XBUS-506 Visual Analytics
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Though visual representations of quantitative information were traditionally cast as the end phase of the data analysis pipeline, visualizations can play important roles throughout the analytic process and are critical to the work of the data scientist. Where static outputs and tabular data may render patterns opaque, human visual analysis can uncover volumes and lead to more robust programming and better data products. For students getting started with data science, visual diagnostics are particularly important for effective machine learning. When all it takes is few lines of Python to instantiate and fit a predictive model, visual analysis can help navigate the feature selection process, build intuition around model selection, identify common pitfalls like local minima and overfit, and support hyperparameter tuning to render more successful predictive models. In this course, students will learn to deploy a suite of visual tools using Scikit-Learn, Matplotlib, Pandas, Bokeh, and Seaborn to augment the analytic process and support machine learning from preliminary feature analysis through model selection, evaluation, and tuning.
Upon successful completion of the course, students will be able to use visualizations to:
- Summarize and analyze a range of data sets.
- Support feature engineering and feature selection.
- Diagnose common machine learning problems like bias, heteroscedasticity, underfit, and overtraining.
- Evaluate their machine learning models' performance, stability, and predictive value.
- Steer their predictive models toward more successful results.
Techniques for Decision Making - Certificate in Data Analytics
Translate Data Into Actionable Business Insights
Nearly every business and organization today is extremely dependent on data. Modern data analytics combines powerful software tools and statistical techniques to produce insights that can improve results in every field and organizational department. Professionals who are adept with these tools and have the latest data analysis skills are highly valuable and in demand.
In this three-course certificate program, you’ll learn how to make — and defend — critical business decisions by wrangling, analyzing, visualizing and interpreting large-scale data sets in meaningful ways. We’ll explore innovative approaches for generating insights and predictions that can boost sales, enhance operational efficiencies and drive qualitative improvements in your products and services. Improve your organization’s data-driven decision making and escalate your own career trajectory.
WHAT YOU’LL LEARN
- Data analysis and statistical techniques for knowledge discovery
- Visual discovery and interpretation methods for large data sets
- Information design, storytelling and presentation using visualization, pivot tables, graphs, charts, infographics and conditional data table formatting
- Making predictions using decision trees, regression analysis, and supervised and unsupervised learning models
- R and SQL programming practices
GET HANDS-ON EXPERIENCE
- Use industry-standard software such as Excel, SQL Server, Azure Machine Learning Studio, RStudio and Power BI tools
- Utilize machine learning models and data mining techniques to create basic prediction, recommendation and classification systems
- Develop and present a Power BI dashboard that allows users to leverage data in business decision making
Enjoy the flexibility of learning at your own pace, with instructor support along the way. Courses are 100 percent online, with no class meetings or fixed deadlines — so you can start anytime. You have up to four months to complete each course and up to two years to complete the program.
Start and finish your program with the same group of students, with frequent opportunities to interact with your instructors and classmates along the way. Courses are 100 percent online, with set due dates for assignments but no class meetings.
Attend classes part time at one of our convenient locations in the Puget Sound region. You’ll engage face to face with your classmates and instructors as part of a highly interactive curriculum
Who should attend
Professionals who want to learn to work with data to make strategic decisions. Those with programming skills who already have experience working with data should consider taking the more advanced Certificate in Data Science.