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4.6 ★★★★★ (55,391 Ratings)
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Do you want to follow a complete Data Science career path/track? IBM’s Professional Certificate Program gives you the opportunity to develop the skills required as a Data Science Professional (Beginner level) but not a data scientist. IBM Data Science Professional Certificate program provides all including data analysis, data visualization, python, databases, SQL, statistical analysis, predictive modeling, and machine learning Algorithms using open-source tools and libraries.

Entry level Job ready tools and skills for 2021 & onward
Programming or CS knowledge is not required
Digital Badge from IBM
Professional Certificate from Coursera

Discover how you can expand your expertise with a variety of training options through IBM’s diverse offerings.

This IBM data science professional certificate has very engaging and informational courses which are very much suitable for those who want to start a career in data science and Machine Learning. All the courses in this specialization have been taught by highly experienced professionals. And this specialization can be considered as a very good resource to not only improve your skills but also start a career in this field. Most of the learners who have enrolled in this specialization claimed to have gotten a promotion or increase in pay. Discover ‘2024’s Leading Data Science Mathematics Training‘ to stay ahead in the fast-evolving field of data science.

Online Course Effectiveness Score (Learn More)
Content	Engagement	Practice	Career Benefit
Excellent ★★★★★	Excellent ★★★★★	Fair ★★★☆☆	Fair ★★★☆☆

Note: This IBM Professional Certificate is for beginners. Checkout Advanced Data Science with IBM Specialization for advanced topics.

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IBM Data Science Professional Certificate (9 Courses):

The IBM Data Science Professional Certificate is worth investment if you’re a beginner having no domain knowledge but wanted to start a career in Data Science. Overall the certificate program is well structured covering both the theoretical concepts as well as practical exercises using relevant tools.

Course 1: What is Data Science? (★★★★★ 4.7 | 41,917 ratings | 7,813 reviews)
In this course, you will get an overview of what today’s data science looks like. The instructor has a variety of topics explained in the course like what data scientists do. What are data science in business and the approaches that companies can use to start working with data science?
Course 2: Tools for Data Science (★★★★★ 4.5 | 19,949 ratings | 3,022 reviews)
Now in this course, you can learn everything there is about Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and data science experience. With that, the instructor is also going to talk about the use of each of the tools and the programming languages that can be executed.
Course 3: Data Science Methodology (★★★★★ 4.6 | 15,219 ratings | 1,786 reviews)
This course comes with only one purpose and that is to share a methodology that one can use within data science and make sure that the data, that is being used in problem-solving is exactly relevant and properly manipulated to address the question at hand.
Course 4: Python for Data Science and AI [Course not available] (★★★★★ 4.6 | 19,412 ratings | 3,059 reviews)
In this engaging course, the instructor will take you from zero to programming in Python in just a few hours. You will learn all the Python fundamentals that include data structures and data analysis and also hands-on exercises will help you develop a good understanding of the topics.
Course 5: Databases and SQL for Data Science (★★★★★ 4.7 | 12,352 ratings | 1,414 reviews)
The aim of this course is to introduce learners to all the relational database concepts and help them understand, learn, and then apply the foundational knowledge of the SQL language. With that, you will also learn to create a database instance in the cloud and much more.
Course 6: Data Analysis with Python (★★★★★ 4.7 | 12,507 ratings | 1,814 reviews)
This course has been designed to help you understand the right way to analyze data using Python. The course starts by teaching the basics of Python and then you will be exploring the many different kinds of data. Other topics like performing simple statistical analysis, creating meaningful data visualizations, and much more will be discussed.
Course 7: Data Visualization with Python (★★★★★ 4.5 | 8,552 ratings | 1,197 reviews)
The main goal of this course is to teach you how to make the most out of data and present it in a form that makes sense to people. The course teaches a variety of techniques for presenting data in an effective manner.
Course 8: Machine Learning with Python (★★★★★ 4.7 | 10,136 ratings | 1,665 reviews)
This course aims to dive deep into the basics of machine learning using an approachable and well-recognized programming language which is known as Python. You will learn about the purpose of machine learning and where it can be applied in the real world. And also understand all about the machine learning algorithms.
Course 9: Applied Data Science Capstone (★★★★★ 4.7 | 4,949 ratings | 620 reviews)
This capstone project course is going to give you a taste of what data scientists do in real life while working with data. You will learn about data location and different location data providers like Foursquare and more in this course.

IBM Data Science Professional Certificate Review [Sample]

Top IBM Data Science Professional Certificate Review:

Here are the real users of IBM Data Science Professional Certificate Review and its 9 courses.

