Review of IBM Data Science Professional Certificate on Coursera

The IBM Data Science Professional Certificate on Coursera is one of many stepping stones on your path to becoming a data scientist. This certificate program consists of nine courses that take students from the fundamentals of data science to developing their own custom capstone project. It’s a well-known, well-crafted online education program that’s been around for a long time, with 234,952 enrollees when this article was written. This certification is an excellent place to start if you want to change careers and work in data science.

I’d recommend it to anyone, whether they’re a beginner or an intermediate. Beginners will get a broad overview of data science fundamentals. Intermediate students will benefit from the capstone project because it allows them to develop their own project, source their own data, and write their own code. Starting your portfolio with a mandatory GitHub account, writing a research paper, and writing a blog article is an added bonus.

If they make it to the end, especially if they enjoy the last two courses, curious students will know whether a data science career is a good fit.

Why You Should Consider IBM Data Science Professional Certificate on Coursera

Over the last few years, there has been a significant increase in the demand for skilled and qualified data scientists. This is due to the increasing adoption of data science in various industries and the positive impact the domain has had on the industry. There are numerous ways to obtain appropriate skills and certification in data science, ranging from mini-courses to DIY routes. However, receiving training and certification from reputable sources is generally assumed to be more beneficial. Is getting the IBM Data Science Professional Certificate worth it?

The IBM Data Science Professional Certificate is well worth your time and money. The course offers expert guidance, assists in developing extensive skills and expertise, and focuses on what is required to gain a competitive edge in the job market. It’s also cost-effective, flexible, and available online, with no prerequisites for participation. There are many other ways to learn data science and meet the functional requirements to break into this field’s job market.

As a result, aspiring data scientists now have a wide range of courses and certifications to become data scientists. Stay tuned to discuss some of the most distinguishing features of an IBM Data Science Professional Certificate later in this article. I am confident that reading this article will assist you in determining if this certificate is the best fit for your specific needs, allowing you to make an informed decision about whether or not to pursue it.

What Is the IBM Data Science Professional Certificate?

There are currently 2.5 million job openings in data science and related fields, many of which are high-paying positions with starting salaries of over $80,000 in the United States. It’s undeniably the hottest profession of the decade, and many people have turned to more prestigious to be better prepared to take on these roles. The IBM Data Science Professional Certificate program is comprehensive, with nine skill-building courses that cover everything you need to know to land a good data science entry-level job. Students gain skills and expertise in the following areas through the use of theory and practice applied learning techniques:

Module 1: What is Data Science?

Well, I’m not sure what else to say. It’s a standard introductory course that does an excellent job: This involves completing projects and publishing reports using various data science and machine learning skills, techniques, and tools. It provides a broad overview of the subject and aids in the removal of some initial doubts. The distinguished definition of who a data scientist is was my main takeaway. It also introduced me to this data science book, which I strongly suggest you read.

Module 2: Tools for Data Science

This one will blow your mind if you’re a total newbie to this space like me. This involves gaining experience with common data science tools like Jupyter notebooks. The module inundates you with tools that you can apply to various use cases you may encounter during your data science career. It is not necessary to memorize all of them, but it is a good place to start if you are faced with a new challenge.

Module 3: Data Science Methodology

You’ll discover how to organize your work. Sounds tedious because you’ve finally decided to learn to code? Yes, it sounds familiar, but believe me when I say that you don’t want to miss it. When analyzing and coding larger projects, it’s easy to get lost. Knowing how to stick to a framework is crucial to achieving your goal. This involves gaining knowledge of the fundamental steps and methodologies involved in solving data science problems.

Module 4: Python for Data Science, AI & Development

The title is more intriguing than the content. This involves getting started with Python by using a Jupyter notebook. You’ll learn the fundamentals of Python and how to work with common data types. That’s all there is to it; no fancy AI, no programming experience, just a little more theory, and some basic coding.

Module 5: Python project for Data Science

At long last, something to do! In this project, you’ll put what you’ve learned in class to use in a situation that’s a little more real-life. I’d say it’s doable for anyone and gives you some confidence in your new skills — great module and a lot of fun to complete!

Module 6: Database and SQL for Data Science with Python

You’ve never used SQL before? Then this is the course for you. It is an excellent introduction to the theory and will provide you with a lot of coding experience. This involves querying databases with SQL code and learning about relational databases.

Module 7: Data Analysis with Python

This module will make you feel like going from 0 to 100. It teaches you how to manipulate data, analyze data, and create models. Importing and cleaning datasets, analyzing datasets, and building and evaluating data models and pipelines are all possible with Python. Modules 5 and 6 do not prepare you for this, so you may feel overwhelmed if you have never done it before. Take your time, read it carefully, and digest it line by line, visual by visual. It will come in handy later.

