REVIEW COURSERA CERTIFICATE - IBM Data Analyst Professional Certificate

This is gonna be my first post in the series about Learning for my Career, which is still fairly unclear about the path I will choose. But for now, I decided to learn and become a Data Engineer. My goal is to write more about the Side Projects that I will take, and what I have learned so far, and review courses for other learners.

Overview of the Certificate

Here is the link to the certificate:

IBM Certificate

If you're new to the data field or have limited knowledge of computer science, the IBM Data Analyst Professional Certificate is an excellent starting point. The content is beginner-friendly, consisting of bite-sized videos and engaging hands-on labs that I personally found super helpful.

This certificate comprises 9 courses, and the best part is you have the flexibility to choose the ones that resonate with you. At the end of each course, there are plenty of recommendations, allowing you to tailor your learning journey.

The overview page highlights key skills you'll acquire, with a special shout-out to the well-explained Pivot Tables, backed up by good examples. While I'm only halfway through the certificate, I won't delve into the entire course just yet. Instead, I'll share my insights on my first Python project and the GitHub repositories, giving you a sneak peek into the practical side of things.

My recommendation/ rating: 4.8/5

What do you think about this rating?

• Totally Agree
• Higher than that
• Maybe a bit lower?
• Nah

The course Python Project for Data Science

Embarking on my first Python project in this course was a genuinely satisfying experience. Despite having instructions on the side, the thrill of pulling data directly from a website added an extra layer of coolness to the process.

What sets this course apart is its hands-on approach to real-world applications. Not only do they guide you through your first project, but they also introduce you to the world of GitHub. Opening my GitHub account and uploading that inaugural project made me feel like I was stepping into a space I'll become very familiar with in the future. The concept of a repository, which seemed a bit distant before, started making more sense through the practical exercises in this course.

The hands-on lab exercises are remarkably user-friendly. With just a bit of revision, you can confidently tackle the assignments on your own. A special shout-out to the feedback loop in this course – it proved invaluable. Getting insights on my assignment was enlightening; it helped me spot areas where my answers differed from the benchmark, providing clarity on the nuances of the task. Much appreciation for the constructive feedback that enhances the learning experience.

Thank you, Sarah, for the wholesome feedback!

Also here is the link to my Github:

GitHub Analyzing Historical Stock Revenue Data and Building a Dashboard

Unveiling Stock Data Extraction and Web Scraping

In this course, we get hands-on with two essential elements of data science: extracting stock data and mastering webscraping.

For stock data, we utilise yfinance or Yahoo Finance, a reliable source breaking down the process for even beginners.

Beyond that, we delve into webscraping, learning how to pull data from websites using BeautifulSoup. The course also guides us through cleaning the data, and removing characters like &$* that can complicate our analysis.

To cap it off, we explore data visualization with the make_graph function, turning raw data into clear, insightful graphs.

Note for myself:

As a personal note, this post doubles as my review and a quick-access reference for the course. Thanks for joining me on this learning journey, and best of luck with your studies! :")