IBM Data Science vs IBM Data Analyst- Which One is Better for you?

IBM Data Science and IBM Data Analyst are the certification programs for data science available on Coursera. Both certification programs have their own identification and popularity. That’s why many people get confused about which one to choose?. So if you are not sure and stuck between IBM Data Science and IBM Data Analyst, read this comparison- IBM Data Science vs IBM Data Analyst. This comparison will help you to decide which one is better for you.

So, without any delay, let’s get started-

IBM Data Science vs IBM Data Analyst

Before going into details, let’s have a quick comparison between both certification programs-

	IBM Data Science	IBM Data Analyst
Rating-	4.6/5	4.7/5
Time to Complete– NOTE- The time, I mentioned here is according to Coursera, you can finish the whole specialization in less time.	11 Months (If you spend 5 hours/week)	11 Months (If you spend 3 hours/week)
Price–	7 Day Free Trial & then $39/month	7 Day Free Trial & then $39/month
Programming Language Used-	Python	Python
Suitable for–	Those who want to learn Data Science skills with no previous programming knowledge.	Those who want to learn Data Analytics with no programming knowledge but some familiarity with high school level math.
No. of Courses-	9 Courses	8 Courses
Pros	• Well Structured Content. • Provides a free IDE on IBM Cloud (limited). • Great course for beginners	• The content of this program is well structured and provides a foundational base in data analytics. • Perfect program for complete beginners to gain hands-on skills practice by working with a variety of data sources, project scenarios, and data analysis tools, such as Excel, SQL, Python, Jupyter Notebooks, and Cognos Analytics.
Cons	• The first What is Data Science? the course is very basic and you may get bored if you have some previous understanding of data science. • The Python for Data Science and AI course is not a complete Python course. This course teaches only the required knowledge of Python for data science.	• The final assignment of the course Data Visualization with Python is tough to complete because the instructor didn’t explain too much about the assignment concepts. •The Python for Data Science and AI is not a well-sequenced and complete course to learn Python. You need some python textbooks to supplement your learning.
	Check  IBM Data Science Professional Certificate	Check IBM Data Analyst Professional Certificate

So, this is a quick comparison between IBM Data Science vs IBM Data Analyst. Now let’s see the topics covered in both Certification programs-

But before covering the topics, I would like to mention one thing. Both certification programs are offered by IBM, that’s why there are 4 courses that you can find in both certification programs. These courses are Python for Data Science and AI, Databases and SQL for Data Science, Data Analysis with Python, and Data Visualization with Python.

That means these 4 courses are available in both certification programs. So let’s see what topics are covered by both certification programs.

Topics Covered by IBM Data Science Professional Certificate–

Course 1-What is Data Science?

The first course is all about Introduction to Data Science. But this introduction is a very high-level introduction with interviews with students and professionals, explaining their experience in the data science field.

The whole IBM Data Science Professional Certificate Program is designed for beginners that’s why the first course is very basic. Some people find this course boring because they already know about data science. But for complete beginners, this course is the first step towards data science.

So if you already have some background knowledge in data science, you can skip this course. But it’s good to complete and know the experience of professional data scientists.

Course 2- Tools for Data Science

This course is all about open-source tools used in data science. The main focus of this course is on tools like Jupyter Notebooks, RStudio, Zeppelin, GitHub, and IBM Watson. This course makes you familiar with these tools. And you will get to know what each tool is used for, what programming languages they can execute, their features and limitations.

Course 3- Data Science Methodology

This course is all about how to think like a Data Scientist. This course will help you to understand each and every stage of Data science methodology. I really appreciate this course because, in data science, strong analysis is important. And this course will teach you from Business understanding to the deployment part.

The best part about this course is its step by step approach. The course begins with understanding the business problem and move in the step-wise approach to the model deployment.

Course 4- Python for Data Science and AI

As the name sounds, this course teaches Python basics, Pandas, and NumPy. This course provides only the required knowledge of Python for data science. All the modern methods and python libraries for data manipulation are included.

What I think about this course is that this course is not a complete Python Course. This course teaches only those topics that are important for data science. Some previous knowledge in python is good to have before starting this course.

