New to Tech — Imposter Syndrome? Just Quit.

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We live in a time of unequaled opportunity. One of such opportunities opened up and I got accepted into the Dataquest and AI Inclusive Scholarship Program…yay!!! community

Coming to an entirely new field of study, especially through the rigorous path of self-study brings with it its share of feeling inadequate. This feeling of inadequacy builds up restraint and makes the newbie hold back despite evident success.

Being one who took practical steps through self-study in an entirely new field, learning data from scratch, learning how to code and visualize data, I will share a few tips that have helped me and will surely be invaluable to others — who experience imposter syndrome. Let’s call these 4 steps to quit feeling like an imposter.

1. Reading Documentation: Take a moment to look at the documentation. Programming language documentation or reading through the documentation of a new dataset holds within it salient keys that will help make analysis much easier. A dataset could have within it “daily” entries” and “annual entries” — this little error could completely skew the outcome of the analysis. I recall my very first personal project, reviewing the rideshare dataset of a bike-sharing company — Google Capstone Project, to advise the Marketing team on how to turn non-paying customers into paying customers based on their bike-share data. I struggled to convert the date from the str in datetime format to something I could work with. My data buddy, Kolapo gave helpful tips and of course, guided me to R Documentation. Lastly, on documentation, I find that studying presentations of analysis results from companies and institutions utilizing data has been helpful. Experienced programmers, analysts, and presenters approach the business problem from different perspectives and churn out smart results to improve the overall business objective.

2. Practice coding and review codes: I find github to be one infinite well of coding knowledge. Millions of programmers and institutions maintain an active repository of projects that one cannot catch up. After completing any certification, we are encouraged to practice programming in R or Python or any other programming language on our own to supplement what was learned during the courses. 2 resources that have been of immense value to me are Python for Data Analysis by Wes McKinney and Data Visualization and Exploration with R by Eric Pimpler.

3. March over to Tableau Public to appreciate the art of visualization from expert and newbie users of Tableau. One way to improve in art is by appreciating art. Visualizing the output of the analysis is an integral part of the data analysis workflow process. Using data to tell captivating and compelling stories makes the presentation of analysis a whole lot easier — especially with the dynamic dashboards that Tableau offers. Furthermore, recall there was an entire course on messy and good data presentation examples in the Google Data Analysis Professional Certification. Appreciating the work of other data enthusiasts on Tableau show you how to, and how not to present your data.

4. Lastly, offer to help someone coming along the path of data science — debug a code challenge, explain a complex concept simpler, or just be a data evangelist.

Overall, sharpen your skills, get involved and you are on a solid path to leveling up your data skills? There are several beginners and intermediate paths with little or no hassle to lead you there. The Google Data Analyst Professional Certificate will take you from a total newbie to intermediate concepts in programming and visualization for data analysis.

If data is a goal for you, then head on to This platform opens you to an unending pool of resources, full access to all the Dataquest contents to keep learning and refreshing your skills.

In summary, developing as a programmer and analyst means being exposed to more tools that you can use in data analysis. These tools can come in the form of mastered programming concepts that may perform a specific task. As you master more tools, you will be able to tackle more analysis tasks and be more flexible as a programmer. A good analyst knows the basic functions needed to solve a problem, but a great one can come up with multiple ways to approach it — Dataquest.

The world and everything in it are for exploration, study, and joy.

Connect with me on LinkedIn to see personal analysis and coding projects I’ve worked on.

Unending passion in Data Analytics