Positive Reviews:

I heard there are 9 amazing and equally competitive courses in this specialization and among them, I decided to take the “what is data science” course. I must say that this was an excellent introductory course for someone who has zero experience in this field. All the learning content was quite interesting and easy-to-understand. Thus taking this IBM Data Science Professional Certificate course turned out to be well for me. (Lauren J, ★★★★★).
Before enrolling in the data science course, I had a little interest in this field but since it was a part of my studies so had to take the course. But I must say that the instructors have simplified all the contents of this course that made it very easy for me to understand all of it. The other learning tools and materials were also quite helpful. (Shubham P B, ★★★★★).
I have always been a fan of Dr. White as he has quite an experience in the field of data science. And that is why I took the “what is data science” course and it turned out great for me. His personal experience with data science made even difficult concepts look convenient to understand. (Meseret G, ★★★★★).
From this amazing IBM specialization, I took the “tools for data science” course and must say that it was worth all the effort that I had to put in. The learning content was well bifurcated and fed to the learners in the best possible manner. Even the difficult concepts were easily digestible and I had a lot of fun learning from this course. (Riyaz R, ★★★★★).
The tools that are required in Machine Learning were all briefed in the tools for a data science course and that is why I had to enroll in this particular course. It helped me a lot in developing a good understanding of all the tools that we need in the ML and data science field. (Shridhar H, 5 stars).
I have taken a variety of online courses my whole life that have helped me a lot in learning different things. But this one undoubtedly gave me a happy and calm feeling about what I have achieved. Also thanks to the financial help that Coursera has given me to help move forward. You guys are truly amazing and life changers. (Carlos J B A, ★★★★★).
The tools for data science provided me with a first-rate introduction to IBM Watson Studio and all the various data science tools including Jupyter Notebooks and R Studio. Before this course, I was quite reluctant in understanding all the concepts as they were quite confusing. But after completing this course, my interest in data science grew bigger. (Christopher A B, ★★★★★).
The data science methodology course was very informative and the step-by-step guide helped me understand how to create a data science project. The course has presented all the concepts in a very engaging and easy way and even the quizzes and assignments were so engaging and a good source of reviewing all the concepts. (Jonathan M, ★★★★★).
I would say that this data science methodology course was quite a challenge for me as I had no prior experience in this field. But thanks to the instructors for being so thorough and making me understand even the difficult concepts effectively. Though the whole course was interesting So this the IBM Data Science Professional Certificate Review from my side customer/client interaction in the data science workflow was the one that I enjoyed learning the most. (H Victor, ★★★★★).
I was able to complete quite a number of courses from this IBM series but this IBM data science Professional Certificate methodology was my most favorite course. The 10 questions that we were supposed to answer turned out to be mind-opening. And the repetition after every video made it easier for the important concepts to stick to mind. (Selemani S J, ★★★★★).
From this specialization, I have taken three courses till now and thought I should share my experience with these three courses. The first course has a lot of data science videos and it seemed to be the video version of the book “Completing on analytics”. Then the second course was all about the explanation of IBM’s Watson ecosystem. Then came the third course that had most of the learning content in it. It was a beautiful simple execution of a lot of concepts about data science. Thus I was able to learn a lot of concepts through these three courses. (Austin F, ★★★★★).
This data science methodology paints an overall picture of the complete set of steps that are to be followed while working on a Data science project. The best part of this course was the exercises and quizzes where we are required to solve a problem to identify the cuisine of any recipe using a decision tree algorithm. The Python coding part was quite tough and had a little information in it but then I’d say the overall course was great. (Prabhakaran E, ★★★★★).
The Python for data science and AI course is very suitable for all those people who don’t know what Python is. The course just starts with the basics and then gradually focuses on data structures that are essential for data science. So, I would say if you have some basic understanding of Python, then this course is the one to enroll in. (Pranay C, ★★★★★).
I haven’t completed all the 9 courses in the specialization but only 2 and would say that so far both the courses had a lot of useful information that one can get. The only critique here is that the material of the course “what is data science” was a little outed. (Chan H DL, ★★★★☆).
I have taken the “data analysis with Python” course from the specialization and thought should share my experience. The instructor has done an excellent job in providing the Python commands that one should know to do data analysis. The only drawback was some typos and other minor issues which looked a little sloppy. (Karen B, ★★★★☆).

Negative Reviews:

I took the “what is data science” course from the specialization but couldn’t learn the way I hoped to. I couldn’t understand the point of adding the discussion of young data scientists in the course where they were talking about their jobs. The topics sure were good but as I said, couldn’t learn the way I expected to. (Rick N, ★★★★☆).
The description in the “data science methodology” course was very dull and the course had a shortage of better examples. (Ozge I, ★★★☆☆).
The “machine learning with Python” was an extremely hard one to understand because of the very dense mathematics. The laboratories had a lot of typos that made understanding the already so difficult concepts even more difficult to understand. (Kevin L K, ★★★☆☆).
The assignments in the Python for data science course had a lot of errors in them which made me question the instructor’s caliber. Otherwise, the content was satisfactory. (Sujai C, ★★☆☆☆).
The data analysis with Python course was not what I expected it to be. There were mistakes in the lecture videos and many typo errors were found which made me thought the course was not worth my time. (Pauli H, ★★☆☆☆).
The quality of the data analysis with the Python course was quite poor and there were a lot of mistakes and errors in the lecture video. I was expecting to learn something more than what I already know from this course but the situation was otherwise. (Alexander D, ★★☆☆☆).

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