“Get the habit of analysis — analysis will in time enable synthesis to become your habit of mind.”
Frank Lloyd Wright
The IBM Data Science Professional Certificate Program is an excellent point to start your data science journey or even refresh your knowledge in that domain. I consider myself lucky as I got broad understandign through data science with that professionally organized course. Although it’s been a long time since I’ve completed it, I still sometimes go back and look at my notes from that program to refresh some important points in data science. So I feel responsible to share my experience, thus I prepared a brief summary of the program, some useful information if you’re thinking to learn, and improve your data science skills. The program consists of 10 separate courses.
Anyone who decides to make a career transition can start with this program. It’s a well-crafted design for absolute beginners.
Here are the 10 courses that you need to complete, in order to earn the certificate.
1. What is Data Science
This is a smooth introduction to data science. You’ll learn some theoretical information, the definition of data science and what data scientists do, data science topics, and use cases of data science in business. It takes around 5–6 hours to complete. It has section quizzes and a peer-graded assignment.
2. Tools for Data Science
In this course, you’ll get familiarized with the tools that are used in data science. Especially Python, Jupiter notebooks, anaconda environment, RStudio IDE, Git, GitHub, and IBM Watson Studio are the ones that will be useful in your data science journey. It’s around 6–7 hours to complete, it has section quizzes, and a peer-graded assignment.
3. Data Science Methodology
Once you get into a discipline, it’s so important to follow the methodology. I find that part important since you need to have it in mind always during your data science career. You’ll learn major steps in tackling with a data science problem from the business question, data collection to model deployment, and communicating the results. That’ll take 2–3 hours to complete.
4. Python for Data Science, AI & Development
With this module, you’ll kick start hands-on exercises with the magic tool, Python. It has a beginner-friendly introduction to Python. You don’t even need to have a prior-programming experience. It takes 5–6 hours to complete, section quizzes, and a peer-graded assignment.
5. Python Project for Data Science
That new mini-course gives you the opportunity to demonstrate foundational Python skills for working with data in 5–6 hours in total.
6. Databases and SQL for Data Science with Python
This course opens the door to relational database concepts and teaches you the fundamentals of SQL language. You’ll learn through hands-on lab exercises, building and running SQL queries. Besides you’ll access databases from Jupyter notebooks by using both SQL and Python. That course will take around 12-14 hours to complete, section quizzes, and a peer-graded assignment.
7. Data Analysis with Python
In this course you’ll perform importing datasets, cleaning and manipulating data, building machine learning regression models and data pipelines. You’ll learn how to use Python libraries like Pandas, Numpy, Scipy, Scikit-learn, and so on. That relatively broad course will take around 12–14 hours to complete. It has section quizzes, and a peer-graded assignment.
8. Data Visualization with Python
The data visualization part is important in data science since you want to have an impact by displaying your insight to stakeholders or decision-makers. You’ll learn how to use visualization libraries like Matplotlib, Seaborn, and Folium. It takes around 7–8 hours to complete, section quizzes, and a peer-graded assignment.
9. Machine Learning with Python
The most attractive part of data science, machine learning. You’ll start using regression, classification, clustering, recommendation models with real-world data. That course will take around 15–16 hours to complete, similarly it has section quizzes, and a peer-graded assignment.
10. Applied Data Science Capstone Project
I always value thesis, dissertation, or a research assignment in a learning program/academic study. It is the most useful, fruitful part, since you learn and consolidate, struggle on your own, and acquire the knowledge. I can say that a capstone project is one of the most useful parts of the program. You’ll learn a lot while you try to find solutions on different platforms. You can always refer and participate in discussion forums when you are stuck.
Cost
The course is accessed through the Course online learning platform. You need to pay for the monthly price, which is 39 $. If you spend just 3 hours per week, it will take 11 months to complete the entire program. That will cost around 429 $ in total. However, If you can spend a few hours per day, you can complete it in 2 or maximum of 3 months. Thay way you’ll need to pay around 80–120 $.
Grading System
Peer-graded system for the assignments and auto-graded system are used for multiple-choice quizzes.
Alternatives
There are other really succesfull courses that you can learn data science. John Hopkins University provides a 10-course specialization program. They use R programming instead of Python. University of Michigan has Applied Data Science Specialization program, which is intermediate level. That may not fit for absolute beginners. Those have similar monthly cost around 40 $.
Conclusion
In conclusion, I highly recommend this course for someone who wants to start learning and refresh his/her knowledge in data science. This program certainly deserves dedication, time, and the cost that you’ll invest.
On Coursera, it’s mentioned that if you follow a pace of 3 hours/week, you’ll complete the program around 11 months. It took two months for me to complete the whole program. I didn’t dedicate all my days, weekends, and evenings. But I worked during the days. So, if you commit yourself, you can do it in even less than two months.
I hope you find that article useful in your data science journey.
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