Module 8: Data Visualization with Python

Nothing beats seeing your data in a visual format. This involves using various data visualization tools, techniques, and libraries to learn data visualization. It will assist you in comprehending it, and this module will show you how to do so. I’m not going to lie: I despise Python for data visualization. It takes a long time, but the insights you gain are invaluable, so pay attention.

Module 9: Machine learning with Python

Another fancy title that promises more than it delivers. This module will give you an overview of the most common and fundamental machine learning techniques that you can use with Python. This involves understanding and applying various supervised and unsupervised Machine Learning Algorithms to solve real-world problems using Python. This isn’t going to lead you down the rabbit hole. However, if you have no prior experience with the subject, I believe it is an excellent introduction.

Module 10: Applied Data Science Capstone

This was, without a doubt, my favorite module. You get to put all of your newly acquired skills to use on a large project. They demonstrate how to do it first, then you must duplicate it. It’s entirely up to you how difficult you make it for yourself. You can either copy and paste the code from the module and apply it slightly differently, or you can create something new. You’ll get what you want either way: The certificate at the end.

Jupyter Notebook, Artificial Intelligence, Watson, Studio, IBM Cloud, Db2, Pandas, Numpy, Bokeh, Matplotlib, Folium, Seaborn, Scikit-learn, SCIPy, RStudio, Zeppelin, Regression, Clustering, Classification, Location, Methodology, Foursquare, and Recommender Systems are among the additional tools and areas of learning covered. Aside from these topics, the course also covers topics that will help students transition into the role of a data scientist or a related entry-level position.

These skills include learning how to apply problem-solving methodologies to think and work like a data scientist and being exposed to the day-to-day activities of a data scientist in the real world. By investing 3 to 5 hours per week in the program, the full duration of the program can take between 10 and 13 months, depending on which site is used to enroll for the course. Without a doubt, these timelines will vary significantly depending on the student enrolled, his or her learning speed, and the study schedule.

If a full-time student learned one module per day, the entire course could be completed in 2–3 months. Finishing all of the program’s courses and meeting the minimum grading criteria in various quizzes, hands-on assignments, and projects constitutes successful completion of the course. If these requirements are met, and the student has complied with all enrolment terms and conditions, the student will receive a Professional Certificate in Data Science as well as an IBM badge.

What Is the Price of an IBM Data Science Professional Certificate on Coursera?

The full price of the course offered by edX is $411, but it is reduced to around $369.90 when it is on sale. After the 7-day free trial period, Coursera charges $39 per month for a Coursera subscription. The fact that the course is offered online saves students money on additional expenses.

Is it Worth It to Get an IBM Data Science Professional Certificate?

The IBM Data Science Professional Certificate on Coursera is well worth the money. The course combines in-depth theoretical work with hands-on learning techniques to help students develop efficiency in today’s most critical areas of data science roles. Furthermore, this course is intended to provide participants with a solid foundation for further data science learning. Approximately 46% of those who completed this specialized course began a new career in data science, and 19% received a promotion or pay increase.

Currently, there is a huge demand for skilled data science professionals who can analyze data and effectively communicate results to inform data-driven decisions in various industries and employers. Despite this demand, a relatively small pool of individuals can fully meet these companies’ requirements. This is because data science is such a broad field, and a specific employer’s needs may necessitate that these professionals be proficient in more than a few data science tools.

Certifications, such as the IBM Data Science Professional Certificate, provide employers with the confidence they need in the competency of job applicants they are considering hiring. The IBM Data Science program is designed to help those who want to learn and develop career-relevant skills, tools, and a portfolio of projects to gain a competitive advantage during the entry-level job search. Students who complete the course are better prepared for this profession, having demonstrated their ability to solve real-world problems and effectively apply various data science methodologies.

Despite the fact that the program is light on statistics, it emphasizes Python throughout. This is the most well-known primary aspect of the field, and potential employees are expected to be efficient in this area. Students learn data science through relative theory and hands-on practice in the IBM Cloud using real data science and real-world data sets, thanks to an extensive course that focuses on the latest job-ready tools and skill-set. All of the IBM Data Science courses, with the exception of the first, place a strong emphasis on applied learning projects.

The course also includes a series of hands-on labs that allow participants to develop practical skills and apply them to real-world data science problems. The program greatly aids its participants in their transition into a business role within the data science domain because of this element of experiential learning. The course is also entirely online. This allows students to set and maintain flexible deadlines based on what works best for them, making it incredibly convenient and comfortable for them to attend classes at their own pace while remaining engaged in the program sessions.

