SDSU's Data Science and Analytics Professional Bootcamp is offered in partnership with HackerU, a leader in digital knowledge transfer, technological solutions, and cyber services. With this unique program, you'll learn how to become an ambassador that can speak on behalf of data as your organization’s single source of truth.

About the Program

Colored data chart.

Data is at the heart of nearly every aspect of our modern lives. From smartphones and social media to mobile banking and cybersecurity, our world is growing increasingly digital - and that means an increased reliance on data, data integrity, and data science. The job market has responded in kind - according to the Bureau of Labor Statistics, data science is one of the nation’s fastest-growing occupations, with an anticipated growth rate of 31% through 2029.

However, the data landscape is evolving. The workforce needs more people who can work responsibly with data, leverage the power of artificial intelligence, and build visualizations that are accessible to everyone to help the business achieve its goals. Technological advancements are moving quickly, and, quite simply, there are not nearly enough people equipped with the skills needed to fill the open data professional positions.

With SDSU's Data Science and Analytics Professional Bootcamp, you can become the type of professional that organizations need to make smarter, data-driven business decisions. This program is designed to take you from little or no experience to a ready-to-hire data professional by providing you with the most up-to-date skills and hands-on experience that companies look for.

 
Take advantage of a market-driven curriculum that's designed to close the skills gap and help you gain an edge in a competitive, rapidly-growing job market.

Prepare for Industry Certifications

This program can help you prepare for an exciting career as a data professional by covering what you’ll need to know to sit for some of the industry’s most recognized exams, including: Microsoft Certified:

  • Data Analyst Associate
  • IBM Data Analyst Professional Certificate
  • IBM Data Science Professional Certificate

Program Structure

Introduction Course

Before you enroll in the complete bootcamp program, you’ll enroll in our 30-hour Introductory Course, which is designed to cover the fundamentals of data analytics and science and give you a sample of what you’ll cover in the complete bootcamp. You’ll finish the intro course with at least three completed data projects, including topics in Excel, statistics, data analytics, data wrangling, SQL, scripting languages, machine learning, and artificial intelligence to help you jumpstart your professional portfolio.

Upon completion of the introductory course, you’ll take a summary exam and evaluate your progress to determine whether the program and industry are suitable for you.

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Professional Bootcamp

The main portion of the bootcamp is divided into three parts. The first group of courses will cover data-driven storytelling and how to use statistics and probability to solve business problems. The second part focuses on data wrangling and analytics, which includes cleaning data, structuring data, and transforming data to prepare it for visualization or for use in a machine learning model. The third section focuses on taking those foundational data skills and using them to solve the most common business problems using programming skills, machine learning, and applied artificial intelligence.

Part 1: Data-Driven Storytelling

Sample stat pie charts.

SQL and Databases
This course provides you with an introduction to SQL, a popular language used to query databases. Using SQL, you will import data into databases, query data, join data together, filter and sort data, create views, and export data. Further, this course introduces you to database design and teaches you how to manage your own database.

Statistics and Probability
This course aims to enlighten you on how statistics and probability are used in business decision-making. This course aids you in building a strong foundation in descriptive statistics, conditional probability, and advanced modeling techniques. We use Microsoft Excel to provide a practical application to theoretical discussions. You develop the ability to approach real-world problems from an analytical perspective with confidence.

Data Storytelling
In this course, you discover the power of a story and how to develop a story arc around your data goals. Successfully communicating data insights depends on the audience of stakeholders and the story points that speak to their needs and expectations. You continue to keep a data story thread throughout the entire data wrangling adventure as you frame your data goals with purpose.

Milestone Project 1: Building and Presenting Data Stories
This milestone project allows you to explore your skills in the areas of statistics, Excel, SQL, and data storytelling. You have the opportunity to demonstrate your ability to clean and manipulate a dataset. Additionally, you perform advanced statistical analysis on the data using summary statistics, linear regression, and modeling. Finally, you put your visualizations and insights into a coherent data story to present to your classmates. The instructional team formally reviews the data analytics milestone project. You then incorporate the project into your GitHub portfolio.

