Lean how visualization is used in data science and what skills you visualization skills you need to be an effective communicator as a data scientist. Jungwoo also introduces useful visualization tools to make your job easier when working on a visualization project in the context of data science.
- To overcome the challenge of effectively communicating the results of data analytics to a lay audience, there are scientists frequently rely on visualization. Therefore, it is to their scientists advantage to have a good understanding of effective visualization techniques, so that they can use the most effective one for a given problem and audience. Some of the well-known characteristics of effective visualization are readily available.
These include displaying data at multiple levels of details, and avoiding distorting the message to be conveyed while attempting to visualize it. It is also very helpful to know how to use some of the software tools offered by the industry leaders of visualization solutions. For example, Tableau offers one of the most popular and comprehensive visualization tools for data scientists.
It supports a variety of visualization elements such as different types of charts, graphs, maps, and other more advanced options. Always remember that your job as a data scientist is that of a middle man interfacing with both experts working with sophisticated technologies. And non-experts who don't have the luxury of wading through mountains of information to get the message they want to hear and see.
Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and analytics, explores which markets are using big data the most, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the four leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.
- A history of data science
- Why analytics is important
- How data science is used in social media, climate research, and more
- Data science skills
- Data science certifications
- The future of big data