Best Coursera Courses for Data Science (With Certificates)

In recent years, massive open online courses (MOOCs) have gained a lot of popularity as a viable means of gaining an education. With more and more people deciding to work remotely from home, it was only a matter of time before getting a competitive education from your computer became an increasingly appealing option.

If you have been interested in going through a MOOC for an education, you’re probably unclear where to start. Between the different sites, courses, and categories on the market today, it can be confusing to know where to go or who to learn from.

While Coursera is a fantastic option for most people looking to improve their software development skills, even that can be a bit daunting if you don’t know what you’re looking for. Below, I have gone through 10 of Coursera’s courses that everyone should consider going through before anywhere else. Take a look at these options and see which is best for you to start at first.

Top 10 Best Coursera Courses for Data Science (With Certificates)

  • Google Data Analytics Professional Certificate
  • IBM Data Science Professional Certificate
  • IBM Data Analyst Professional Certificate
  • Deep Learning Specialization
  • Introduction to Data Science Specialization
  • Data Science Fundamentals with Python and SQL Specialization
  • Machine Learning
  • Applied Data Science Specialization
  • IBM AI Foundations for Business Specialization
  • IBM Data Analytics with Excel and R Professional Certificate

Google Data Analytics Professional Certificate

Google Data Analytics Professional Certificate (GDAPC) is an excellent starting path into a data analytics career in under six months. The course is offered by Google themselves and offers students a fully immersive exploration into the day-to-day activities of a junior data analyst. In the course, students are taught key essential skills like data cleaning or data analysis. They are also given various tools like R programming, spreadsheets, and SQL, just to name a few.

The course is open to people of all experience levels, including those without a degree. These classes are pretty short as well. At a pace of 10 hours per week, a student can get through the entire course in under six months.

Google Data Analytics Professional Certificate

Pros:

  • No Experience Required: Can start without any previous degree in computer science.
  • High Business Opportunities: If you want to get a job, completing this course has an 82% chance of doing so within six months of completion.
  • Very Short: The course can be completed in under six months at less than 2 hours of studying per day.

Cons:

  • Highly Specialized: If you aren’t looking to become a data analyst, this course isn’t going to do much to put you in a good place or teach you much.
  • Fairly Simplistic: For those at a very high level, much of this information can seem a bit elementary compared to other courses out there.

Review: While not for everybody, there is something to be said about becoming a data analyst. If you are just starting and need a foundation in the online world that can translate into a job, this course is for you.

Link to the course

IBM Data Science Professional Certificate

The IBM Data Science Professional Certificate (IDSPC) course is one of the best ways to start your career legitimately and authentically. Offered by the giant itself, IBM, this 11-month course breaks down data science. It gives students of all experience levels a chance to learn many foundational skills useful in the field, like Python programming, data visualization, and analysis, or SQL.

This course offers its students many practical skills and tools that can be directly applied to available real-life jobs. Of those who have graduated from this class alone, over 20% were reported to have immediately begun a career.

IBM Data Science Professional Certificate

Pros:

  • No Experience or Degree Necessary: Can start from the ground up, even if you are a complete beginner.
  • Applicable Skills: Students are taught real-world skills that directly translate to the job in question rather than solely academic situations.
  • Different and Effective Tools: Students are given various tools for improvement, like R Studio, GitHub, or Watson Studio.
  • Highly Competitive Resume Inclusion: Incredibly strong all on its own in a resume or portfolio, getting 20% of all completionists a job without supplemental training.

Cons:

  • A Long Amount of Time: Suggested study pace time is around three hours a week for a total of 11 months. This can be a lot for many students.

Review: If you are someone looking to gain a baseline understanding of data science or computer programming, or you want to go into that work almost immediately, investing your time in the IDSPC course is the right option. Not only does it give you incredible information, but it also does so with real-world exercises that are invaluable in any information-based study.

Link to the course

IBM Data Analyst Professional Certificate

A terrific option for those that wish to get into the data analyst world, the IBM Data Analyst Professional Certificate (IDAPC) course can unlock your potential in the field while giving you job-ready skills no matter your experience.

Offered by IBM, this course is around 11 months to complete and offers real hands-on projects to improve your overall ability. Students are trained in Data Analysis, Spreadsheets, Pivot Tables, and IBM Cognos Analytics.

IBM Data Analyst Professional Certificate

Pros:

  • No Experience or Degree Necessary: Whether or not you have a previous degree or prior experience, this course is something you can do.
  • Applicable Skills: Not only are students given the option to learn valuable skills in general but these skills can also be applied to just about any data analyst job in the world.
  • Highly Competitive Resume Inclusion: 20% of all completionists have begun their career with only this certificate. If used with other course certificates, this can double or even triple.

