This course provides Airmen with an introduction to data analytic terms and concepts. Students are shown how to acquire data, conduct basic analysis, and leverage data to better inform data-driven decision making. Although this course is designed as a stand- alone course, it will also serve as a springboard to more advanced courses on data management and data analysis.

NOTE: This course was formally called WKSP 0658 – Data Analytics for the Rest of Us.” The two-day instructor-led course, or the 4.5 days VIRR option, is designed for Air Force personnel as a foundational hands-on course. Students will apply Data Analytics techniques to solve case-based problems in a laboratory environment. Students will gain a basic understanding of conducting statistical analysis using Microsoft Excel® and several representative case studies/exercises on using data analytics to describe, diagnose, and predict various operations management challenges. Students will learn how to manipulate a large data set, including d descriptive statistics, pivot tables, charting and visualization techniques, formulas, text manipulation, data tables, concatenation, and parsing. Graduates will obtain immediate tools they can use to increase their job performance and Data Analytics acumen.

This course provides Airmen an opportunity to explore fundamental data analysis to manipulate data using Microsoft® Excel as a foundational analysis tool. An Airman’s data analytics journey ultimately arrives at this crucial stage where the data in use will need proper formatting and reorganizing through a process known as data manipulation. Following this short course, students will be able to navigate through data contained in spreadsheets more efficiently and apply time-saving techniques to data sets through the use of several special built-in functions, user- created formulas and logical functions. Students will be able to immediately apply what they have learned within their organizations and be confident in taking the next steps in their data journey.

This course provides Airmen a deeper understanding of the importance and techniques of cleaning and validating raw datasets in preparation of extrapolating information from them. Airmen will follow a single data source through the cleansing process, with each module providing a specific error and solution, to gain understanding of the tools and techniques to clean data prior to analysis. The course culminates in a direct contrast of the analysis of a raw and cleaned dataset. Following this short course, students will be able to readily recognize errors and weak points in data entry and apply cleansing techniques to ensure accurate and precise analytical outcomes from more advanced data manipulation efforts.

Manipulating data for analysis can be extremely time consuming and rigorous. This course provides Airmen an opportunity to quickly and easily manipulate data using Microsoft Excel as a foundational analysis tool. An Airmens data analytics journey ultimately arrives at this crucial stage where the data in use will need proper formatting and reorganizing through a process known as data manipulation. Following this short course, students will be able to quickly summarize data sets through the use of pivot tables modified by slicers, filters, and dynamic formatting. Students will be able to immediately apply what they have learned within their organizations and be confident in taking the next steps in their data journey.

Take the next step in your Data Analytics journey, applying the tools you mastered in Data Analytics Tools and Techniques. This workshop will build on what you have already learned to solve a real world problem you may be facing in your organization today. In an immersive five day workshop, or during 10 half-days (VIRR), you will work in dynamic teams using industry tools and statistical methods to uncover keen insights about an assigned case while further adding to your Data Analytics toolkit. You will learn how to diagnose a problem using data analytics while producing a solution, predicting the outcome and finally visually communicating your solution with confidence. Tools used in this workshop are: Microsoft Excel, Access, Structured Query Language SQL, Tableau, R and R-Studio. Graduates of this workshop will be prepared to lead data analytics projects within their organizations, helping foster a more data-driven culture.

Data Analytics is rapidly becoming one of the most sought after capabilities within the professional world. It has been said that Knowledge is Power and those who can manage and make sense of the 2.5 billion gigabytes of data we amass everyday can harness that power. Strategic data analytic capability has yielded phenomenal results throughout numerous industries to include the Federal Government and the DoD at large. Presidential Elections, Major League Baseball Teams, Major Online Retailers and even Defense Contractors… have all successfully used Data Analytics to gain a competitive advantage, cut operation and production costs, speed up delivery, generate precise forecasts and ultimately make better decisions. This workshop will explore various tools and techniques required to achieve the results garnered by leading edge, high-performing organizations. We also discuss intermediate statistical techniques and how to leverage them utilizing popular Data Analytics tools commonly found on our computers today: Microsoft Excel, Access, Structured Query Language SQL, Tableau, R and R-Studio. Graduates of this workshop will be prepared to apply what they’ve learned to take an active role in their organization’s data analytics, directly contributing to data-driven de decision making.

This course provides Airmen with an introduction to different types of data and associated architectures used for storage and retrieval for different purposes. It covers relational and non- relational databases, database design, choice of database, and issues associated with Big Data. Although this course is designed as a stand-alone course, most of the topics will be studied in greater depth in other courses in the DES tract.

This course provides Airmen with a working knowledge of the SQL relational database system. This course addresses database planning and design, data loading and management, and simple to advance SQL querying. It is taught using the MySQL variant of SQL. This course will give the data scientist sufficient knowledge and competency to use SQL databases for data storage, and SQL queries to both extract answers to certain types of questions and extract data for data science activities beyond SQL’s capabilities.