This course also includes the first project of this IBM Data Science Professional Certificate Program. In the project, you have to analyze a set of economic data using Watson Studio.

Course 5- Databases and SQL for Data Science

This course is much practical than other courses. In this course, you will learn how to build databases, how to collect and analyze the data using Python. The hands-on project in this course is also very interesting and challenging.

If you are a beginner in SQL, then definitely you will learn a lot from this course. The whole course is a well structured and perfect balance between theoretical and practical knowledge.

Course 6- Data Analysis with Python

This course will help you to learn statistics in an easy way. This is really a wonderful course for learning Data Analysis using Python. In this course, you will learn a range of data analysis techniques, starting from importing and wrangling data to statistical analysis and modeling.

The best part about this course is that explains useful libraries( Pandas, Numpy, Scipy, and scikit-learn) and methods. And what I didn’t like in this course is the lab assignments. The lab assignments in this course need improvement.

Course 7- Data Visualization with Python

This course is all about data visualization using Python. Data Visualization is an important topic in data science. The course introduces a range of data visualization techniques like line graphs, pie charts, bar charts, and specialized visualizations like Waffle and Folium.

Course 8- Machine Learning with Python

This course covers a lot of machine learning topics like simple regression models, classification, clustering, and recommendation systems. The course explained the mathematical and theoretical foundations behind some of the machine learning algorithms.

In the final project of this course, you have to apply four different types of machine learning algorithm to a data set and check which is the best.

Course 9- Applied Data Science Capstone

The last but not the least- The Capstone Project!

The capstone project has two parts. In the first part, there is another learning module, where you have to cover the Foursquare API to get location details.

The second and final project of this course is totally open-ended. In that project, you have to prepare your own questions to answer with the tools you had learned. The only requirement for this project is to use the Foursquare API, use data analytics, and create a Folium map as a part of the presentation.

The freeform nature of the project will force you to do a lot of self-learning in order to complete it. After completing this course, you have a report, a blog-post, and a notebook with complete code that you can showcase in your portfolio.

Now let’s see the-

Topics covered by IBM Data Analyst Professional Certificate–

Course 1- Introduction to Data Analytics

The first course provides a gentle introduction to data analytics with the help of real-world examples. This course will clear your doubts between Data Analyst, Data Scientist, and Data Engineer. You will get to know about the roles and the responsibilities of a Data Analyst.

In this course, you will also explore the fundamentals of gathering data, cleaning data, analyzing the data, and how to share your data with the use of visualizations and dashboard tools.

In a nutshell, this is an excellent and insightful course for beginners that provides an overview of all the bricks, tools, and concepts making the Data Analytics field.

Course 2- Excel Basics for Data Analysis

Excel is an important tool for working with data. That’s why this course will teach basic working knowledge of Excel spreadsheets for Data Analysis. In this course, you will learn basic level data wrangling and cleansing and how to analyze data through the use of filtering, sorting, and using pivot tables within the spreadsheet.

In a nutshell, this course is well designed, detailed, and good for a beginner and intermediate learner.

Course 3- Data Visualization and Dashboards with Excel and Cognos

This course is more focused on hands-on practice. In this course, you will gain a basic understanding of using spreadsheets as a data visualization tool. You will learn how to create data visualizations, such as charts or graphs.

This course will also cover another dashboarding solution called Cognos Analytics. And you will explore Cognos by creating visualizations, building a simple dashboard, and discovering its advanced features.

Course 4- Python for Data Science and AI 

This is the same course available in  IBM Data Science Professional Certificate. In this course, you will learn Python basics, Pandas, and NumPy. This course provides only the required knowledge of Python for data science. All the modern methods and python libraries for data manipulation are included.

There is one project in this course where you have to analyze a set of economic data using Watson Studio.

Course 5- Databases and SQL for Data Science with Python

 This course covers the basic and advanced SQL and databases like how to build databases, how to collect and analyze the data using Python. 

If you are a beginner in SQL, then definitely you will learn a lot from this course. The whole course is a well structured and perfect balance between theoretical and practical knowledge.

