Python Pandas Certification Courses
Tags: datavisualization, pandas, datascience, python
Published: 2021-03-22 13:57:49

---
title: Python Pandas Certification Courses
published: true
date: 2021-03-22 13:36:21 UTC
tags: datavisualization,pandas,datascience,python
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---

![](https://cdn-images-1.medium.com/max/1024/1*4isMjTHIgIDzuybqMYf6JQ.png)

I am excited to announce [Python Pandas Certification Courses ](https://www.dunderdata.com/live-online-courses)— a series of courses to help you become an expert at using the pandas library to analyze data, each with a challenging certification exam given at the end.

The main goal of the Python Pandas Certification series of courses is to provide you with a path to gain mastery of the pandas library so that you can use it confidently in a professional environment.

### First Course FREE — Begins March 29!

The first course in the certification series, Selecting Subsets of Data in Pandas, is free to take. It includes 8 hours of live instructions, over 100 exercises, and a challenging certification exam. [Sign up to take this class for FREE here](https://www.dunderdata.com/selecting-subsets-of-data-pandas-certification).

### Courses

There are currently seven courses in the series that will cover nearly all of the pandas library. A description of each course is provided below. To get more info and to register, visit the [Python Pandas Certification home page](https://www.dunderdata.com/live-online-courses).

### Course 1: Selecting Subsets of Data in Pandas

Selecting a subset of data is perhaps the most common task performed in pandas. Unfortunately, there are many different (and overlapping) ways to select various rows and columns of a pandas DataFrame. This course goes into great detail into all of the various ways to select data and gives guidance on choosing the most effective method for specific situations. [This course is FREE to take and begins March 29](https://www.dunderdata.com/selecting-subsets-of-data-pandas-certification).

### Course 2: Essential Pandas Commands

In this course, you’ll learn the most common and fundamental commands used in just about every data analysis. You’ll learn the distinction between methods that aggregate (summarize the data with a single number) and those that do not. You’ll learn how to change the direction of the operation so that calculations are done horizontally as well as vertically (the default for DataFrames). You’ll work with missing values and perform other tasks such as sorting, ranking, and looking for unique values.

You will also learn how to operate on string and date columns, which require a different set of methods than numeric columns. You’ll learn about all of the many kinds of data types that pandas offers and how to operate with them.

### Course 3: Grouping Data

In Essential Pandas Commands, you learn to operate on the entire dataset. In Grouping Data, you learn how to operate on independent groups with the dataset. You’ll learn how to aggregate and summarize each group by a single statistic and present the information as a pivot table.

Grouping data in pandas opens you up to the possibility of writing extremely inefficient code. It’s not unusual to see performance gains of 10–100x by rewriting code in a more efficient manner. You will learn specific approaches to writing efficient grouping commands.

### Course 4: Time Series

Pandas provides a variety of tools to process time series data. You will learn how to sample and group time series by different periods of time. You’ll also learn how to perform moving aggregate operations. You will complete a project analyzing coronavirus cases and deaths from different countries.

### Course 5: Cleaning Data

It’s often that you’ll want to transform the initial dataset given to you to a different form, before attempting any serious data analysis. Text data is particularly difficult to work with if it is not properly transformed. You’ll learn the fundamentals of regular expressions, which match patterns within text to help discover and extract particular pieces of information. You’ll also learn how to reshape your data and convert it to a form that makes data analysis simpler.

### Course 6: Joining Data

Working with multiple datasets simultaneously often requires you to merge them into a single table. You’ll learn how to join together multiple pandas objects together. You’ll also learn how to join tables using SQL-like logic. You will learn about data normalization and complete exercises where you transform datasets so that they are normalized.

### Course 7: Data Visualization

You will first learn the basics of matplotlib, a powerful and popular data visualization library in Python. You’ll then learn how to plot data with it as well as with the pandas and seaborn libraries. You will learn about the different approaches that different libraries take to plotting data.

### Exercises, Projects, and Exams

To help you gain mastery of pandas so that you can use it effectively in a professional environment, each course will come with dozens of exercises and at least on project. All exercises have detailed solutions to help you reinforce the material.

Each course has a challenging comprehensive exam on the material covered. Passing it will earn you a certificate of completion.

### Register for the entire series

If you desire a mastery of pandas with proof that you are indeed an expert, then you’ll want to sign up for the [Python Pandas Certification Courses.](https://www.dunderdata.com/live-online-courses)

### Instructor

My name is Ted Petrou and I have written two books on the pandas library:

- [Master Data Analysis with Python](https://www.dunderdata.com/master-data-analysis-with-python)
- [Pandas Cookbook](https://www.amazon.ca/Pandas-Cookbook-Scientific-Computing-Visualization-ebook/dp/B06W2LXLQK)

I have completely mastered how to use the pandas library effectively and would love to be your guide for reaching the same level. I have taught more than 1,000 hours of live classes on pandas and know exactly where the pain points are and how to alleviate them. My classes are interactive and keep your hands on the keyboard so that you are coding along with me. Feel free to reach out to me with any questions — ted@dunderdata.

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