Most data scientists’ time is spent cleaning and manipulating data, and only 20% is spent analyzing it. Cleaning the data is crucial for any data scientist since dirty data can lead to inaccurate analysis. You have come to the right place if you struggle with this complex task. This article will discuss Best Data Cleaning Courses to reduce your efforts each time.
Why is Data Cleaning Important?
Data cleaning is also known as scrubbing, which means identifying and fixing errors and removing irrelevant data from raw datasets. Nearly 30% of organizations believe their data needs to be more accurate. There are more losses than money at stake for companies with dirty data. Cleaning data allows you to make informed, intelligent decisions based on consistent, structured, accurate data.
In the United States, data scientists earn $123,820 per year. LinkedIn says data science and statistics majors have experienced 650% job growth since 2012. According to the US Bureau of Labor Statistics, approximately 11.5 million jobs will be added by 2026. If you also struggle with data cleaning, choose an appropriate course from the Best Data Cleaning Courses Online in the list below.
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Best Data Cleaning Courses, Certification, Tutorials, Training, Classes Online
- Best Overall Getting and Cleaning Data by Johns Hopkins University [Coursera]
- Best for Python Python for Data Cleaning [Datacamp]
- Best for Beginner Learn Data Cleaning with Python [Codecademy]
- Best for R Data Cleaning using R [Datacamp]
- Best for ML Data Cleaning for Machine Learning [Udemy]
- Best for Excel Excel in Data Cleaning [Pluralsight]
Getting and Cleaning Data by Johns Hopkins University [Coursera]
Before you process the data, you have to gather data and clean it. This Online Data Cleaning Course is all about how to obtain data. You will learn how to drive data from the web, APIs, databases, and other formats. This data-cleaning tutorial will explain a dataset’s components, such as raw data, processing instructions, codebooks, and processed data. You will be able to understand how to collect, clean, and process the data.
Course Instructor
The Getting and Cleaning Data Course will be taught by the most knowledgeable instructors, including Jeff Leek, Roger D. Peng, and Brian Caffo. The instructors are experienced in data science and work as associate professors and professors in the Department of Biostatistics at the Bloomberg School of Public Health.
What You’ll Learn
This Cleaning Data Course is part of various programs and specializations such as Data Science Specialization and Foundations using R Specializations. The syllabus is divided into 4 chapters to cover all the primary ways of cleaning data.
- Week 1: In this first week of the course, you will learn how to find data and read different types of files. Able to implement the practical exercises using R.
- Week 2: As a part of this module, you will be introduced to the most common storage systems for data, along with the tools that can be used to extract data from the web or databases like MySQL.
- Week 3: Using the material from weeks 1 and 2, you can organize, merge, and manage the data you have collected.
- Week 4:The final week of lectures will focus on text and date manipulation in R. As part of this final week, you will also review course projects by peers.
Pros & Cons
Pros
- Helpful and well-guided
- Access to assignments and graded projects
- Enjoyable hands-on experience
Cons
- More challenging for beginners
- Need an understanding of R programming
Key Highlights & Learning Objectives
- Learn about the most common methods of data storage.
- Look at data cleaning basics and apply them to make your data “tidy.”
- Build knowledge of R as a programming language for manipulating text and dates.
- Learn how to use the web, APIs, and databases to obtain usable data.
- Enjoy lifetime access to 25+ video lectures, 7 reading materials, and 8 practical quizzes to enhance your knowledge after every week’s module.
- Receive a shareable certificate of completion after finishing all assignments, projects, and lessons to showcase your capabilities to your employers.
Who is it for?
This Online Data Cleaning Course is offered to professionals and programmers interested in data science. By the end of the course, you will develop the knowledge required to clean, organize, and manage data. The basic skills can be used for real-world projects and help you upgrade your skills.
Rating: 4.5/5
Students Enrolled: 242,331
Duration: 19 hours
Python for Data Cleaning [Datacamp]
Data cleaning is the first step for data scientists, as analyzing untidy data can lead to inaccurate insights. This Data Cleaning using Python Course will teach how to obtain data, diagnose, and treat various problems in Python. It will explain how to deal with different data types, check your data, handle data, and perform record linkage.
Course Instructor
The DatacampPython Data Cleaning Class is created and taught by Adel Nehme. He is an educator, speaker, and Evangelist at Datacamp and is passionate about data science. His data science and business analytics expertise made him release various courses and live training sessions. Students will find his courses amusing and exciting to study and practice without a doubt.
Pros & Cons
Pros
- Very well-structured and presented
- Introduce to best methods
- Explore powerful tools
- Practical examples
Cons
- Need deep knowledge of data science
Key Highlights & Learning Objectives
- This DataCamp Data Cleaning Online Course is divided into 4 chapters to learn more about data cleaning in Python and its applications.
