Completed the 60-day mark in my #66DaysOfData & #100DaysOfCode challenge

It’s been 60 days since I embarked on a journey to learn data analysis. I’ve always been fascinated by the power of data to drive decision-making and improve business outcomes, but if I am being completely honest I was a bit hesitant when I started, despite that, at the end of 2022, I decided to take the plunge and dive into learning more about this field.

💼 ABOUT GOOGLE DATA ANALYST PROFESSIONAL CERTIFICATION:

I started off by completing the Google Professional Data Analyst certification. This was a great introduction to the field and provided me with a solid foundation in data analysis concepts and tools. The certification covers a wide range of topics including data wrangling, visualization, and statistical analysis. I would recommend anyone interested in learning data analysis, to start with the Google Professional Data Analyst certification, it’s a great starting point that will give you a solid foundation in the field. I found it to be a great way to gain a broad understanding of the field and to get a sense of the different tools and techniques that are commonly used in data analysis. However, there’s a caveat. The course doesn’t dive too deep into SQL and R and data exploration in general. It’s just an introduction to the field, and if you want to take it to the next level, you’ll have to look for more advanced courses.

Link to the Google Data Analytics Professional Certification: https://www.coursera.org/professional-certificates/google-data-analytics

🔗ABOUT DataCamp SQL CAREER TRACK CERTIFICATION:

Hence, I moved on to the DataCamp SQL Career Track certification. The DataCamp SQL Career Track certification was an add-on to my previous lack of SQL knowledge and helped me to understand the basics of writing SQL queries, statistics involved in data analysis, intermediate SQL, and the use of different types of JOINs, set theory, nested & subqueries, and data manipulation. Additionally, it covered PostgreSQL summary stats and window functions, exploratory data analysis and data-driven decision-making. Eleven courses make up the career track as a whole and offer a thorough introduction to SQL. But the course had a limitation, though it dived fairly deep into SQL, I found there were some inconsistencies throughout the eleven courses and maybe it was because all those eleven courses are actually taught by completely different instructors.

Link to the SQL career track: https://www.datacamp.com/onboarding/create_account?track_id=6942

📊ABOUT THE DataCamp R CAREER TRACK CERTIFICATION:

The DataCamp R Career Track certification was another important step in my learning journey. Though I have completed just four courses in the entire track, I learned about the intertwined processes of data manipulation and visualization using tools such as dplyr and ggplot2. I also gained experience in transforming and aggregating data using R-dplyr. The R language is a powerful tool for data manipulation and visualization, but I’ve decided to pause my R Language Career Track for a couple of reasons. Firstly, I wanted to focus on completing the SQL Career Track and quickly do a bunch of projects. Secondly, now that I am a bit more experienced, I have realized that most job descriptions mention python and python-related libraries, so I will probably focus on that in the future.

Link to the DataCamp Data Analyst in R career track: https://www.datacamp.com/onboarding/create_account?track_id=5

🔖TWITTER CHALLENGES THAT I FOLLOWED AND PARTICIPATED IN:

Throughout this 60-day journey, I also participated in the following challenges on Twitter: #30DaysOfCode, #66DaysOfData, and #100DaysOfCode. The reason why I mentioned 100DaysOfCode is because I wanted to continue to learn even after my 66DaysOfData challenge, but I’ll see how things unfold. The challenges were a great opportunity to stay motivated and to share my progress with others. It was also a great way to connect with other people who are also learning data analysis and to learn from their experiences.

📒 WHY DOCUMENTING DATA ANALYSIS LEARNING JOURNEY COULD BE HELPFUL:

Deciding to document my journey of learning data analysis has been incredibly beneficial for me. It has helped me in several ways:

1. It has helped me to keep track of my progress. By keeping a log of the certifications and courses I’ve completed, I can easily see how far I’ve come and what I’ve accomplished in the past 60 days. This has been incredibly motivating and has helped me to stay on track.
2. It has helped me to articulate and summarize what I’ve learned. By writing about my experiences and the knowledge I’ve gained, I’ve been able to better understand and process the information. This has made it easier for me to remember what I’ve learned and to apply it to real-world problems.
3. It has helped me to reflect on my learning process. By reflecting on my journey, I’ve been able to identify areas where I struggled and areas where I excelled. This has helped me to understand my learning style better and to make adjustments to my study habits to improve my learning process.
4. Finally, documenting my journey has helped me to share my experience with others who may be interested in learning data analysis. By sharing my journey, I’ve been able to connect with other people who are also learning, and learn from their experiences.

In conclusion, I highly advise others to journal their own learning process because it may be a useful tool for learning, making connections with like-minded individuals, and progressing in their particular industries.

🎯FINAL CONCLUSION:

Overall, I’ve had a terrific learning experience thus far with data analysis. I’ve accumulated a significant amount of knowledge and practical experience working with data from the actual world. I have much more faith now in my capacity to interpret data and draw conclusions from it, and I’m eager to learn more and advance in this domain.