About the Course

Let's have a look at the course's content and its intended audience.

We'll cover the following

Welcome to the course

This course covers everything you need to know about advanced business intelligence— from the basics to implementing data preprocess pipelines and presenting insights. In most cases, this is all you need for advanced analytics. The goal of this course is to help you acquire data science skills that will enable you to start a large freelance project or work for an international enterprise.

Intended audience

This course is designed for programmers who want to learn various methods of visually representing and analyzing data.

Prerequisites

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

  • Some knowledge of Python programming.
  • Some knowledge of common Python libraries, such as NumPy and pandas.
  • Some basic mathematics knowledge.

Python has quickly become the language of choice for many people when it comes to data analytics, machine learning, and data science. Even though many other languages, such as R and Julia, are also popular, Python clearly takes the lead in popularity for most programmers. 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. We’ll use Python exclusively in this course and discuss many of its great libraries, such as NumPy, pandas, Matplotlib, Seaborn, and Plotly.

Python logo

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 2-D and 3-D.