Data Science 101: Methodology, Python, and Essential Math

Welcome! Nice to have you. I’m certain that by the end you will have learned a lot and earned a valuable skill. You can think of the course as compromising 3 parts, and I present the material in each part differently. For example, in the last section, the essential math for data science is presented almost entirely via whiteboard presentation.

The opening section of Data Science 101 examines common questions asked by passionate learners like you (i.e., what do data scientists actually do, what’s the best language for data science, and addressing different terms (big data, data mining, and comparing terms like machine learning vs. deep learning).

Following that, you will explore data science methodology via a Healthcare Insurance case study. You will see the typical data science steps and techniques utilized by data professionals. You might be surprised to hear that other roles than data scientists do actually exist. Next, if machine learning and natural language processing are of interest, we will build a simple chatbot so you can get a clear sense of what is involved. One day you might be building such systems.

The following section is an introduction to Data Science in Python. You will have an opportunity to master python for data science as each section is followed by an assignment that allows you to practice your skills. By the end of the section, you will understand Python fundamentals, decision and looping structures, Python functions, how to work with nested data, and list comprehension. The final part will show you how to use the two most popular libraries for data science, Numpy, and Pandas.