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.” This two-day instructor-led course 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 the analytic tradecraft knowledge, skills, and abilities needed by intelligence analysts to fulfill the Office of the Director of National Intelligence ODNI education requirements and the Secretary SecAF/Chief of Staff CSAF of the Air Force Strategic Master Plan SMP objective ISR.6.

NOTE: This course was formally called WKSP 0669 – Data Analytics for Senior Leaders.” This one-day instructor-led course is designed for Air Force personnel as a leadership course. Students explore the taxonomy of data analytics capabilities and the organizational maturity levels associated with its deployment. This strategic capability is surveyed through a systems approach to examine the implementation of data analytics competencies for various levels of the organization. Holistic considerations will be evaluated for how an organization can fully integrate data analytics into their business strategy by addressing the inevitable obstacles of implementing a more data-focused approach, to include challenges when data analysis-based conclusions cut against the organization’s conventional wisdom. A framework for creating a more data driven culture and the elements necessary to effect change in an organization will be discussed. Finally, attendees close the workshop by reviewing a case study from industry leaders to uncover best practices and replication opportunities for their organizations.

Provides an introduction to the fundamental principles of data management and the importance of the data management function in an integrated product team IPT. Topics covered include the following: Evaluating data requirements to achieve the goal of minimum essential being placed on contract. Ensuring the data being ordered are legally binding authorized Data Item Descriptions properly called out on the Contract Requirements List DD Form 1423. Coordinating the data order through a data review process to achieve an accurate order tailored down to fit the program. Planning for and developing a Government Concept of Operations for an Integrated Digital Environment IDE.

This course provides Airmen an opportunity to explore fundamental data analysis to manipulate data using Microsoft® Excel as a foundational analysis tool. 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 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.

One of the most challenging aspects of data analytics is communicating the results from hours of work to decision makers, shareholders or colleagues. This course provides Airmen an opportunity to effectively and efficiently communicate analysis results through charts using Microsoft Excel. Students will be introduced to basic human visual processing methodologies. By utilizing these visual processing methods, students are aided in the selection of chart type, use of color and formatting options to minimize audience effort in understanding data visualizations. Students will be able to immediately apply these visualization concepts in using data to tell a story to their intended audience. Students visual communication repertoire will be enhance, not only for data analysis.

This workshop focuses on maturing a data analytics capability from predictive to prescriptive. The emphasis is on using systems dynamics to develop process models utilizing data analytics methodologies to identify and exploit system constraints, decrease waste, and reduce variation. Topics include relational databases, introduction to programming, optimization, and advanced data analytics techniques. Students should be comfortable working with Microsoft Excel and have some mathematical aptitude. A recommended prerequisite is AFIT Data Analytics for the Rest of Us, although not required.

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.

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, 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.

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 course 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 experience with an industry database server suitable for the needs of large enterprises and small organizations. While WKSP 0702 focused on general SQL topics that a Data Scientist would need, WKSP 0703 goes more into the implementation, loading, management, and administration of relational databases.

This course provides Airmen with This course provides Airmen with both an overview of the Big Data analysis landscape and a working knowledge of the tools that are most useful in doing data analysis. It goes into the core components of Hadoop, namely its file structure and MapReduce querying language. Then it explores the low- level HBase database system with primarily CRUD capability. It explores in detail several query languages that sit on top of MapReduce and/or HBase namely, Apache Phoenix, Hive, and Pig. Some of the Hadoop data management tools such as Oozie, Sqoop, and Zookeeper are introduced to give the student an orientation to them.

Extracting knowledge from data requires the use of statistical tools and methods. One of the most powerful and readily available statistical analysis tools is the R language. 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. Additional R language capabilities of creating basic visual charts and graphs are also explored. Following this short course, students will be able to use R in RStudio to produce and replicate simple data analysis and plotting. Students will be able to immediately apply what they have learned within their organizations and be ready to further utilize RStudio in their data journey.

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 with an introduction to data science through basic Python coding skills. Students are shown the importance of data science and develop necessary statistical foundations to work with data and conduct basic analysis 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 machine learning and other advanced analytical techniques.

This course provides an introduction to machine learning utilizing Python/Jupyter Notebook as the primary software tool. Students will learn the fundamentals of artificial intelligence and how to apply it to systems to generate machine learning programs. These programs will automatically access and analyze data using varying statistical techniques to quickly and efficiently output numerical results. This is an advanced course on Data Science designed for students with both familiarity of Python and advanced statistics.

This course provides Airmen with an introduction to Natural Language Processing NLP and Neural Networks NN utilizing Python/Jupyter Notebook. Students will understand the fundamentals of natural language data and how to program systems to automatically ingest and analyze the information. Additionally, students will understand how to interconnect artificially intelligent systems to tackle complex data problems that require multiple computing programs to solve. This is an advanced course on data science designed for students with both familiarity of Python and machine learning.