Cons:

  • Extended Amounts of Time: Because the course requires so much time (11 months), it can potentially be too much schooling if studied just on its own.

Review: This is pretty much the foundational bedrock that any data analyst for IBM should take to heart. It has nine separate classes that break down every aspect of data analysis, making it something that is very much worth your time.

Link to the course

Deep Learning Specialization

Offered by DeepLearning.AI, Deep Learning Specialization (DL) is a great course that teaches students to become experts in Machine Learning. They are taught some of the fundamentals involved in deep learning and AI technology.

Throughout the 5-month long course, students are taught to create various types of neural networks (ANN, CNN, RNN) and analyze DL applications while using standard techniques and optimization algorithms.

Deep Learning Specialization

Pros:

  • Dense Training Time: The course takes around five months to complete while studying around seven hours a week.
  • Competitive Resume Inclusion: Over 11% of students who took this course by themselves could begin a career in the Deep Learning industry.

Cons:

  • Intermediate Level Training Required: Because students are taught so much advanced programming in the course, they must at least have some knowledge under their belt before applying.
  • Highly Specialized: While AI learning is great and may have worldwide potential in the future, it currently isn’t wide enough to warrant being taken seriously outside of specific AI tech spheres.

Review: Deep Learning Specialization is a powerful course for those interested in learning about artificial intelligence. If you are someone that wants to excel in that field, this is the course for you. Otherwise, you may want to consider some of the other options.

Link to the course

Introduction to Data Science Specialization

If you are looking for a great way to launch your data science career, starting with the Introduction to Data Science course (IDS) is something you should consider. This 4-month course is beginner-friendly and is broken into four parts, What is Data Science; Tools for Data Science; Data Science Methodology; and Databases and SQL for Data Science with Python.

The course is offered by IBM and provides students with the essential foundations that all data scientists need to prepare for their careers or gain advanced learning in the field. The course goes over things like Python Programming, Cloud Databases, and how to write SQL, among many other areas.

Introduction to Data Science Specialization

Pros:

  • Beginner-Friendly: Students aren’t required to have any experience or degrees before applying.
  • Foundational Skills: Many of the skills taught in this course are foundational and are essential no matter what area of Data Science you get into.
  • Short Completion Time: While studying at a pace of 3 hours per week, you can complete the course in around four months.

Cons:

  • Potentially Boring: If you are at a higher level, the material covered in this course may be too boring for you as it essentially surrounds the basics.
  • Weak Portfolio Options: Don’t expect to get much more than a very baseline position with this as the sole item on your resume or in your portfolio.

Review: IDS is something that every beginner should start with, no matter where you plan to focus your career. It covers the basics of data science at a level that everyone can understand. It would help if you moved forward with your studying after the course, but it needs to be one of the first on the list.

Link to the course

Data Science Fundamentals with Python and SQL Specialization

Offered by IBM for every person looking to build a career in Data Science, Data Science Fundamentals with Python and SQL (DSFPS) is a course that develops a student’s hands-on experience with several programming tools like Python, SQL, and Jupyter. This 6-month long course trains students in the fundamentals to translate that into any other Data Science field.

The course comprises five separate classes that each run over the early aspects of Data Science. Tools for Data Science goes over some of the most popular tools like GitHub and JupyterLab. Python for Data Science, AI & Development gets into your training for these different programming tools, with a significant focus on Python. Python Project for Data Science is a mini-course that allows you to apply your knowledge of Python in several hands-on exercises. Statistics for Data Science with Python goes over the basic principles of stats and procedures. Databases and SQL for Data Science with Python use these two powerful programming languages to communicate and extract data to become a data scientist.

Data Science Fundamentals with Python and SQL Specialization

Pros:

  • Foundational Information: Great basic information that every prospective studier of Data Science should know.
  • Beginner-Friendly: No prior knowledge is required to take the course, nor is any degree or information dealing with programming languages.
  • Relatively Short Completion: It takes around six months to complete if you are studying at a pace of three hours per week.

Cons:

  • Very Basic Information: Much of the course’s information is basic and fundamental. If you are someone that doesn’t know anything about Python or SQL, this is great. If you are trained in either of these courses, it may not be worth your time.

Review: This course is ideal for students coming into Data Science completely fresh and without any other information. If you are trained in a coding language, you should keep in mind that that is a significant portion of the material you’ll be going over.

Link to the course

Machine Learning

Offered by Stanford, Machine Learning (ML) is a course that gets into programming computers’ science to self-run and acts without interference. Students of ML are taught about pattern recognition, data mining, and algorithmic parametric.

The course is relatively short, depending on how intensely the information is studied. If studied at a pace of six hours per week, it can be completed in just under four months.

