The editors at Solutions Review have compiled this list of the best data wrangling courses and online training to consider.
Data wrangling is the process of cleaning, structuring and enriching raw data into the desired format. The practice has become increasingly important as data volumes and varieties continue to grow larger. Data wrangling typically involves six iterative steps, including data discovery, structuring, data cleaning, data enrichment, data validation, and publishing. The end-result of this time-consuming process is curated data sets that are easy to access, analyze and generate insights from.
With this in mind, we’ve compiled this list of the best data wrangling courses and online training to consider if you’re looking to grow your data management or analytics skills for work or play. This is not an exhaustive list, but one that features the best data wrangling courses and online training from trusted online platforms. We made sure to mention and link to related courses on each platform that may be worth exploring as well. Click Go to training to learn more and register.
The Best Data Wrangling Courses
Description: This course allows you to apply the SQL skills taught in “SQL for Data Science” to four increasingly complex and authentic data science inquiry case studies. Students will learn how to convert timestamps of all types to common formats and perform date/time calculations. You’ll also select and perform the optimal JOIN for a data science inquiry and clean data within an analysis dataset by deduping, running quality checks, backfilling, and handling nulls.
Related path/track: Process Data from Dirty to Clean
Description: The real world is messy and your job is to make sense of it. Toy datasets like MTCars and Iris are the result of careful curation and cleaning, even so, the data needs to be transformed for it to be useful for powerful machine learning algorithms to extract meaning, forecast, classify, or cluster. This course will cover the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Related path/track: Interactive Data Visualization with rbokeh
Description: Edureka’s Machine Learning Certification Training using Python will help you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes, and Q-Learning. This module will also help you understand the concepts of statistics, time-series, and different classes of machine learning algorithms like supervised, unsupervised, and reinforcement algorithms.
Related path/track: Data Science Certification Course using R
Description: This introductory Excel course will equip you with a strong foundational knowledge of Excel to organize, analyze and work with data. You will develop essential Excel skills, such as simple data wrangling and managing spreadsheets, along with a foundational understanding of business data analysis.
Related paths/tracks: Excel for Everyone: Data Analysis Fundamentals, Excel for Everyone: Data Management, Data Science: R Basics, Data Analytics Basics for Everyone, Learning Analytics Fundamentals, Data Science: Wrangling
Description: This course will teach you from start to finish how to get your data into R efficiently and polish it up so that it is as good as it can be. This will let you or your team focus after this step on the statistical modeling, visualization, reporting, sharing, or any other post-processing task you wish to perform. Confidence, reliability, and reproducibility in your data acquisition and preparation are the kingpins to being able to maximize your data’s value.
Platform: LinkedIn Learning
Description: In this course, learn about the principles of tidy data, and discover how to create and manipulate data tibbles—transforming them from source data into tidy formats. Instructor Mike Chapple uses the R programming language and the tidyverse packages to teach the concept of data wrangling—the data cleaning and data transformation tasks that consume a substantial portion of analysts’ time.
Related path/track: R Essential Training: Wrangling and Visualizing Data
Description: This course, Data Wrangling with Python, is aimed at helping you do exactly that. First, you’ll see how to merge data from different sources using the methods concat, append, and merge. Next, you’ll discover how to combine data into groups. The primary function used here is groupby. In the next two sections, you’ll explore how to transform and normalize data. You’ll learn why these processes are necessary, and then proceed to see how they work in practice.
Description: Advance your programming skills and refine your ability to work with messy, complex datasets. You’ll learn to manipulate and prepare data for analysis, and create visualizations for data exploration. Finally, you’ll learn to use your data skills to tell a story with data.
Description: This course enables learners to acquire the knowledge and statistical data analysis wrangling and visualization skills that are most important. The module will take you (even if you have no prior statistical modeling/analysis background) from a basic level to performing some of the most common data wrangling tasks in Python. It will also equip you to use some of the most important Python data wrangling and visualization packages such as seaborn.
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