- Common data problems
- Text and categorical data problems
- Advanced data problems
- Record linkage
- Learn to overcome data problems, convert data types, remove future data points, and avoid double-counting.
- Build skills in fixing whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.
- Develop an understanding of advanced data cleaning problems and gain skills to solve those problems.
- Able to link records by observing and calculating the similarity between strings.
- Earn a certificate to share with potential employers and within your professional network.
- Stream 13 video lectures, watch 4 hours of content, and practice 44 exercises for free for life.
Who is it for?
This Python for Data Cleaning Class is for professionals eager to expand their knowledge of data cleaning. Data analysts and engineers can enrol to gain critical skills to proceed in the data science field. If you are not aware of data science, you must check the list of our Best Data Science Courses Online.
Rating: 4.4/5
Students Enrolled: 1,00,218
Duration: 4 hours
Learn Data Cleaning with Python [Codecademy]
Data Professionals use Python to process and manipulate the data. In the Cleaning Data with Python Course, you will learn standard techniques to collect, clean, and prepare data for analysis. Along the way, you’ll learn the basics of Regex, a fun and powerful tool to find string patterns. It is the most recommended Python-based course for data pulling and cleaning from the Web and APIs.
Course Instructor
Codecademy launched this data cleaning with python tutorial to help students gain practical hands-on experience. Each lesson will provide coding examples to teach how to clean data and provide practice exercises at the end.
Pros & Cons
Pros
- Hands-on learning
- Bite-sized lessons
- Auto-graded quizzes and immediate feedback
- Short and interactive curriculum
Cons
Not for beginners
Require Python skills
Key Highlights & Learning Objectives
- This Codecademy Data Cleaning Training Course includes 2 lessons, 2 quizzes, and 1 hands-on project.
- Learn about how to use regular expressions in Python and code to clean and manipulate the data
- Understand the basics of how to clean the data using Python
- Get the quiz to practice what you’ve learned in the first lesson
- Access one project to code in Python and clean the data to process for analysis.
Who is it for?
This Online Data Cleaning with Python is for data scientists and analysts who want to sharpen their skills. Once you’ve completed this training program, you can use popular methods for cleaning data and work with numerical data that have different effects on data analysis.
Rating: 4.4/5
Students Enrolled: 39,788
Duration: 2 hours
Data Cleaning using R [Datacamp]
This data cleaning course with R will introduce you to various techniques to clean dirty data using R. First; you will learn how to convert data types, implement range constraints, and deal with duplicates during the cleaning process. The more you practice working on shared data problems, the more advanced challenges you’ll face, such as making sure measurements are consistent and dealing with missing values.
Course Instructor
The instructor of this Datacamp Cleaning Data in R Course is Maggie Matsui, a curriculum manager at Datacamp. She has lots of experience in teaching programming, math, and statistics. Her passion is to make resources accessible to everyone interested in programming and data science.
Pros & Cons
Pros
- Very useful content
- Excellent materials and projects
- Simple and effective videos
- Bite-sized lessons
Cons
- Require knowledge about Dplyr
- Difficult
Key Highlights & Learning Objectives
- This R for Data Cleaning Online Tutorial is divided into 4 chapters, including:
- Common Data Problems
- Categorical and Text Data
- Advanced Data Problems
- Record linkage
- Improve your business with skills of cleaning data quickly and accurately.
- Learn to identify categorical and text data and reformat strings to build consistency.
- Identify and correct any incorrectly entered values so that missing values will not adversely affect your analysis.
- Create a master dataset from two restaurant review datasets using your new skills.
- Free lifetime access to 13 video lectures, 4 hours of content, and 44 exercises. Become a certified professional and share it with employers.
Who is it for?
This R with Data Cleaning course is designed for intermediate learners with basic R programming skills and experience using R for projects. Before enrolling, students must take the Joining Data with Dplyr course to get more benefits. If you are new to R, check our list of the Best R Programming Courses to help you gain this vital skill.
Rating: 4.4/5
Students Enrolled: 50,851
Duration: 4 hours
Data Cleaning for Machine Learning [Udemy]
Unleash new skills by taking this Data Cleaning for Machine Learning Course from Udemy. It is designed to teach data manipulation skills in Python. You will learn how to use Jupyter Notebooks and process real-world raw data. The Data Cleaning in Python Online Course will discuss a range of data cleaning techniques such as imputing missing values, feature scaling, and fixing data type problems.
Course Instructor
Ajatshatru Mishra has created this Udemy ML Data Cleaning Course Online to share his experience in working with data analytics, machine learning, and deep learning. He is currently working as a data scientist at the analytics department in one of the largest corporations in India.
