Data Science 101: Methodology, Python, and Essential Math
From data science methodology, to an introduction to data science in Python, to essential math for data science.
Created by Ermin Dedic | 14.5 hours on-demand video course
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.
What you’ll learn
- 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
- Indicate how to create and work with variables, data structures, looping structures, decision structures, and functions.
- 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
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