Participants in the program can also join IBM’s Talent Network after completing the course. This network can be highly beneficial to any data science career aspirant because it provides many of the tools needed to land a dream job with IBM and recommendations and direct access to many IBM opportunities. Several sites currently offer the course, each with slight differences in expected completion time and available features. Even though the differences are minor, you should check them out because, depending on your specific situation, one platform may be better and more cost-effective than the other.

Coursera offers the course for approximately ten months at 5 hours per week, while edX provides the course for about one year and one month at 3–5 hours per week. Furthermore, Coursera charges a subscription-based fee for the program, whereas edX charges a one-time fee for this certification. This gives you more options for scheduling your studies, giving you even more flexibility and adaptability. Furthermore, Coursera has improved the program slightly, making it even more accessible to the general public.

English, Russian, Korean, Persian, Vietnamese, German, Spanish, Arabic, Turkish, French, and Portuguese subtitles are available (Brazilian). Furthermore, Coursera offers a seven-day free trial, while edX offers a one-course free trial, which is ideal for getting a feel for the program before committing to the full program and its cost. While any prior knowledge of the industry or the data science domain will be beneficial, there is no requirement for prior experience to enroll in this course and complete it successfully. The most important requirement is a desire to learn and grow in the field, which makes it simple and straightforward for anyone interested in pursuing a career in data science.

The course is designed to help students advance their careers. Students will have built up an impressive and thorough data science project portfolio by the end of the course and completion of all related courses within, giving them confidence in the functional requirements of the role when entering the job market. Students receive a digital badge from IBM and a Professional Certificate upon completion of the course, which recognizes and attests to their data science proficiency.

How Long Does It Take to Get a Certificate?

Working evenings and weekends for 17 days took me 106 hours. According to the course materials, it takes an average of 195 hours and up to 10 months to complete the program (6 weeks for the capstone course alone). Is that the amount of time it will take you? That is debatable. It depends on how much you already know, how many commitments you have, and the difficulty of your capstone project. I probably spent three months on mine.

Based on the other capstone projects I’ve seen, most people spend two or three weeks on their capstone projects. Furthermore, many of the students enrolled in the course have far more free time than those who work. So, if you have some Python experience, a lot of free time, and a tendency to solve problems quickly, you should be able to complete the certificate in less than three months.

Is it Worth Your Time and Money to Get a Certificate?

Absolutely! Throughout the program, you cover a lot of material and get to practice your data science skills. The Capstone course allows you to put what you’ve learned into practice. The capstone also requires you to create a GitHub portfolio, which I had been putting off but am glad I had to do (my GitHub). You must also write a blog or create a presentation summarizing your findings as part of the capstone.

What Kinds of Social Media Badges Are Given Out?

Through Acclaim, IBM provides attractive badges for each individual course (by Credly). These are both digitally verifiable and open to the public. To see an example of how they work, go to my Acclaim link. The final certificate issued by Coursera signifies the completion of the entire program. While not as attractive as the individual IBM course badges, this certificate is the one you should share on social media.

Are There Any Better Certifications?

Sure. Statistics and data science certificates are available on EdX from Harvard and MIT and Coursera from Johns Hopkins. At Duke University, the University of Michigan, the University of Washington, and other large institutions, there are several excellent Python/R/Statistics-focused certifications. The point, in my opinion, is not whether a certificate is better or worse. Instead, the goal is to approach the material from various angles, each of which reinforces the previous.

For example, despite having recently completed a graduate degree in this field, I was able to gain many new ideas, tools, and techniques from this certification – particularly from the capstone. Learning never ends; you simply gain a better understanding of more approaches and are able to execute them more quickly over time. As a result, I recommend thinking of this certification as a great starting point on a long journey for those who already have a strong foundation in math, statistics, data analysis, or software development.

If you have no prior experience in the field, there are simpler certifications with fewer courses from which you could begin. Some advanced courses and certifications should be pursued after this one.

Conclusion

The IBM Data Science Professional Certificate on Coursera is well worth the investment, as it provides numerous benefits throughout the learning process – particularly with Coursera. It’s also simple to commit to, affordable, and completely flexible. As with anything, the value you gain from this course will ultimately be determined by what you make of it and what you take away from the program. Finally, the IBM Data Science Professional Certificate program is an excellent program for beginners interested in pursuing a career in data science.

If you choose to pursue this program, you will not only be prepared for the short-term functional requirements of an entry-level data science position, but you will also be able to build a strong foundation in the domain and continue your education with more advanced courses.

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