From data science methodology, to an introduction to data science in Python, to essential math for data science

Explain data science methodology, starting with business understanding and ending at deployment

Identify the varied elements of machine learning and natural language processing involved in building a simple Chatbot

Recall the various functionality of the two main data science libraries: Numpy and Pandas

Solve a system of linear equations

Define the idea of a vector space

Compute a least squares solution via orthogonal projection

None. This course is ideal for a bner to Data Science.

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 sections, and I present the material of each section 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 (like, 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 even 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.

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.

The final section delves into essential math for data science. You will get the hang of linear algebra for data science, along with probability, and statistics. My goal for the linear algebra part was to introduce all necessary concepts and intuition so that you can gain an understanding of an often utilized technique for data fitting called least squares. I also wanted to spend a lot of on probability, both classical and bayesian, as reasoning about problems is a much more difficult aspect of data science than simply running statistics.

Bners to Data Science

Individuals considering switching fields.

Individuals who want to get a big picture overview before focusing on specific Data Science topics.

You are interested in using Python for Data Science and Machine Learning.






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