptive Statistical Methods

2.4   Importance of knowing statistical distributions including the Normal Distributio

Unit 3.   Data Visualization

3.1    Why visualize data?

3.2    Techniques of visualizing data for Measurement and Non-Measurement data

3.3.  Deciding between Tables vs Graphs

3.4   How not to be misled by data visualizations

3.5   Usefulness of Dashboard and their do’s and dont’s

Unit 4.     Statistical Estimation and Hypothesis Testing

4.1    Understanding Statistical Estimation

4.2    Understanding the difference between standard deviation and standard error

4.3   Understanding the 95% Confidence Interval to derive a Statistical Estimate

4.4    Understanding the statistical concept of Sampling Distribution

4.5    Understanding the statistical concept and mechanics  of Hypothesis Testing

Unit 5.     Dealing with Measurement Data

5.1   Statistical concepts associated with Hypothesis Testing

5.2    Understanding Null Hypothesis, p-value and interpretation of test outcomes

5.3    Statistical tests involving 2-group measurement data

5.4    Statistical tests involving multi-group measurement data

5.5   Non-parametric tests for measurement data

Unit 6.    Dealing with Non-Measurement Data

6.1   Understanding the concept of statistical association

6.2   When to use the chi-square test for statistical association

6.3   When to apply the chi-square test with continuity for correction

6.4    Fisher’s Exact Probability Test for very small sample size

6.5   Dealing with matched data – application of McNemar’s Test

Unit 7.     Statistical Correlation and Regression

7.1    What is predictive analytics?

7.2    Predicting one outcome variable from a predictor variable – simple linear regression

7.3    Predicting one measurement outcome variable from several predictor variables – multiple linear regression

7.4    Predicting one binary outcome variable from several predictor variables – multiple logistic regression

7.5    Misuses of regression techniques in predictive analytics

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