This course provides Airmen with a working knowledge of the administration and management of SQL relational database systems. This course addresses issues of information governance, administering roles and permissions, the concept of ETL (Extract, Transform, and Load), database automation, data warehouses versus transactional databases, and data quality tools. Microsoft’s SQL Server will be used through the course, providing students with experience with an industry standard database server suitable for the needs of large enterprises and small organizations.

This course provides Airmen an opportunity to explore fundamental data analysis using R in the popular RStudio environment. Basic R language syntax and concepts are introduced and used to import data and compute descriptive statistics. Visualizing this data via common plot types will also be explored. Following this short course, students will be able to use R in RStudio to produce and replicate simple data analysis and plotting.

This course provides Airmen an opportunity to explore data analysis and visualization using the powerful R language in RStudio. The concept of packages is introduced, a feature that extends and compliments R's capabilities, and is used for advanced data manipulation and analysis. Additional new capabilities for creating complex visual charts and graphs are also explored. Following this course, students will be able to manipulate large datasets and utilize strong data visualization tools.

This course provides Airmen with an introduction to data research through management of a project under a life- cycle plan. Students will understand the components of a project and learn how to see a project to completion from project initiation through project closing. Additionally, students will learn how to evaluate the end state of a project to plan for future approaches or prepare for any future issues. Although this course is designed as a stand-alone course, it will also serve as a springboard to more advanced courses on machine learning and other advanced analytical techniques.

This course provides Airmen a look into the multi-disciplinary aspect of data research. Students will discuss how to apply various analytical tools and techniques to be more informed decision makes and to solve complex problems found in the exponentially expanding data collected by organizations today. Students will also go more in-depth in data exploration, data management, linear programming and optimization to build models within Excel to find or define the solution space. They will gain a basic understanding of the theoretical assumptions of these models and the abilities and limitations such models inherently provide. At the end of the course students will get an introduction to R-studio and programming in R. This is an intermediate course on data analysis tools and techniques for students familiar with Excel and limited understanding of object oriented programming.

This course provides Airmen an advanced look at the statistical theory used in data research analysis utilizing R-Studio programming. Students will get an initial refresher in the essentials of coding in R before delving into data summarization, statistical probability, data visualizations, inferential statistics and stochastic modeling within R. Students will understand how to apply the results of the statistical models/programs to make more informed and impactful decisions. This is an advanced course in data research methods for students familiar with the basics of statistical theory within data analysis and programming in R.

This course provides Airmen an introduction and detailed look at the specific realm of data research known as statistical learning. Students will understand supervised and unsupervised learning, when to use linear regression or classification regression. Additionally resampling methods and model validations are discussed and implemented. The regularization of model selection through subset, dimensional reduction and ridge regression are examined. Finally, Tableau visualizations are implemented upon the statistical learning results to tell a story to the decision makers and stock holders. This is an advanced course in data research for students familiar with statistical theory and modeling methods. Additionally students should have experience with R-studio.

This course provides Airmen an opportunity to explore foundational Python programming skills in Jupyter notebook. An Airmen’s data analytics journey ultimately arrives at this crucial stage where the data in use will need proper formatting and reorganizing through a process known as data manipulation. Following this short course, students will be able to import and export data in Jupyter notebook and access a variety of package tools to begin working with data. Additionally, students will be able to conduct basic calculations and manage data within arrays using the NumPy package tool.

This course provides Airmen an opportunity to explore Python data frame manipulation and visualization within Jupyter notebook. As feature exploration becomes important in analyzing data, a data frame helps in maintaining the structure of the data within a multidimensional setting. Following this short course, students will be able to create and manipulate data frames using the Pandas tool package. Additionally, students will be able to create basic visualizations to depict relationships and key features of datasets.

This course provides Airmen an opportunity to explore technical skills beyond basic analytic tools and gain a solid foundation in Python Programming. Python is a programming language used for automation, data analysis, and problem-solving. Through hands-on exercises, students will learn core programming concepts, data manipulation techniques, and visualization capabilities to automate tasks and solve real-world problems. Additionally, this course also introduces Generative AI as a tool to streamline code generation and increase productivity. By the end of this course, students will be ready to apply Python to operational challenges and experiment with AI-driven tools in their workflow.

Introduce students to the methods and techniques of data science using the Python programming language. This workshop will train students to interact with stakeholders and domain experts, formulate a data science task, organize and clean data, choose the appropriate data science tool based on the required task, and present findings to aid in decision-making.

Introduce students to various intermediate level concepts of data science with a focus on machine learning methods. This workshop will train students on a range of topics including dimensionality reduction and expansion, unsupervised learning, plotting decision spaces, and Neural Networks with Keras, including Convolutional Neural Networks and Recurrent Neural Networks.

Introduce students to various advanced level concepts of data science, especially in regard to application. This workshop will train students on a range of topics including Recommender Systems, Reinforcement Learning, Natural Language Processing, Transformers, Generative AI, and AI Bias and Ethics.