Pros & Cons
Pros
- Clear and easy to follow
- Explain real-world purposes
- More advanced topics
Cons
- No free auditing
- Basic knowledge of Python
Key Highlights & Learning Objectives
- The Data Cleaning for Machine Learning Certification in Python includes 4 sections and 31 videos to data quality issues in Machine learning using Python.
- Introduction and Setup
- Detecting Data Quality Issues
- Data Cleaning and Preprocessing
- Data Cleaning and Preprocessing for NLP
- Discover how to detect and impute missing values in data and correct incorrect data types.
- Learn how to deal with categorical columns and replace wrong values with correct ones.
- Learn to implement the Lambda method for advanced cleaning functions and group the dataset by a particular column.
- Determine how to perform feature scaling and clean and preprocess textual data for NLP
- Enjoy unlimited access to 1.5 hours of on-demand video, 4 articles, 4 downloadable resources and certificate of completion.
Who is it for?
Data Analysts, Data Engineers, Machine Learning Engineers, and Data Scientists can take this ML Data Cleaning Tutorial. It does require experience in Python programming. After completing this course, you will gain confidence to deal with dirty data requiring much effort and advanced Python skills. Opt for the Best Python Data Science Courses to discover how Python libraries can be used for analyzing and manipulating data.
Rating: 4.4/5
Students Enrolled: 100+
Duration: 1.5 hours
Excel in Data Cleaning [Pluralsight]
This Data Cleaning with Excel can help you learn some tactics to clean data and reduce labor and effort for data professionals. In this Cleaning Data in Excel tutorial, you can learn how to cleanse data effectively and efficiently in Excel. It will also explain different types of dirty data and how to keep your data accurate and tidy, even the duplicate files used by multiple people.
Course Instructor
An experienced Guru, Susan Walsh, is the creator of the Pluralsight Excel Data Cleaning Tutorial to fix dirty data. Susan is the Founder of the Classification Guru, a data Cleaning consultancy. She is an expert in data classification and taxonomy customization, which made her establish another company, COAT.
Pros & Cons
Pros
- Bite-sized lessons
- Brief and interactive videos
Cons
- Need more resources
Key Highlights & Learning Objectives
- This Cleaning Data with Excel Course comprises 4 short lessons to understand everything you need to clean and organize untidy data.
- Why Data Needs to be Cleaned
- Data Cleaning Best Practice
- Cleaning Names in Excel
- Cleaning Addresses in Excel
- Learn the impact of dirty data and identify methods to recognize dirty data.
- Discover tips to maintain accuracy when multiple people are involved in a project.
- Understand how to use Vlookup to clean and merge addresses.
- The ability to work efficiently, reduce errors, and minimize the time it takes to clean data.
- With projects and interactive courses, you can practice and apply knowledge faster in real-world scenarios.
Who is it for?
The Excel Data Cleaning Class is designed for beginners who want to minimize their workload in obtaining data. Upon completing this course, you can work efficiently, reduce errors, and minimize data Cleaning time.
Rating: 4.3/5
Duration: 1h 45m
Frequently Asked Questions
Is Data Cleaning Difficult?
Data Cleaning is one of the main challenges that can cause several issues during data analysis. Furthermore, it is time-consuming since many data sets require attention, and some errors take time to pinpoint.
However, you can reduce errors and minimize effort by taking any data Cleaning course from the list. By developing skills and knowledge about methods of cleaning data, you can focus on other job roles and responsibilities and increase productivity.
What are the benefits of data cleaning?
Data Cleaning organizes data to maintain accuracy and reduce duplicate or incorrect data. There are various benefits of Cleaning data:
– It will improve decision-making for organizations or people.
– Companies can boost their revenue and achieve business goals
– It can reduce the cost of mail marketing and the environmental damage of campaigns.
– Increase the productivity of your sales and marketing team.
Who needs to learn how to clean data?
Data engineers, data managers, and data analysts carry out Data Cleaning. Thus, it is a beneficial skill for those who work with data and use it for analysis. If you are a manager, HR professional, or marketer, learning data cleaning can help you to discover tips and techniques to identify poor data and obtain accurate data for analysis.
Is data cleaning easy to learn?
Learning Data Cleaning is easy for data scientists and other professionals with little knowledge about data science; as you may know, 80% of data scientists’ time is wasted cleaning and manipulating data. That means the actual process of cleaning dirty data is time-consuming and challenging.
Last Words
Data Cleaning is the essential step in data analysis; no data scientist can skip this step. It is a tedious task to do. Data scientists and analysts may look for methods and techniques to gain data-cleaning skills effortlessly and accurately. This review article can help you find the right course that meets your needs and improves your proficiency.
