Learn what role statistics play in data science. Jungwoo explains the minimum statistics knowledge requirements for a data scientist as well as more advanced skills to be aquired eventually.
- [Voiceover] Statistics lays a foundation for data science. In fact, statistics is where data science started. Therefore, developing a reasonable understanding of statistics is a must for a data scientist. In fact, the more you know about statistics, the better. At a minimum, a data scientist needs to be proficient with concepts such as probability, correlation, variables, distributions, regression, null hypothesis significance tests, confidence intervals, t-test, ANOVA and chi-square.
You also need to know how to use common statistical analysis tools, including R, Excel and SAS. At a more advanced level a data scientist needs to be familiar with concepts and algorithms, like logistic regression, support vector machines, or SVMs, and Bayesian methods.
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
Skill Level Appropriate for all
1. Define Data Science
6. Future of Big Data
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