15+ Best Data Analysis Courses & Certifications Online in 2024
Do you want to become a professional data analyst?
If you want to learn the ins and outs of cleaning, transforming, and modeling business data in order to discover useful insights for business decision making, then you need to get your hands dirty with some badass data analysis courses.
In this article we are going to look at the best data analysis courses and certifications online to help you launch a career in data analysis.
Through these courses you’ll learn both qualitative data analysis and quantitative data analysis techniques. These are the primary methods of data analysis today.
In addition to that, you’ll also discover how to use Python for data analysis and visualization through a foundational business data analysis class.
By the end you’ll be able to use Excel like a pro for financial intelligence analysis.
Let’s get started.
Here is a detailed summary of what you’ll learn in each of these data analysis courses on Coursera.
We’ll look at what each course is about, what you’ll be able to do after the course as well as the requirements and skill level you need to have before starting any of these courses
1. The Data Scientist’s Toolbox
In this data analysis course, you will get an introduction to the main tools and ideas in the data scientist’s toolbox.
The course gives you an overview of the data, questions, and tools that data analysts and data scientists work with.
There are two components to this course.
The first is a conceptual introduction to the ideas behind turning data into actionable knowledge.
The second is a practical introduction to the tools that will be used in the program like:
- Version control
- Markdown
- Git
- GitHub
- R
- RStudio
This course is part of multiple programs.
And this course can be applied to multiple Specializations or Professional Certificates programs. For this reason, this course suits to mentioned as one of the best Coursera courses for data analysis.
Completing this course will count towards your learning in any of the following programs:
Data Science: Foundations using R Specialization
Rating: 4.6 stars (28, 527 ratings).
Level: Beginner.
Students: 516, 255.
Duration: 19 hours.
Language: English.
Subtitle: French, Korean, Russian, English, Spanish.
2. Introduction to Data Science in Python
This Coursera course for data analysis will introduce you to the basics of the python programming environment. Including fundamental python programming techniques such as:
- Lambdas
- Reading and manipulating CSV files
- The NumPy library
The course will also introduce you to data manipulation and cleaning techniques using the popular python pandas data science library.
Additionally, this course will introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as GROUP BY, merge, and pivot tables effectively.
By the end of this course, you will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.
Rating: 4.5 stars (3, 441 ratings).
Level: Intermediate.
Students: 501, 673.
Duration: 21 hours.
Language: English.
Subtitle: English.
Related:
Data Engineering Courses
Power BI Tutorials
3. Mastering Data Analysis in Excel
This data analysis course will prepare you to design and implement realistic predictive models based on data.
In the Final Project (module 6) you will assume the role of a business data analyst for a bank, and develop two different predictive models to determine which applicants for credit cards should be accepted and which rejected.
Your first model will focus on minimizing default risk, and your second on maximizing bank profits.
These two models should demonstrate to you in a practical, hands-on way the idea that your choice of business metric drives your choice of an optimal model.
The second big idea of this course seeks to demonstrate that your data-analysis results cannot and should not aim to eliminate all uncertainty.
Your role as a data analyst is to reduce uncertainty for decision-makers by a financially valuable increment while quantifying how much uncertainty remains.
You will also learn to calculate and apply to real-world examples the most important uncertainty measures used in business, including:
- Classification error rates
- Entropy of information
- Confidence intervals for linear regression
By the end of this course, you’ll be ready to learn any other Excel functionality you might need in the future.
Since this course gives you many details about excel, it has earned its place here as one of the best courses for data analysis on Coursera.
Rating: 4.2 stars (3, 441).
Level: Beginner.
Students: 337, 544.
Duration: 10 hours.
Language: English.
Subtitle: English.
4. What is Data Science?
The art of uncovering the insights and trends in data has been around since ancient times.
The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year.
Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science.
In this course for data analysis on Coursera, you will meet some data science practitioners and you will also get an overview of what data science is today.
This course is part of multiple programs
So it can be applied to multiple Specializations or Professional Certificates programs.
Completing this course will count towards your learning in any of the following programs:
Key Technologies for Business Specialization
IBM Data Science Professional Certificate
Introduction to Data Science Specialization
IBM AI Foundations for Business Specialization
Rating: 4.7 stars (39, 448 ratings).
Level: Beginner.
Students: 337, 544.
Duration: 10 hours.
Language: English.
Subtitle: English, Russian.
5. Excel Skills for Business: Essentials
In this first course of the specialization, Excel Skills for Business, you will learn the essentials of Microsoft Excel.
Within six weeks, you will be able to expertly:
- Navigate the Excel user interface
- Perform basic calculations with formulas and functions
- Format spreadsheets
- Create visualizations of data through charts and graphs
Whether you are self-taught and want to fill in the gaps for better efficiency and productivity, or whether you have never used Excel before, this course will set you up with a solid foundation to become a confident user and develop more advanced skills in later courses.
A broad range of practice quizzes and challenges will provide great opportunities to build up your skillset.
Spreadsheet software is one of the most ubiquitous pieces of software used in workplaces across the world.
Learning to confidently operate this software means adding a highly valuable asset to your employability portfolio.
At a time when digital skills jobs are growing much faster than non-digital jobs, make sure to position yourself ahead of the rest by adding Excel skills to your employment portfolio.
Rating: 4.9 stars (24, 985 ratings).
Level: Beginner.
Students: 373, 790.
Duration: 26 hours.
Language: English.
Subtitle: Arabic, French, Portuguese (Brazilian), Vietnamese, Russian, English, Spanish, Hungarian.
6. Python for Data Science and AI
This introduction to Python will kickstart your learning of Python for data science, as well as programming in general.
This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours.
