Course description

This comprehensive course is designed to introduce learners to Stata, a powerful statistical software widely used in data analysis across various fields. The course covers the essentials of Stata, from installation and understanding its interface to performing complex data wrangling, cleaning, and statistical analysis tasks. Learners will learn how to manage datasets, manipulate variables, handle missing data, and perform exploratory data analysis using Stata's extensive features. By the end of the course, learners will be proficient in using Stata for a wide range of data analysis tasks, making you well-equipped to apply these skills in real-world scenarios.

Course objectives

By the end of this course, you will be able to:

• Install Stata software and understand its user interface, including the Command Window, Data Editor, Variables Window, and more.
• Identify and work with different data types in Stata, such as numeric, string, and date/time formats.
• Load/export data from/to various sources, save, and explore datasets.
• Recognize and handle errors in Stata effectively using control statements and error-handling functions.
• Utilize conditional and repetitive control structures, such as if, if ... else, if ... else if ... else, foreach, forvalues, and while loops, to automate tasks and make scripts more efficient.
• Perform essential data wrangling tasks, renaming and generating variables, and recoding data for analysis.
• Select and manipulate specific variables and observations to create focused subsets of data for detailed analysis.
• Identify and resolve common data quality issues such as missing values, inconsistent data types, duplicates, and outliers.
• Merge and concatenate datasets effectively, using various join techniques to combine data from multiple sources.
• Conduct exploratory data analysis (EDA) including frequency tabulation, and descriptive statistics to uncover insights from data.

Target groups

The course is designed for:

• Data Analysts: Professionals looking to enhance their data analysis skills using Stata for more efficient and accurate data manipulation and visualization.
• Researchers: Individuals who need to process and analyze large datasets as part of their research projects.
• Statisticians: Statisticians who want to deepen their understanding of statistical methods and apply them using Stata.
• Students: Those studying data science, statistics, or related fields who require a solid foundation in Stata programming for their coursework and projects.

Course requirements

To get the best out of the course, the following will be required:

• Dedication: This course demands a significant commitment to learning and practice, and a serious level of dedication and concentration throughout the workshop sessions.
• Problem-Solving Skills: Be prepared to tackle complex data challenges.
• Basic Programming Knowledge: While not strictly necessary, prior programming experience will be beneficial.

Course outline

1. Introduction to Stata for Data Analysis
   1. Installation and Stata Interface
      1. Install Stata
      2. Stata Interface Overview
   2. Stata Data Types
      1. Introduction
      2. Numeric
      3. String
      4. Date and Time
   3. Data Sets and Matrices
      1. Introduction
      2. Load Data Sets
      3. Save Data Sets
      4. Commands for Viewing Datasets
      5. Introduction to Matrices (Optional)
   4. Basic Error Handling
      1. What is Error Handling
      2. Importance of Error Handling
      3. Error Handling Functions
   5. Control Statements
      1. Introduction
      2. Conditional Statements
   6. Repetitive Structures
      1. Introduction
      2. foreach
      3. forvalues
      4. while Loops
      5. Loop Control Statements
2. Data Wrangling and Cleaning Part I
   1. Creating and Importing Data Sets
      1. Introduction
      2. Survey Data Sets
      3. Import Data Sets
      4. Export Data Sets
   2. Working with Variables
      1. Common Data Frame Commands
      2. Rename Variables
      3. Assign Variable Labels
      4. Generate New Variables
      5. Specify Where to Insert the Generated Variable
      6. Generating Multiple Variables
      7. Replace the Values of a Variable
      8. Recode Categorical Variables
      9. Combine Categories
      10. Transform Continuous into Categorical Variable
      11. Assign Ranks to a Variable
   3. Extended Generation of Variables
      1. Generate Variables by Row-Wise Operations
      2. Writeado Program to Generate Variables
      3. Generate Dummy Variables
      4. Split Strings into Separate Variables
      5. Multiple Response Questions
      6. Concatenate Multiple Variables into One
   4. Creating Subsets of Data Sets
      1. Introduction
      2. Select Variables
      3. Select Observations
      4. Select Both Observations and Variables
      5. Drop Variables
      6. Drop Observations
      7. Randomly Sample a Data Set
      8. Split Data Set on Categorical Variables
3. Data Wrangling and Cleaning Part II
   1. Changing the Appearance of Data Sets
      1. Sort Data Sets
      2. Relocate Variables
      3. Transpose Data Sets
      4. Stack Data Set Variables
      5. Reshape Data Sets
   2. Detecting Data Quality Issues - Part 1
      1. Data Set Overview
      2. Inconsistent Data Type
      3. Missing Values
      4. Apply .ado Programs to Data Set
      5. Detect and Replace Outliers
      6. Handling Duplicates
      7. Scale Numerical Variables
   3. Detecting Data Quality Issues - Part 2
      1. Extract Substrings
      2. Transform the Case of Data Set Values
      3. Remove White Space
      4. Correct Misspelled Words
      5. Replace Occurrence of Substrings
      6. Replace Strings Using a Named Vector
      7. Remove Specified Characters
      8. Drop Rows or Columns that Contain a Specified Substring
   4. Combining Data Sets (Concatenate and Merge)
      1. Concatenating Data Sets
         1. Append Rows
         2. Append Columns
      2. One-to-One Merging (Joins)
         1. Inner Join
         2. Outer (Full) Join
         3. Left Join
         4. Right Join
      3. Merging More Than 2 Data Sets
      4. One-to-Many Merging
      5. Many-to-One Merging
      6. Many-to-Many Merging
4. Exploratory Data Analysis
   1. Tabulation of Frequencies
      1. Introduction
      2. One Way Frequency Tables
      3. Two Way Frequency Tables
         1. Calculating Frequencies
         2. Adding Row Percentage to Two Way Contingency Tables
         3. Adding Column Percentage to Two Way Contingency Tables
   2. Descriptive Statistics
      1. Introduction
      2. Tables of Statistics
         1. Minimum, Maximum, Mean and Standard Deviation
         2. Minimum, Maximum, Median and Inter-Quartile Range
         3. Multiple Categorical Variables
         4. Multiple Quantitative Variables
      3. Calculating p-values
   3. Analysis of Multiple Response Questions
      1. One Way Frequencies
      2. Calculate Associations
5. Way forward