Time flies, a full month has elapsed since my last exploration of Machine Learning which also represents a month of dedicated focus on SQL and Data. Let's now take a moment to evaluate this pivotal month.
Accomplishments:
Intermediate Understanding of SQL
Fundamentals of Relational Database and Microsoft SQL server
Fundamentals of Tabluau and Google Big Query
Fundamentals of the "Art" of Visualization and Presentation
Courses completions:
| CS50 | Week 9 of 10 |
| Google Data Analytics | Course 7 of 8 |
Upcoming Courses & Certifications:
In a prior blog, I designated a six-month period (March 25th to September 25th) as the "Infrastructure Stage" to solidify my foundational knowledge in Machine Learning. However, as I discovered that entry-level ML roles are nonexistent, I redirected my attention towards Data, the core of Machine Learning. Although I plan to maintain the original timeframe as a progress marker, I now anticipate accomplishing the following by the end of 2023:
SQL - Exam DP-300 - Microsoft
Microsoft Certified Data Analyst Associate - Logikbot
Google Advanced Data Analytics - Coursera
Tableau Certified Data Analyst - Tableau
Power BI - Exam PL-300 - Microsoft
AWS or GCP certification (either one or both if I have the headspace and time)
If I can adhere to my set standard, upon the end of the Infrastructure Stage, I should have at least secured the SQL certification along with a certification in one of the visualization tools. These qualifications will position me as a viable candidate for an entry-level data analyst role.
Things I need to learn in SQL:
Before I can confidently tackle the exam, I believe I need to brush up on a lot of the concepts in relation to database management and practice some advanced concepts such as:
Stored procedures
Triggers
Transaction control
Error handling
Creating and Managing indexes
Optimizing query performance
What about My ML Project?
In my latest blog post (week 13), I discussed my ongoing efforts on the Personal Assistant component of my ambitious project. At this point, version 3.0 is still under development. Moreover, my vision for the final product has evolved to something not just an impressive resume feature, but also a practical tool. Going forward, I face two significant challenges:
I need to rewrite the roadmap to reflect the new goal
I'm overloaded with all the study
Solutions:
Expect the new road map in the next monthly update
Time Schedule for project
Workdays: 30 minutes
Dayoffs: 1 hour
That's approximately 18 to 20 hours in a month, falling short of my initial target of 20% (around 28 hours spent on ML projects) of the Infrastructure stage. However, as my study priority has been shifted to match reality, I believe maintaining a daily commitment to ML as a side interest could arguably propel me further and faster.