The instructor will take you through python basics where you will program your first-ever program, as well as look at types and string operation.
Additionally, you will also learn about Python data structures including:
- Lists and Tuples
- Sets
- Dictionaries
But that’s not all, the instructor will also take you through Python programming fundamentals which consist of conditions and branching, loops, functions, and objects and classes.
Then, you will also take a look at what it means working with data in Python, you will do this by reading files, writing files, loading data with Pandas, and Numpy.
Finally, you will create a project to test your skills.
Rating: 4.6 stars (18, 504 ratings).
Level: Beginner.
Students: 218, 818.
Duration: 27 hours.
Language: English.
Subtitle: French, Portuguese (Brazilian), Vietnamese, Korean, German, Russian, English, Spanish, Persian.
7. Introduction to Probability and Data with R
This data analysis course on Coursera introduces you to sampling and exploring data, as well as basic probability theory and Bayes’ rule.
You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference.
A variety of exploratory data analysis techniques will be covered by the instructor, including numeric summary statistics and basic data visualization.
Additionally, the instructor will guide you through installing and using R and RStudio (free statistical software), and you will use this software for lab exercises and a final project.
You will also learn about designing studies, explore data via numerical summaries and visualizations, and learn about rules of probability and commonly used probability distributions.
The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.
By the end of this data analysis course, you will have gained skills in:
- Statistics
- R Programming
- Rstudio
- Exploratory Data Analysis
Rating: 4.7 stars (4, 340 ratings).
Level: Beginner.
Students: 189, 799.
Duration: 15 hours.
Language: English.
Subtitle: English, Korean.
8. Data Analysis with Python
In this Coursera course for data analysis, you’ll learn how to analyze data using Python.
This course will take you from the basics of Python to exploring many different types of data.
You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!
The topics covered in this course are:
- Importing Datasets
- Cleaning the Data
- Data frame manipulation
- Summarizing the Data
- Building machine learning Regression models
- Building data pipelines
This course, Data Analysis with Python will be delivered through lectures, labs, and assignments.
It includes the following parts:
Data Analysis libraries: you’ll learn to use Pandas, Numpy, and Scipy libraries to work with a sample dataset.
The instructors will introduce you to pandas, an open-source library, and you will use it to load, manipulate, analyze, and visualize cool datasets.
Then the instructors will introduce you to another open-source library, sci-kit-learn, and you will use some of its machine learning algorithms to build smart models and make cool predictions.
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge.
Rating: 4.7 stars (11, 954 ratings).
Level: Beginner.
Students: 139, 416.
Duration: 24 hours.
Language: English.
Subtitle: Arabic, Vietnamese, Korean, Turkish, English.
9. Exploratory Data Analysis
This course for data analysis on Coursera covers the essential exploratory techniques for summarizing data.
These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.
Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data.
The instructors will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics.
Additionally, the instructors will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
You will look at some of the workhorse statistical methods for exploratory analysis.
These methods include clustering and dimension reduction techniques that allow you to make graphical displays of very high dimensional data.
Finally, you’ll look at two case studies in exploratory data analysis.
The first involves the use of cluster analysis techniques, and the second is a more involved analysis of some air pollution data.
Rating: 4.7 stars (5, 601 ratings).
Level: Beginner.
Students: 131, 144.
Duration: 57 hours.
Language: English.
Subtitle: French, Portuguese (Brazilian), Chinese (Simplified), Vietnamese, Korean, Russian, English, Spanish.
10. Introduction to Data Analysis Using Excel
Did you know that the use of Excel is widespread in the industry?
It is a very powerful data analysis tool, that almost all big and small businesses use in their day to day functioning.
This is an introductory course in the use of Excel and is designed to give you a working knowledge of Excel with the aim of getting you to use it for more advanced topics in Business Statistics later.
The course is designed keeping in mind two kinds of learners, those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills.
The course takes you from basic operations such as reading data into excel using various data formats, organizing and manipulating data, to some of the more advanced functionality of Excel.
All along, Excel functionality is introduced using easy to understand examples which are demonstrated in a way that learners can become comfortable in understanding and applying them.
To successfully complete course assignments, you must have access to a Windows version of Microsoft Excel 2010 or later.
Rating: 4.7 stars (6, 440 ratings).
Level: Beginner.
Students: 135, 538.
Duration: 20 hours.
Language: English.
Subtitle: Arabic, French, Portuguese (Brazilian), Russian, English, Spanish.
11. Data Visualization with Python
“A picture is worth a thousand words”. We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets.
Data visualization plays an essential role in the representation of both small and large-scale data.
One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data, and findings in an approachable and stimulating way.
Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.
The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people.
Various techniques have been developed for presenting data visually but in this data analysis course, you will be using several data visualization libraries in Python, namely:
- Matplotlib
- Seaborn
- Folium
Rating: 4.5 stars (8,187 ratings).
Level: Intermediate.
Students: 87, 946.
Duration: 18 hours.
Language: English.
Subtitle: English, Vietnamese.
Conclusion
In the present data-savvy world, the possibilities of data analytics are immense.
And with the right knowledge and skills, you can grab lucrative work opportunities.
In these data analysis courses on Coursera, you will learn how to organize, analyze, and interpret these new and vast sources of information.
Here is a summary of what you will learn in these data analysis courses:
- You’ll learn how to leverage a software tool to visualize data, extract information, better understand the data, and make more effective decisions
- Basic principles of constructing data graphics.
- How to use Pandas
- Python basics
- The essentials of Microsoft Excel
I hope these data analysis courses on Coursera help you learn the ins and outs of data analysis and launch a successful career in this lucrative field.
Have you ever taken any of these Coursera courses for data analysis before?
If yes, please share your experience in the comments below.
