Learn what machine learning scientists do to contribute to the data science industry by analyzing the qualities required of them as well as advanced technical and practical IT skills.
- [Voiceover] Qualified machine learning scientists are sought after and their salaries are also climbing up. At the top of their career, machine learning scientists program computers to learn on their own. So then more specifically what is required of a machine learning scientist? At its highest level the job requires you to be highly creative and independent. Nobody can tell you what to do when your job is to enhance customer interactions at a multi-billion dollar company through machine learning techniques.
You also need the discipline to follow through and meet deadlines. Finally, attention to details and quality is critical. As small, seemingly insignificant mistake can cause a havoc on your entire project when you have to deal with millions of unhappy customers. Now let's talk about technical skills. The most foundational ones are usual suspects required for any advanced IT professions.
Math skills are essential because they form the foundations of the technical language machine learning scientists use. In particular, deep knowledge of statistics and probability is important. Next is an ability to develop and validate a mathematical model representing various aspects of machine learning. Once a model is developed, it needs to be translated into an algorithm or unambiguous and discrete processes computer can execute.
Finally, you also need some practical IT skills. Proficiency in programming languages, such as Python, C++, Java and R is very helpful. Your work efficiency as a machine learning scientist is often dependent upon your ability to preprocess a large amount of text very quickly and efficiently. Therefore, your familiarity with Unix Linux tools like sed, awk, grep, find, and sort is highly useful.
Last but not least is your understanding of distributed computing because your machine learning program will most probably have to take advantage of technologies such as Hadoop and cloud computing. As we can store data more cheaply and easily, there is an increasing number of data sources available to us. These include images, videos, maps, networking data, social media data and so on. Therefore, naturally there is a growing need for data processing.
Machine learning scientists are at the forefront of this kind of efforts for leveraging the data around us.
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