Course 6- Data Analysis with Python

 In this course, you will learn a wide range of data analysis techniques, starting from importing and wrangling data to statistical analysis and modeling.

The best part about this course is that explains useful libraries( Pandas, Numpy, Scipy, and scikit-learn) and methods. But the lab assignments of this course are not well structured and need improvement.

Course 7- Data Visualization with Python

This course covers data visualization techniques like line graphs, pie charts, bar charts, and specialized visualizations like Waffle and Folium.

But the final assignment of the course is tough to complete because the instructor didn’t explain too much about the assignment concepts in the course.

Course 8- IBM Data Analyst Capstone Project

This is the last course of this certification program where you have to apply various data analytics skills and techniques that you have learned throughout this program.

In this final Capstone Project, you have to collect data by scraping the internet and using web APIs, then you have to clean your dataset with various techniques, and after cleaning the data, you have to analyze the dataset to find the distribution of data, presence of outliers and the correlation between different columns.

And then you have to create visualizations using the developer survey data and create a dashboard using IBM Cognos Analytics.

And after completing these steps, you have to finally create a compelling story that helps to clarify your analysis in an easy to understand presentation.

In short, this last capstone project is a good opportunity to practice almost all the concepts studied in this whole program.

So now you knew the topics covered in both specialization program. But the most important question is IBM Data Science vs IBM Data Analyst– Which One is Better for you?

To answer this question, first, you have to understand the difference between Data Scientist and Data Analyst. So let’s understand the difference between Data Scientist and Data Analyst-

Data Scientist vs Data Analyst

A data scientist is a person, who works on a vast amount of data. This data may be structured as well as unstructured. They use their all skills like statistics, programming, and machine learning for creating a tactical plan. A data scientist has the power of all data editor activities. For business-related decision making, a data scientist has a higher probability to make a decision.

A data analyst takes the data, perform analysis on this data and help companies to make better decisions. Data analysts perform analysis on numeric as well as other kinds of data. Then convert it into the English language so that anyone can understand. After translating it to the English language, the upper management uses this data to make a better decision, which helps them in the business.

Now let’s see what skill sets are required for Data Scientist and Data Analyst-

Data Scientist: Skill Set

A Data scientist required following skills sets-

1. A Data scientist must be a master in Statistical and Analytical skills.
2. They should have in-depth knowledge of Machine Learning and Deep Learning.
3. A Data scientist must have data mining skills.
4. In-depth knowledge of programming languages( Python, R, SAS).

Data Analyst: Skill Set

1. Data warehousing. (means to deal with data processing like collection, cleaning, and other processes).
2. Adobe and Google Analytics.
3. Must have programming Knowledge. (It is not mandatory but a plus if you have).
4. Statistical Skills.
5. Data Visualization Skills.
6. Database Knowledge like SQL.
7. Spreadsheet Knowledge.

I hope now you understood the basic difference between Data Scientist and Data Analyst. now let’s see who should enroll in which certification program between IBM Data Science and IBM Data Analyst.

Who Should Enroll in IBM Data Science Professional Certificate?

• Perfect course for Data science beginners who are planning to enter in Data Science field. 
• This program is also good for those who have been off the tools recently.

Who Should Enroll in IBM Data Analyst Professional Certificate?

• Those who are a beginner and want to gain data analytics skills.
• And those who are familiar with high school level math.

NOTE- Completing any course will not make you a data scientist or Data Analyst. These courses will only provide the necessary knowledge of data science and data analytics and a few hands-on projects. So after completing these courses, you have to work on projects with the skills you learned in these courses and expand your portfolio with some other unique projects.

Conclusion

I hope this IBM Data Science vs IBM Data Analyst comparison has cleared your doubts and now you can easily choose the one which suits you. If you have any questions, feel free to ask me in the comment section. I am here to help you. And If you found this article helpful, share it with others to help them too.

• Check IBM Data Science Professional Certificate
• Check IBM Data Analyst Professional Certificate

All the Best!

Happy Learning!

Thank YOU!

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