In this module, we will focus on importing different file formats such as .txt, .csv, and .xlsx into SAS. You'll learn how to effectively use the proc import feature to handle dataset imports and explore the distinctions between traditional data step importing methods and using proc steps for greater efficiency.

In this module, we will explore the essential building blocks of SAS syntax, focusing on how the data step and proc step serve different functions in the software. You'll learn to write accurate code following SAS rules and gain insights into how SAS compares to popular languages like R and Python by manually creating data across all three platforms.

In this module, we will dive into advanced data manipulation techniques using SAS. You will explore dataset options, delimiters, and methods for reading dates in data. Additionally, you'll learn how to create new variables, filter observations, and use SAS conditional logic effectively. Topics like automatic variables, sorting, merging datasets, and data cleaning techniques will also be covered to improve your data management skills.

In this module, we will revisit importing techniques, focusing on how to import an SPSS file into SAS. You'll gain practical insights into dealing with different dataset formats and understand how to use the proc import process to handle various types of files.

In this module, we will cover the basics of input types and informats, showing you how to read and manage different data structures. You'll learn about list and column input, and how to apply informats for more accurate data reading. Additionally, you will discover how to create user-defined formats to tailor data analysis in SAS.

In this module, we will delve into the use of arrays in SAS. You'll learn how to recode and construct new variables using arrays, which can significantly simplify your code when working with large datasets. Arrays allow for efficient handling of repetitive tasks, making your data processing faster and more effective.

In this module, we will explore a range of SAS functions that are essential for data processing. You’ll learn about functions that help clean and transform datasets, such as RAND for random sampling, LENGTH for managing large datasets, and COMPRESS for removing unwanted characters. Additionally, we'll cover how to convert data types using the INPUT and PUT functions for more accurate analysis.

In this module, we will focus on advanced SAS programming techniques, particularly how to vertically combine raw data files. You'll learn flexible methods for managing multiple datasets, helping you to streamline processes and increase efficiency in handling large-scale data projects.

In this module, we will focus on creating visual representations of data, such as scatter plots and bar graphs. You'll learn how to use SAS to create these basic visualizations and understand their importance in presenting data analysis results clearly and effectively.

In this module, we will focus on statistical analysis using SAS. You'll learn how to perform independent samples T-tests and Chi-Square tests, and interpret their results. These statistical techniques will help you apply rigorous data analysis methods to real-world datasets, providing you with valuable insights.

In this module, we will dive into regression analysis, covering both linear and multiple regression. You'll start with a refresher on regression concepts before performing these analyses using SAS. The focus will be on applying regression techniques to analyze complex relationships between variables in your datasets.

In this module, we will apply the knowledge gained throughout the course in healthcare-related case studies. You'll integrate concepts such as the data step, data manipulation, and statistical analysis, providing practical applications of your learning to solve real-world problems.

In this module, we will introduce the fundamentals of SQL in SAS. You'll learn SQL syntax and how to write queries using SELECT and WHERE clauses. Additionally, you’ll explore summary functions and CASE logic to perform more sophisticated data queries.

In this module, we will cover how to join datasets using SAS SQL. You'll learn how to perform inner, left, right, and full joins to merge data from multiple tables. This will enable you to manage complex data relationships effectively within your SQL queries.

In this module, we will explore how to work with tables using SAS SQL. You’ll learn how to create tables, alter their structure, and insert rows into them. These skills will help you manage datasets more effectively within a SQL environment.

In this module, we will focus on practical applications of SAS SQL. You'll learn how to compare tables, identify duplicates, and customize your table sorting methods. Additionally, you’ll explore how to update tables under specific conditions, improving your data management strategies.

In this module, we will cover the fundamentals of SAS indexes, focusing on how and when to use them for optimizing data queries. You'll explore PROC datasets and WHERE expressions to manage indexed datasets, which will significantly improve the efficiency of your data processing.

In this module, we will explore the fundamentals of SAS macros, focusing on creating and using macro variables to streamline repetitive coding tasks. You’ll also learn important macro functions like %Index and %Scan, and how to debug and store macros for better data management and flexibility.

In this module, we will introduce predictive modeling using logistic regression in SAS. You’ll explore the business applications of predictive models, identify common challenges, and learn the steps involved in building an effective logistic regression model.

In this module, we will focus on the preliminary steps of building a predictive model. You’ll learn how to generate a problem statement, conduct a data audit, and perform univariate and bivariate analyses to gain deeper insights into your dataset before building a predictive model.

In this module, we will cover advanced data preparation techniques for predictive modeling. You’ll learn how to handle missing data, perform variable clustering, and subset selection, and calculate parameter estimates to ensure your model is optimized for accuracy.

In this module, we will explore the key evaluation metrics for predictive models, focusing on tools like the ROC curve and decile calibration plots. You’ll also learn how to score validation datasets and apply feature engineering to refine and improve your predictive models.

In this final module, we will address the top five most commonly asked questions about SAS certification. You'll gain insights into exam preparation and understand the challenges you might face during the certification process, equipping you with strategies to succeed in becoming SAS certified.