The New Role: Complete Career Change

I am going to start writing more frequently, so I can hopefully encourage others to do the same in their journey to learn more, and also to hopefully get feedback about how to improve my posts or personal knowledge base. I am always open to learning something new.

So why wait till now to start writing? Well, I started my new role, and I simply love it, however, I haven’t been writing much because of how content I am with the team and the things I get to work on and learn. 

Photo by Immo Wegmann on Unsplash

Hold on pause! BACK IT UP!

Okay, here is some context. I was working in the Sales department with a great team of people, however, the work was unrelated to what I wanted to do long term, which is, to work with databases, and eventually, work as a Data Scientist!

Photo by Myriam Jessier on Unsplash

So far things have been going great in my new role, but getting to where I am now was not the easiest thing for me to do. I had to put together a plan because attending classes is not enough if you are going through a complete career change like I was. Let me give you the rundown of the different roles I have held over the years.

career_journey <- c('Paraprofessional at Roberts Academy','English Teacher in Japan','Long Term English Substitute Teacher for Finneytown Highschool','Kroger IT Support Analyst','Sales Underwriting Assistant','Business Intelligence Developer')
summary(career_journey)
career_journey <- data.frame(career_journey)
The Various Jobs I have held on my Arbitrary Path Towards My Career
career_journey
Paraprofessional at Roberts Academy
English Teacher in Japan
Long Term English Substitute Teacher for Finneytown Highschool
Kroger IT Support Analyst
Sales Underwriting Assistant
Business Intelligence Developer

I graduated from school wanting to be a teacher helping students learn the English language through literature, well, that was fun while it lasted, but proved to be not the right career move for me. I was scared of moving into something related to computers, however, when I jumped in and realized how fun and rewarding working with data can be, I felt like I had found my mate for life…data. Hence the blog name and image…

Maybe you find yourself in a similar situation, where your current career goals…

1. seem to not be working out the way you thought

2. have become a lie you tell yourself to get through the day

3. you are finding that the thing you loved the most about your career is getting replaced or overrun by something else (this is the category I fell into).

Don’t give in! Explore your options and never doubt yourself! Your brain is a powerful tool that can be reconfigured to whatever you set your mind to. As cliché as that sounds, it’s true. The only limit is what you put into action. For me, this was a Data Analytics Program, a ton of self-study (SQL phone apps, Quizlet for vocab, R phone apps, and practice on my PC), and a determination to succeed.

If you are thinking about switching careers you probably have a long laundry list of things you think will be holding you back. One of mine was having my first kid! Can you believe that? I actually thought an incredible innocent little creature was going to “hold me back,” but guess what, she became another motivating factor for me to work harder if anything else, because now my daughter was apart of my journey, and I couldn’t let myself, my wife, and my daughter down!!! There were quite a number of mornings at 5 am where my daughter would only fall asleep in the carrier which I would strap on and start rocking her to sleep while also typing SQL queries, and SAS or R code on my computer for my class projects (Rest assured, if I were to put her into her crib, she would be back up in a heartbeat 😉 , so the carrier was the best option for her and myself).

So what if I am just not as amazing as you? This question assumes that I am some degree of “amazing” by myself, but the truth is the most important ingredient to your success is the team of people you have behind you whole love and support you.

I hope you found this piece encouraging! I want to help those who wish to move into a different career but feel trapped. You can do it! It takes a lot of hard work, but you can do it!

I am going to more regularly blog tips and tricks I have picked up that have proven to be helpful for me. I also want to post some different projects I complete.

Thank you for reading!

“Stop Acting Like a Baby”

Side note on the title: The title is inspired by the various dad moments I’ve had in the last few weeks where I literally caught myself saying those exact words to my daughter who is going to be 10 months soon; shortly after saying them, I laughed, and my daughter kept innocently staring at me.

The dataset I used I originally found through Data School1. which lead me to the Advanced High School Statistics book on OpenIntro’s2. website. The data set source derived from their site but the dataset originated from “Season of birth and onset of locomotion” by J. B. Benson3.

Here is a briefing on the findings of the study…

What did I find?

The Dataset…

This snapshot was taken from R. This dataset is the same as the csv file except the columns Ctemp (Average Temperature in Celsius) and avg_crawling_age_months (Average Crawling Age in Months). You can see the code for adding these columns along with all of my other code here: https://github.com/sterlingn/babycrawl_data/tree/292ddeef79dc76c3d1047dbe37ff3c57bea78f33.

According to the summary of the dataset, found on https://www.sciencedirect.com/science/article/abs/pii/0163638393800298, there were 425 infants in the study, however, our dataset shows that there are only 414 infants. Looking at the correlation between temperature and babies crawling, our r=-.70, see the graphic below:

And so, there is a negative correlation, but this doesn’t imply causation. Given that we only have one variable to work with, it is harder to know exactly what causes babies to start crawling later than other babies, so how good of a predictor is the temperature for the crawling age? Well, let’s take a look in R.

After running the summary statistic on the simple linear model that was created, the results show that the temperature, as a coefficient, has a P value significantly less than the 0.05 threshold, which results in the rejection of the null hypothesis that there is no correlation between the two coefficients, temperature and average crawling age, however the adjusted R-squared value being at .4386 out of 1 shows that this model is not the best. There appears to be other variables that are influencing the average baby crawling age, however, temperature does seem to be an influencer. Another downfall to using the dataset I have is that the temperature and the crawling data are both averages taken from the actual data which can seriously cause issues when trying to analyze the data, because the data is already a summary of the actual data.

References:

  1. https://www.dataschool.io/resources/
  2. https://www.openintro.org/stat/textbook.php
  3. J.B. Benson. Season of birth and onset of locomotion: Theoretical and methodological implications. In: Infant behavior and development 16.1 (1993), pp. 69-81. issn: 0163-6383.
  4. https://www.sciencedirect.com/science/article/abs/pii/0163638393800298