In their first 30 hours, learners are introduced to core data science and analytics topics, such as computing basics and the data life cycle. Course participants will have the opportunity to learn the basics of a Jupyter notebook and interact with a Titanic dataset. They will pull the project materials from a GitHub repository and walk through it in a Google Colaboratory notebook. Learners can then look forward to completing three more projects throughout the remainder of the program to add to their personal portfolios.
Learners are introduced to SQL, which is a popular language used to query databases. Using SQL, learners will import data into databases, query data, join data together, filter and sort data, create views, and export data. Further, learners are introduced to database design and learn how to manage their own databases.
This course aims to enlighten learners on how statistics and probability are used in business decision-making. This course aids the learner in building a strong foundation in descriptive statistics, conditional probability, and advanced modeling techniques. Microsoft Excel is used to provide a practical application to theoretical discussions. Learners develop the ability to approach real-world problems from an analytical perspective with confidence.
Learners discover the power of a story and how to develop a story arc around their data goals. Successfully communicating data insights depends on the audience of stakeholders and the story points that speak to their needs and expectations. Learners continue to keep a data story thread throughout their entire data wrangling adventure as they frame their data goals with purpose.
This milestone project allows learners to explore their skills in the areas of statistics, Excel, SQL, and data storytelling. Learners are able to demonstrate their ability to clean and manipulate a dataset. Additionally, learners perform advanced statistical analysis on the data using summary statistics, linear regression, and modeling. Finally, they put their visualizations and insights into a coherent data story to present to their classmates. The data analytics milestone project is formally reviewed by the instructional team. Learners then incorporate their projects into their GitHub portfolio.
Learners explore the fundamental concepts of programming and how to structure their analyses. Topics include core programming concepts such as expressions, data types, variables, functions, loops, and arrays. Learners practice their coding skills through building highly structured and maintainable code using Jupyter notebooks.
Learners develop core data wrangling skills by expanding their Python programming skills. Learners explore a series of data analysis processes from sourcing, curating, and importing data to exploratory data analysis, data cleansing techniques, and data visualization techniques. Next, learners expand their toolbox by using industry-standard software to automate data wrangling processes.
Learners 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.
Building upon the skills learners gained in their previous SQL and Databases course, this course extends the learner’s skill in SQL programming and covers topics such as stored procedures, functions, common table expressions (CTEs), and query optimization. Learners also develop ETL scripts and data pipelines combining the use of SQL and Python.
This milestone project focuses on developing the learner’s ability to attain, transform, investigate, and present data throughout a data project life cycle. Learners demonstrate their ability to build data pipelines and wrangle data into a usable format for downstream data visualization and analytics. Learners present their reports and findings to classmates and then incorporate their projects into their GitHub portfolio. The instructional team reviews projects in this milestone.
Learners build upon visual communication concepts by learning to use popular industry business intelligence tools to create insightful analyses and visualizations. Learners also develop and apply best practices for reporting, graphs and charts, and dashboards in a way that can be applied in any business intelligence application.
In this course, learners 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. Learners build upon their previous knowledge of Python by using PySpark to access big data and create analytics models.
Learners 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, learners investigate and apply supervised and unsupervised machine learning algorithms, including classification, clustering, association rules, and time-series forecasting. Next, learners explore several advanced methods, including natural language processing, neural networks, and deep learning.
After an introduction to machine learning-created AI in the previous module, learners 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.
Learners meet the challenge of presenting their data insights and visualizations clearly and with ease to diverse types of stakeholders. They learn how to organize data visualizations around a goal and integrate a story arc to keep their audience engaged. Learners work on their own individual project that incorporates skills and knowledge gained throughout the program. This milestone culminates in a final project presentation that capitalizes on their data analysis, data storytelling, and presentation skills. Finally, learners share their work by uploading their completed projects to their GitHub portfolios.
Learners integrate with Career Services and the career curriculum to acquire the knowledge and skills to be successful in the digital job market. This preparation ensures learners possess the tools to readily apply to opportunities in the field.