From data science methodology to an introduction to data science in Python, to essential math for data science
Key Features
- Explain data science methodology, starting with business understanding and ending at deployment
- Identify the various elements of ML and NLP involved in building a simple chatbot
- Indicate how to create and work with variables, data structures, looping structures, and more
Book Description
The opening part of Data Science 101 examines some frequently asked questions.
Following that, we will explore data science methodology with a case study. You will see the typical data science steps and techniques utilized by data professionals. Next, you will build a simple chatbot so you can get a clear sense of what is involved.
The next part 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 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. Finally, we will wrap up the two most popular libraries for data science—NumPy and Pandas.
The last part delves into essential math for data science. You will get the hang of linear algebra along with probability and statistics. Our goal for the linear algebra part is to introduce all necessary concepts and intuition for an in-depth understanding of an often-utilized technique for data fitting called least squares. We will spend a lot of time on probability, both classical and Bayesian, as reasoning about problems is a much more difficult aspect than simply running statistics.
By the end of this course, you will understand data science methodology and how to use essential math in your real projects.
All resources are available at https://github.com/PacktPublishing/Data-Science-101-Methodology-Python-and-Essential-Math
What you will learn
- Examine frequent questions asked by passionate learners
- Explore data science methodology with a healthcare insurance case study
- Solve a system of linear equations
- Define the idea of a vector space
- Recognize the proper probability model for your use case
- Compute a least-squares solution through pseudoinverse
Who this book is for
This course is designed for people who are new to data science or who are interested in pursuing a career in data science, as well as those who wish to obtain a broad overview before diving into specialized data science topics.
This course will also benefit students who want to master the fundamental arithmetic for data science or obtain an introduction to data science in Python.
You need not have any prior experience in data science to take up this course.
