Coursera IBM Data Science Professional Certificate Review

Learning data science to change or upgrade your career is a definite reason. Before enrolling to course, let’s learn more in-depth about the Coursera IBM Data Science Professional Certificate review.

Coursera and IBM have partnered to create the best online course for data science, IBM data science course on Coursera. This IBM Data Science Professional Certificate consists of 9 courses. The nine-course in IBM data science is listed below:

1. What is Data Science?
2. Open Source Tools for Data Science
3. Data Science Methodology
4. Python for Data Science and AI
5. Databases and SQL for Data Science
6. Data Analysis with Python
7. Data Visualization with Python
8. Machine Learning with Python
9. Applied Data Science Capstone

This course starts from the very basics of data science concepts and takes you through the completion of the capstone IBM data science project. By completing the nine courses, you will earn nine badges where you can share with your employer and showcase it on Linkedin profile.

Before we get into the IBM data science certificate review, let’s see some of the frequently asked questions on data science.

FAQ on IBM Data Science Course Review

These faqs provide you some insight on the course overview and if it’s worth getting IBM data science professional certificate. 

Now that we learned the faqs on the IBM data science course, let’s review and look at the nine courses offered by the professional certificate course.

9 Course in Coursera IBM Data Science Professional Certificate Review

Let’s explore each of the nine individual courses and what it offers, how it helps students learn effectively and efficiently. By reviewing the course contents, you’ll get an idea of what you’re getting into before you get into it.

1. What is Data Science?

Did you know that tapping on the insights of data and trends in data has been around since ancient times? Obviously, this was after the invention of writing and recording. Using data and analyzing it, ancient Egyptians were using to conduct a census to collect the tax efficiently. 

They also used it to predict before the flood occurred from the river Nile accurately. It is since then that we are using math and science to predict things. In modern times in the time where computer exists in our palm, this become data science. 

In this first part of the course, you’ll go over basic applications of data science, like linear regressions, data mining, and real-world uses. You’ll also hear from the working professionals and students letting you know their experience in data science.

An introduction to IBM Watson will provide more information on the tool, one of the first tools provided to you in the course of data science.

2. Open Source Tools for Data Science

You’ll be in trouble if you don’t know which tools to use to learn data science. Since you’re beginning your journey in the IBM Data Science course, you should be aware of some of the popular tools for data science.

This particular course is where you will learn about them and their features. You’ll learn about IBM Watson, RStudio IDE, Apache Zeppelin, and Jupyter Notebooks. 

You’ll gain insight into the use case of all the tools, which programming language they are compatible with to execute, their features, and limitations. 

All the tools are hosted in the cloud and using Cognitive Class Labs; you can test each tool. You are to follow the specific instructions to run simple code in R, Python, or Scala. 

By the end of the second course, you should create a final project using a Jupyter Notebook on IBM Data Science Experience. You should also demonstrate on preparing a notebook, writing Markdown, and sharing your work with your peers. 

3. Data Science Methodology

The third course is designed to serve only one purpose, and that is sharing the methodology to used within data science. By following this methodology, you can make sure that the data used here is relevant to solve a problem and has been accurately managed to address the question.

This course will help you think like a programmer. The model used here is iterative and recursive. Every step used here will prompt you to think back on the previous actions and helps you observe the points that can be improved. 

Since data scientist is one of the hardest skill to achieve, taking this course will ease the burden of learning because the course is designed by IBM to help students from every phase. You will become a reliable analyst by the end of the third course. 

4. Python for Data Science and AI

The fourth part of the IBM data science course will kick off by introducing the Python programming language. In general, you’ll learn Python for data science and Python programming.

Since this course doesn’t have any prerequisites to learn the IBM data science course, Python programming language is taught to you from the basics. It will lead you from zero to hero in Python programming language.

You’ll learn the popular Python modules for data science, such as NumPy and Pandas. Even though this course doesn’t need you to know Python beforehand, we highly recommend that you learn Python programming and write some sample codes for practice. 

The reason why we are insisting you to learn Python before is because you don’t get discouraged when you don’t understand something while going through the course video. By writing your code, you comprehend more about the language, and it stays forever in your memory.

Also, this course is included with the first real project of the IBM data science course. Using Watson Studio, learners should analyze a set of economic data. Coursera has a unique grading model, except multiple-choice quiz your peer grade all assignments.

5. Databases and SQL for Data Science

Enormous data of the world lives in the databases. SQL is the Structured Query Language used to communicate and extract data from databases. The knowledge of databases and SQL is essential to become a successful data scientist.

Hence the fifth course is designed to introduce you to the database world. You’ll learn more about the relational database concepts and the basics of SQL language. You’ll get exposed to both practical learning and hands-on in this course.

When we said hands-on, yes, you will work using the real data science tools, with real databases, and real-world datasets. You create a database instance in the cloud and practice building and running SQL queries. 

Using the gained knowledge from earlier courses, you will use Python and SQL to access databases from Jupyter notebooks. Note to remember is that we’ve already mentioned that there is no prerequisite for taking the IBM Data Science Professional Certificate course. 

