Hello guys, if you want to join IBM's Data Science Professional Certification on Coursera but are not sure whether you should join or not or whether you are thinking it's worth it or not then you have come to the right place. Earlier, I have shared the 10 best Coursera courses for data science, and in this article,, I will share my experience with Coursera's popular Data Science Professional certificate. Many people call data science the sexiest job in this 21 country because of its demand by companies that need to have insights into their data for decision-making and high-paying jobs. So for that reason, many people are trying to learn the skills needed for the position of a data scientist, and almost no better place than taking this IBM course for data scientists.
IBM Data Science Professional Certificate
is one of the best online courses that will give you the most needed skills to
get started, and it doesn’t require any skills to get started on your journey in
his field, so let’s discuss more this course and what you will learn when
enrolling in this program.
Is IBM's Data Science Professional Certificate on Coursera Worth it? [Review]
I generally look at three things before joining a course, who is teaching, mean instructor and author, the course curricular and what is covered and not covered in the course, and people's review, I mean what other people who have joined the course are telling about it. Keeping that tradition, here is my review of Coursera and IBM's popular Data Science Professional certification review.
1. The Instructors Review
This program was created by almost 11 instructors working in the IBM company and all of them have a greater position with solid experience in the domain of data science like a Ph.D. degree and senior data scientists, so all of the information you get is based on their experience.
2. Course Content
2.1. What is Data Science?
You will start learning by defining what a data scientist is and what he does inside the company, like what tools are used by the data scientist daily and the skills needed by anyone wanting a career in this industry with some advice for new people. Finally, you will learn the approaches or methods companies need to take to start working with data science.
2.2. Tools for Data Science
After getting some information about the company’s data science role, you will begin in the second portion of this program to learn about the programming languages used by the data scientists, such as python programming language. You will also see the popular data science tools used: GitHub, jupyter notebook, and R studio.
2.3. Data Science Methodology
Now you will get to why companies want a data scientist and the different methodologies used by a data scientist and learn the two stages of data science methodology, which are business understanding and analytic approach. You will see also what we mean by understanding the data and cleaning them and the purpose of data modeling.
2.4. Python for Data Science, AI & Development
Now you’ve got to the most fun part, which is learning the python language by starting with its basics like the different data types and moving to the data structure such as how to store data collection in one variable like lists and tuples and dictionaries and how to use python with data like reading and writing in files and perform web scraping.
2.5. Python Project for Data Science
This course will require you to have experience in python language, and you have completed the previous courses since you will start exploring and testing your skills in this language working with data.
2.6. Databases and SQL for Data Science with Python
You will understand using the SQL language to pull data from the database. Next, you will understand the relationship between the different tables inside the database. Also, you will move to some intermediate level of searching data using string patterns and ranges and how to access the database using python.
2.7. Data Analysis with Python
The most important part of data science is analyzing data, so you will learn to use python to analyze data and explore many different data types. You will learn skills such as preparing data for analysis and performing simple statistics.
2.8. Data Visualization with Python
Using this python package, you will start by understanding the different visualization tools such as matplotlib and create charts such as pie charts and scatter plots. Next, you will move to advanced data visualization and learn about the seaborn library and how to perform word cloud visualization and geospatial visualization using Folium.
2.9. Machine Learning with Python
You will understand machine learning's different types and how they are applied in health care and other industries. Next, you will start applying machine learning algorithms to data such as regression and classification using different algorithms like the SVM and decision trees and learn about applying to clusters and building a recommendation system.
2.10. Applied Data Science Capstone
This the last course in the program will test your knowledge in data science and apply what you have learned in a real scenario, like making RESTful API and using python pandas library to manipulate data and visualize the latter and applying machine learning to your data.
Conclusion
Data science is an intense field, and some companies require you to have more skills than a simple program has reached you, like deep learning and another programming language. Hence, you have to understand much more and practice real-life data to gain the knowledge needed for this position.
By the way, you can either join this professional certificate alone or you can take a Coursera Plus membership which allows access to more than 3000+ Coursera courses and unlimited access to popular Coursera certifications and specializations. If you want to take more than one course or certificate then you should join Coursera Plus.
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