Predictive analytics is one of the richest disciplines within the realm of data science. As the tools and techniques for using data to predict future outcomes have evolved, business and data analysis professionals can use this learning path to stay up to date with the latest advancements.

Data visualization is ultimately the make-or-break moment for any data science operation or business analytics project. Develop a solid foundation for how to think about the visual interpretation and communication of data and data insights.

If you want to participate in the data revolution, you need the right tools and skills. R is a free, open-source language for data science that is among the most popular platforms for professional analysts. Learn the basics of R and get started finding insights from your own data, in this course with professor and data scientist Barton Poulson. The lessons explain how to get started with R, including installing R, RStudio, and code packages that extend R’s power. You also see first-hand how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis. By the end of the course, you’ll have a thorough introduction to the power and flexibility of R, and understand how to leverage this tool to explore and analyze a wide variety of data.

Learn the most popular data-science-specific language: R! This learning path provides a strong foundation of skills and knowledge on which to build your coding resume. Learn how R works, from the foundational concepts on up. Practice using R with two of the most common tools in data science: Excel and Tableau. Explore the applied use of R in social network analysis.

Data integrity is the new focal point of the data science revolution. Now that everybody is onboard with the role of data in people's lives and business, it's not an unfair question to ask, "Can you prove that your data is accurate?" In this course, you can learn how to identify and address many of the data integrity issues facing modern data scientists, using R and the tidyverse. Discover how to handle missing values and duplicated data. Find out how to convert data between different units and tackle poorly formatted text. Plus, learn how to detect outliers, address structural issues, and identify red flags that indicate potential data quality issues.Where possible, instructor Mike Chapple shows how to correct the issues using R, but the same principles can be applied to any statistical programming language. Learning objectives Missing data Duplicate rows and values Converting data Formatting data Working with tidy data Tidying data sets Dealing with suspicious data

Data fills all available space, and now that storage is cheap, the amount of data has exploded. However, all that information is useless without analysis and context. The R programming language is designed to make it easier to analyze and visualize massive amounts of data. For example, R provides the ability to multiply one block of variables by another—an assumption that provides inherent advantages over other languages. This course shows why R is ideal for high volumes of data, introduces more efficient ways to use the language, and explains how to avoid the problems and capitalize on the opportunities of big data. Learn how to determine if you have enough memory and processing power, produce visualizations of big data, optimize your R code, and use advanced techniques such as parallel processing to speed up your computations. Plus, discover how to integrate R with big-data solutions such as SQL databases and Apache Spark. Learning objectives Accessing memory and processing power Visualizing high-volume data Profiling and optimizing R code Compiling R functions Parallel processing with R Using R with other big data solutions

The R language is one of the top two languages you need to learn if you want build the strongest career path possible in data science. (The other is Python.) After mastering the basics of R, take your skills in data science into highly valued areas of specialty with this learning path. Learn R in the context of the R tidyverse. Create data visualizations and presentations. Develop business analytics skills at an advanced level in Excel.

Do you know the difference between AI and machine learning? Do you know how they affect you, your career path, and the world around us? After taking the courses in this learning path, you'll have a mastery of the concepts and future directions of technologies like artificial intelligence and machine learning. You'll be able to make more informed decisions and contributions in your work environment. Gain a clear and detailed understanding of how AI and machine learning work. Learn how leading companies are using AI and machine learning to change the way they do business. Learn how the next generation of thinking about AI is addressing issues of accountability, security, and explainability.

Quickly learn the general programming principles and methods for Python, and then begin applying that knowledge to using Python in data science-related development. Learn the basics of Python as an object-oriented programming language. Apply Python coding skills to analytics uses. Explore the Python scientific stack of tools.