I recently finished the Google Data Analytics Course series (via Coursera) provided by Merit America a week before this writing. I highly recommend this course if you ever want to get into the general foundations of the data world, if you wish to become a data analyst, data engineer, or, most of all, a data scientist. If you have a Coursera Plus subscription, you can take this (and other Google-provided courses) for free. But better yet, if you want full support of your career building and job searching, I recommend enrolling through Merit America. This is my second boot camp-style program I enrolled. Because of MA's support system (job placement assistance from the beginning until reaching the job success phase), I truly felt the support, encouragement, and motivation to go through the program until I earned my certificate.

However, my journey of data-world learning continues. The certificate proves I have completed and learned the foundations of data analytics (even data science if I ever thought of pursuing that higher-level (?) route). But as someone who is extraordinarily detail-oriented, I needed more time to hone my natural skills as well as the skills I learned from the program. I didn't complete my (optional) requirements to get into MA's job success phase --- that being the capstone project. You see, my current workplace happened.

After the foundations, it's time to go deeper

At the same time, I also wanted to focus more on going in-depth with the foundational concepts introduced by the program. For instance, getting a more solid ground on SQL and R and returning to another programming language I learned ages ago: Python. I also plan on taking an Intro to Statistics and Probability course to solidify my math skills. Math has always been my weakest subject, but back in college, I enjoyed taking statistics and probability courses.

In short, I want to feel completely ready, skills-wise. At the same time, I also want to feel ready on a job success level too. Job interviews and communication, in general, is what I would like to prepare even further. I also need to improve at creating demo videos for each project added to my portfolio. I'm also working on redoing the layout and visual of my portfolio into something more organized and not cluttered into one page like the one I have right now. The main reason is that I want to make room to add more projects in my Works section (including my non-data analytics-related projects and case studies). I also thought about opening a blog section within the new portfolio layout to write case studies alone. Still, maybe it's better to post them here or create notebooks (R Notebooks or Jupyter Notebooks) for them. I'll have to see in the future.

I decided to take more courses and apply for jobs at my own pace because, as a data analyst and a general observer, I noticed apparent patterns within the job descriptions of analyst job openings through LinkedIn and found some common denominators. I'm not talking about the available years of experience or the usual "must have a Bachelor's or Master's Degree in a quantitative field" types. I'm speaking about the skill-based requirements they're looking for:

• Must have an advanced level or experience with SQL: we have learned essential SQL using BigQuery, but the ones often mentioned in these job descriptions include MySQL/MariaDB, Oracle, PostgreSQL, etc. What are the differences with these SQL platforms and BigQuery?

• Must have an advanced level or experience with a programming language such as Python, R, Java, etc.: We learned basic R in the Google Data Analytics course, but Python is the most popular language of choice in the data field, and it seems to be the preferred choice for a lot of companies, big and small.

• Must have an advanced level of experience with data visualization tools such as Tableau, Power BI, etc.: From the job searching that I went through during the cohort on LinkedIn, Glassdoor, and other job search sites such as Otta, I see Tableau and Power BI pop up a lot in the job descriptions. For job openings that require Python or R, candidates are also expected to know the data visualization packages for those languages too. Even a web development backend programming language such as JavaScript has its own JS-based data visualization library too called D3.js, even though JavaScript isn't programmed as a data analysis language unlike Python and R. I've read that you can actually earn specialization certifications in Tableau and Power BI, so I thought about attempting to earn a specialization for either one or both. The main issue this time is that their exam fees are costly (around $300 for a Tableau exam, $165 for a Power BI Exam). You don't need a specialization to apply for an entry-level data analyst job, but it does help you stand out among all the other candidates.

• Must have experience in communication and presentation skills: One of the things that I personally had trouble with during our cohort was that we had to go through a series of interviewing practices through BigInterview and create demo videos for my select projects. I am one of those shy and introverted people who are naturally terrified of speaking in front of a huge group of people, and my lack of confidence of public speaking gave me an obstacle. I understand the importance of open communication among data analysts, which is why I want to practice more so I can build up my confidence, not just with job search-related or work-related, but overall as an individual, too.

I need more than just learning the foundations and jumping to the next level. Others are ready to take the big leap, and I admire them for stepping up. I'm not at that level just yet.

What am I doing now?

I still feel fresh from earning my certificate, and because work had become hectic because of the holidays, here are my plans so far:

• Going back to Canvas and doing the Optional Technical Assignments: Many bootcamps utilize Canvas for their course modules, which includes MA. I'm going back there and complete the optional technical assignments and find my favorite activities to be added to my portfolio.

• Recently Enrolled in the Data Science: Foundations using R specialization course series on Coursera provided by the Johns Hopkins University Bloomberg School of Public Health. The series' first course, The Data Scientist's Toolbox, is one of the pre-cohort prerequisite courses provided by MA that some students were required to take before the Data Analyst cohort began. I wasn't required to take the prerequisite courses after I got my acceptance email from MA, but I requested for them to give me access so that I can take them while waiting for the cohort to begin. I didn't get to finish the class when the cohort began by just one more lesson. After getting my certificate, I returned to this course and finished that one lesson. I also earned a certificate for this course alone too. I discovered that this course is a part of the specialization course mentioned, so I decided to enroll in the whole series (using my Coursera Plus subscription). It's only five courses, focused explicitly on R, but the steps are already familiar to us as we learned them from the cohort. I already started with the second course of the series, R Programming.

• Still utilizing Huntr and add more job openings on my wish list. I haven't applied to any of them yet, but I'm using their job descriptions as references for other subjects I should learn and improve on.

As 2023 is about to end soon, I look forward to what 2024 would be like for me and my data analytics career. I hope I can accomplish more and finally feel ready to take on the next step.

Here's to the future data analyst me!