Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python

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This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.

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49 on-demand videos & exercises
Level: Beginner
4hrs 21mins

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What to know about this course

Welcome to the course where you will learn about the NumPy stack in Python, which is an important prerequisite for deep learning, machine learning, and data science. In this self-paced course, you will learn how to use NumPy, Matplotlib, Pandas, and SciPy to perform critical tasks related to data science and machine learning. This involves performing numerical computation and representing data, visualizing data with plots, loading in, and manipulating data using DataFrames, performing statistics and probability, and building machine learning models for classification and regression.

In this course, we will first start with NumPy; we will understand the benefits of NumPy array and then we will look at some complicated matrix operations, such as products, inverses, determinants, and solving linear systems. Then we will cover Matplotlib. In this section, we will go over some common plots, namely the line chart, scatter plot, and histogram. We will also look at how to show images using Matplotlib. Next, we will talk about Pandas. We will look at how much easier it is to load a dataset using Pandas versus trying to do it manually. Then we will look at some data frame operations useful in machine learning, such as filtering by column, filtering by row, and the apply function. Later, you will learn about SciPy. In this section, you will learn how to do common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing. Finally, we will also cover some basics of machine learning that will help us start our deep learning journey.

By the end of the course, we will be able to confidently use the NumPy stack in deep learning and data science.

Who's this course for?

This course is designed for anyone who is interested in data science and machine learning, who knows Python and wants to take the next step into Python libraries for data science, or who is interested in acquiring tools to implement machine learning algorithms.

One must have decent Python programming skills and a basic understanding of linear algebra and probability for this course.

What you'll learn

  • Understand supervised machine learning with real-world examples
  • Understand and code using the NumPy stack
  • Make use of NumPy, SciPy, Matplotlib, and Pandas to implement numerical algorithms
  • Understand the pros and cons of various machine learning models
  • Get a brief introduction to the classification and regression
  • Learn how to calculate the PDF and CDF under the normal distribution

Key Features

  • Study basics of machine learning and understand how to use the NumPy stack for deep learning in data science
  • Learn how to use NumPy, Matplotlib, Pandas, and SciPy for critical tasks in data science and machine learning
  • Perform numerical computations, visualize data, load, and manipulate datasets using Pandas

Course Curriculum

About the Author

Lazy Programmer

The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.

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