Part 2: Data Wrangling and Analytics

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Python Programming
In this course, you explore the fundamental concepts of programming and learn how to structure their analyses. Topics include core programming concepts such as expressions, data types, variables, functions, loops, and arrays. Practice your coding skills through building highly structured and maintainable code using Jupyter notebooks.

Data Wrangling
Develop core data wrangling skills by expanding your Python programming skills. Explore a series of data analysis processes from sourcing, curating, and importing data, exploratory data analysis, data cleansing techniques, and data visualization techniques. Next, expand your toolbox by using industry standard software to automate data wrangling processes.

Visual Communications
Explore visual dynamics and principles to produce effective data visualizations that show the most important parts of data to stakeholders in a clear and simplified way.

Advanced SQL Programming
Building upon the skills you gained in the SQL and Databases course, this course extends your skill in SQL programming and covers topics such as stored procedures, triggers, cursors, and query optimization. You also develop ETL scripts and data pipelines combining the use of SQL and Python.

Milestone Project 2: Data Integration, Preparation, Reporting, and Presentation
This milestone project focuses on developing your ability to attain, transform, investigate, and present data throughout a data project life cycle. Demonstrate your ability to build data pipelines and wrangle data into a usable format for downstream data visualization and analytics. Present your reports and findings to classmates and then incorporate the project into your GitHub portfolio. The instructional team reviews projects in this milestone.

Part 3: Data Science & Business Intelligence

Data charts infographic for big data.

Business Intelligence
Build upon visual communication concepts by learning to use popular industry business intelligence tools to create insightful analyses and visualizations. Develop and apply best practices for reporting, graphs and charts, and dashboards in a way that can be applied in any business intelligence application.

Big Data
In this course, examine core concepts and methods used for big data and IoT, including characteristics of big data, data warehousing, data lakes, data virtualization, and cloud-based data infrastructure services. Build upon your previous knowledge of Python by using PySpark to access big data and create analytics models.

Machine Learning
Analyze a variety of use cases in a business context for determining appropriate machine learning methods to apply. Through a series of Python lectures and labs and using Jupyter Notebooks, investigate and apply supervised and unsupervised machine learning algorithms, including classification, clustering, association rules, and time-series forecasting. Next, explore several advanced methods, including natural language processing, neural networks, and deep learning.

Applied AI
After the introduction to machine learning-created AI in the previous module, explore a variety of pre-packaged AI cloud services offered by leading providers like Microsoft, Amazon, and Google. AI provides a more targeted data analytics experience, as it produces a greater amount of data insights through various applications of computer vision, speech recognition, natural language processing, and robotics.

Capstone Project: Delivering Insights and Presentations
Meet the challenge of presenting your data insights and visualizations clearly and with ease to diverse types of stakeholders. Learn how to organize data visualizations around a goal and integrate a story arc to keep your audience engaged. Work on your own individual project that incorporates the skills and knowledge you gained throughout the program. This milestone culminates in a final project presentation that capitalizes on your data analysis, data storytelling, and presentation skills. Finally, share your work by uploading your completed project to your GitHub portfolio.

Analytics infographic.

Why Choose SDSU’s Full-Stack Development Bootcamp?

  • 406 virtual in-class hours of instruction
  • 12 specialized courses
  • 3 test-preparation workshops to help you prepare for industry certification exams
  • 3 milestone projects to help you reinforce the lessons and build your portfolio
  • Advanced remote learning technology allows you to collaborate and interact with your instructors and classmates
  • Dedicated career service workshops to help you build your résumé, learn how to interview, and develop your network
  • Live lessons with industry leaders with an insider’s understanding of the fast-paced field of full-stack development

Want to Learn More?

For more information, please visit digitalskills.sdsu.edu or call (619) 839-3030.

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