6. Data Analysis with Python

In the sixth course, you’ll use Python language to analyze data. The sixth course will take off from basic Python to many different types of data. 

Some of the essential topics covered here are importing datasets, cleaning the data, data frame manipulation, summarising the data, building a machine learning regression model, and building data pipelines.

You’ll gain further knowledge on more Python libraries such as Pandas, NumPy, SciPy, and Scikit. This course covers a range of data analysis techniques.

You’ll work with the sample dataset using SciPy libraries. Next, using the Panda library, we load, manipulate, analyze, and visualize datasets. Then using the Scikit learn, another open-source library, we use its machine learning algorithm and build smart models to make predictions.

There are no peer-graded projects and include only the multiple-choice quizzes. The skills learned here will be used in future courses and in the final project. 

7. Data Visualization with Python

The seventh course in the IBM data science course is Data visualization with Python. It introduces various data visualization methods such as bar charts, line graphs, pie charts, and specialized visualizations like Waffle and Folium. 

Due to the fact that the data is vital in presenting it in a practical way, data scientists use data visualization methods to serve both small and large scale data.

In this course, you’ll learn to use a tool to visualize the data to enable the extraction of information and a better understanding of the data to help make effective decisions by an organization.

Since numbers are way to complicated to understand by many, this course teaches us to use the method of Data Visualization using Python. You’ll be using many data visualization in Python, such as Seaborn, Matplotlib, and Folium.

8. Machine Learning with Python

The eighth course will start lessons from the basics of machine learning using the easiest programming language, Python. Also, this is one of the most challenging parts of the course. Here you’ll be learning two critical elements. 

First is about learning the purpose of machine learning and its applications in the real world. Second, an overview of supplementary topics of machine learning is covered, such as supervised, unsupervised learning, model evaluation, and ML algorithms.

Even though this is one of the difficult parts of the course, it is also a fun part. Because here, you’ll practice the learnings learned from the previous courses using real-life examples of machine learning. 

The final project in the eighth course involves four diverse kinds of machine learning protocols to a data set to conclude which was the best. You’ll understand more about this once you go through the course material by yourself.

9. Applied Data Science Capstone

The ninth and final course in the nine-part course of IBM Data Science Professional Certificate is the capstone project. This capstone project will give you a glimpse of what data scientist feels when working with data. 

You’ll learn more about the location data and different location data providers. One of them being Foursquare. Here there is another learning module that covers the Foursquare API to get information of locations. 

This course prepares you in situations where data is not easily available by parsing HTML code and scraping web data. You’ll use Python and its many libraries that you learned here to analyze the data. 

Then in the final step, you are required to use the Folium library to communicate your results and findings.

By the end of this course, you would’ve learned quite a skill set that adds great value to your current portfolio and prepares you with a ton of job-ready skills. Also, consider taking Coursera Data Science Projects for freshers or beginners.

Time to Complete IBM Data Science Course 

Since Coursera charges you every month, it would be better to complete the Coursera IBM Data Science professional certificate course in less time. If you are already familiar with some of the concepts of data science, then you may complete it in 30-45 days.

If you’re a complete beginner to the data science, then according to the Coursera, you may complete the course in 2 months if you follow the schedule. 

But if you’re looking forward to completing the course in one month, then we recommend that you learn some of the prerequisites before enrolling in the IBM Data Science professional certificate course.

The two prerequisites of data science is learning one of the programming language and a few math topics related to data science.

5 Tips to Learn Data Science Effectively 

Here I’ve compiled some of the best tips to learn the IBM data science course effectively.

1. Stop procrastinating, practice discipline, and study every day. Remember it’s alright if life gets in a way and you couldn’t study on that day. 
2. Don’t just write the code side by side, watching the videos from Coursera. Go through the code thoroughly and understand every line of it. It would be hard for some people and easy for some, but if you invest time to learn it now, it would be easier in the future. 
3. Dedicate 30 minutes of your time in day to explore data science in Reddit, Quora, Medium, Youtube, etc. Learn about the tips and tricks, hacks, and breakthroughs in data science. This will keep you up to speed on the latest trends in data science. 
4. Keep yourself motivated, and don’t be discouraged if you come across a hard part of the course.
5. Follow the Pomodoro technique to study data science. This technique is used to study 25 minutes straight, followed by a 5-minute short break. Follow this technique to stay focused.

Summary

The purpose of the Coursera IBM Data Science Professional Certificate Review is to help you understand what you will be learning and how it’s going to help you to study the course and earn the professional certificate successfully.

If you’ve decided to enroll in this course, you earn both the Coursera course certificate and IBM digital badge after the successful completion of the course. 

Displaying both the certificate and badge with your employer or on Linkedin is going to be a personal achievement. A mode of self-satisfaction where the hard work finally paid off. 

If you loved this IBM data science certificate review, please share it with your friends and colleagues or anyone who might be interested in taking up data science. Also, if you’ve any questions, please feel free to leave a comment, and I’ll get back to you.