Machine Learning

Pros:

  • Offered by Stanford: Stanford is a very prestigious university in California and is well known for its impressive curriculum. By having this course on your portfolio, that reputation can help your chances when applying for a job or promotion.
  • Highly Marketable: If you are interested in the Machine Learning area of data science, this field is growing more prevalent in every company worldwide. Having these skills already makes prospective employers more likely to hire or promote than your contemporaries.
  • Short Amount of Time: Despite the dense amount of information taught, the course can be completed in under four months while at a 6-hour per week study pace.

Cons:

  • Higher Level of Experience: While you do not need to be an expert to learn from this area, you need some coding and programming experience under your belt beforehand.
  • Focused Niche: If you are potentially unsure of where you want to go in your career or need to learn the basics, this course may be too advanced and too focused for you.

Review: Machine Learning is a vitally important course that everyone should learn at some point. However, if you are just starting or are not interested in that part of data science, it may not be the right fit for you right now.

Link to the course

Applied Data Science Specialization

Applied Data Science (ADS) is a course that offers those interested in a career in data science hands-on skills and experience. There, they are taught to use Python as well as how to analyze and visualize data. They are a beginner-friendly course and have various labs and projects to solidify their student’s abilities.

The course is offered by IBM and is broken off into four separate lessons. The course is around six months long if studied at a 3-hour per week pace. Completion of the course also can be put towards getting the IBM Data Science Professional Certificate.

Applied Data Science Specialization

Pros:

  • Beginner-Friendly: Does not require any prior knowledge surrounding Python programming language or aspects of data science.
  • Foundational Topics: The course goes over different aspects of Data Science that are fundamental and foundational to every other area and avenue surrounding Data Science. Understanding these tips is crucial for a beginner.
  • IBM Digital Badge: After completing the course and the completion certificate, students are also given an IBM Digital Badge that recognizes them as a Data Science specialist.

Cons:

  • Simplistic: If you have a higher level of knowledge of Data Science, this course may seem a bit simplistic and straightforward.

Review: Regardless of your status, if you are interested in a career in Data Science, you will want to go through this course. Even if you have a strong understanding of this material, being a recognized specialist makes it worth it all on its own.

Link to the course

IBM AI Foundations for Business Specialization

IBM AI Foundations for Business (IAFB) goes deep into artificial intelligence and its many uses. The course also breaks down various forms of terminology that are often used, like Machine Learning, Deep Learning, or Neural Networks.

The 3-part course is relatively short, taking around two months to complete while at a 3-hour per week pace. It is also easy to get started even for beginners, not requiring any prior knowledge or skills.

IBM AI Foundations for Business Specialization

Pros:

  • An Accessible Course: Not only is the course pretty easy to jump into, but it is also easy for anyone to get involved with, not pushing any concepts that are too advanced for most beginner students.
  • Competitive Knowledge: Students who undergo this course put themselves in an optimal position for companies as they shift more into AI and computerized optimization. Having that knowledge now makes a person potentially indispensable in the future.

Cons:

  • Too Foundational: If you are in this field and have no prior knowledge, taking this course is excellent. If you are already well-versed in AI, much of this may seem redundant.

Review: This course is best if you already have some Data Science under your belt. Even if you can take it without any prior knowledge, using this in tandem with other foundational pieces of information can truly set you apart when putting yourself in a position for a job or promotion.

Link to the course

IBM Data Analytics with Excel and R Professional Certificate

IBM Data Analytics with Excel and R Professional Certificate (IDAERP) kickstarts your career in Data Analytics while mastering data analysis and visualization. The course offers several tools to help develop yourself into a person with job-ready skills for an entry-level position.

This IBM offered, 8-part course takes a person from a complete beginner to a fully skilled and employable data analyst. What’s more, it does this in just around 11 months at a 2-hour per week study schedule. At the end of the course, the student must take a capstone project exam that solidifies their ability to perform in a real-world scenario with real-world datasets.

IBM Data Analytics with Excel and R Professional Certificate

Pros:

  • No Experience or Degree Necessary: You can start as a complete beginner and become a competent expert.
  • Applicable Skills: Unlike other courses that may require additional supplemental training to really make the information usable, this course has a test that proves you can use this material in the real world.

Cons:

  • Extended Amount of Time: Because the course requires so much time (11 months), it may not be worth it to only study this on its own without additional course information.

Review: When it comes to this course, I can’t recommend it strongly enough, but with a caveat. If you know that you want to become a Data Analyst and move into that world as a career path, then this is definitely good for you. That said, if you aren’t sure or don’t feel it’s a good move, look at some of the shorter courses first.

Link to the course

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