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Introduction to Data Science with Python

About the Course

Explore foundational skills in data science with Python, covering data preprocessing, visualization, and analysis. Learn to work with key libraries to create and customize charts, manage datasets, and effectively present insights using Python tools.

We'll cover the following...

Welcome to the course

This course covers advanced business intelligence, from foundational concepts to building data preprocessing pipelines and presenting insights. This provides a solid foundation for advanced analytics. The course focuses on building data science skills for freelance work and roles in global organizations.

Intended audience

This course is for programmers who want to learn methods for data visualization and analysis.

Prerequisites

Although we’ve designed the course to be accessible to anyone, the following will be helpful:

  • Basic knowledge of Python programming
  • Basic knowledge of common Python libraries, such as NumPy and pandas.
  • Basic mathematics skills.

Why Python for analytics?

Python is widely used for data analytics, machine learning, and data science. Other languages, such as R and Julia, are also used, but Python is more widely adopted. This is because of its applicability to many fields, its ease of use, and its amazing ecosystem of libraries that support data analytics and machine learning. This course uses Python throughout and covers libraries such as NumPy, pandas, Matplotlib, seaborn, and Plotly.

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Learning outcomes

By taking this course, you’ll learn to do the following:

  • Plot different types of charts, individually and together.

  • Set the “Tick,” “Text,” “Legend,” and annotate elements of a plot.

  • Customize the grid and spine displays of a plot.

  • Set the colors for different elements of a plot.

  • Customize the style and appearance of different plot components.

  • Choose different chart types based on data type and requirement.

  • Combine and merge datasets.

  • Show the distribution of data.

  • Show the relationship between different variables.

  • Plot data in 2D and 3D.