An Industrial Engineer turned Data Scientist who is interested in all things technology! Find me on LinkedIn:

You can say goodbye to coding for data wrangling and data cleaning

I started learning Alteryx for my position as a data analyst about 6 months ago, and I love it. I can accomplish most of I did in Python in school, but with less headaches and I can train others without Python experience to use it as well.

Whether you are a data scientist, a data analyst, or have a role where you rely heavily on Excel, there are lots of reasons to try out using Alteryx. It is a very powerful ETL and analytics tool which can greatly simplify the data wrangling, data cleaning, and even modeling processes.

So, here…

Here’s my story of transitioning from Industrial Engineering to Data Analytics & Data Science

First of all, I would like to say that I am humbled by the response that my articles on Towards Data Science have received. I owe a huge thank you to the people who have followed me, consistently read my articles, and gave me feedback — you all are awesome.

I would also like to note one caveat before I jump into my story: the position I recently accepted is titled “Global Solutions Analyst,” and it is really a mix of data analytics, some data engineering, data science, and some industrial engineering.

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Hello, I’m Megan! (Photo by Adam Solomon on Unsplash)

For those of you who do not know…

Here’s what I learned along the way

Thanks to a group of dedicated data science boot camp grads, Zoom, and the World Wide Web, I have listened to and answered too many data science interview practice questions to count. These past months of practice have taught me a lot, not only about data science, but also about self-confidence, hard work, and the power of building relationships via networking.

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Photo by Green Chameleon on Unsplash

I’ll start with the data science stuff because this is a data science blog and that’s what you all are likely here for.

I’ll take “What is Data Science?” for 200, please.

While going through a data science bootcamp, I felt like I was grasping most of the…

Why 80/20 is the golden rule (of data analysis and time management)

I’ll start this article with a story.

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photo by Andreas Dress on Unsplash

I interned at Tesla last summer in the Production Optimization Department. As an aspiring industrial engineer, I was tasked to “go out and improve things.” Easier said than done.

One area of the Gigafactory that I worked in housed a giant machine that shuttled batteries through a buffer and then to the next step in the battery pack manufacturing process. There were a lot of problems with this complex machinery, resulting in red flashing lights and production stoppages.

After several meetings with engineers and querying some data, I learned the true value of…

Using engineering to organize the chaos and improve your efficiency

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Photo by Valeriy_G.

Did you know that the movie “Cheaper by the Dozen” was inspired by a real family named the Gilbreth’s?

The parents, Frank and Lillian Gilbreth, were both industrial engineers by trade in the 1920s. In fact, Frank Gilbreth was considered to be the “Father of Management Engineering” ( They used their research findings to not only improve life for workers in factories but also to manage a household of 12 children. In short, as a passionate industrial engineering student the past four years, they were my idols.

Explanations to effectively communicate technical data science work to a non-technical audience & reduce misunderstanding

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From Amy Hirschi on Unsplash

I have heard stories from managers about data scientists who are brilliant in their technical prowess but lack some essential business communication skills. I have also heard data scientists complain about how upper management does not understand the difference between some technical terms and will throw in buzzwords, like “big data”, without understanding their meaning.

The purpose of this article is to help out both sides here! Data scientists, you can read this and get help with explaining jargon in non-technical terms. …


Lessons I have learned after 4 months of my data science curriculum

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I quit my first job, in technical consulting at an accounting firm, after a bit of a personal crisis. I moved home and was desperately trying to figure out my next move. Knowing I have typically enjoyed school and work projects that utilize coding and data analysis, I considered the possibility of applying to data analyst and data scientist positions.

Reading those job descriptions made me realize that I did not have all the skills I needed to feel confident and competent in a data science role, even though I do have a degree in Industrial Engineering. My father suggested…

Machine Learning

The basics of how artificial neural networks are loosely modeled after your biology

You may be thinking: Neural networks! Those sound so complex, and while I know they are popular, I am not sure I will be able to understand them without a deep scientific or technical background…

Keep on reading — this article is for you. And anyone else, for that matter.

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Photo by Bret Kavanaugh on Unsplash

Some Biology Concepts

In your brain, you have neurons, which handle information. They take up new information, process it, and then transmit signals — electrical and chemical ones. (If you have a background in biology, I apologize for the oversimplifications that are happening here.)

Neurons are connected to each other by axons. This…

Explaining How ML Models Work as Simply as I Can

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If you are new to data science, this title is not intended to insult you. It is my second post on the theme of a popular interview question that goes something like: “explain [insert technical topic] to me as though I were a five-year-old.”

Turns out, hitting the five-year-old comprehension level is pretty tough. So, while this article may not be perfectly clear to a kindergartener, it should be clear to someone with little to no background in data science (and if it isn’t by the end, please let me know in the comments).

I will start out by explaining…

Important questions to ask about your data during exploratory data analysis

Creating machine learning models is cool. It’s tempting as a beginner (I know from experience) to jump straight to the cool part — after all, it’s the most important part too right?

What if you skipped straight to the climax of a movie that you’ve never seen before? Would you be confused? Would it even be enjoyable?

Just as the first hour of character development is foundational in a movie, exploratory data analysis (EDA) is a crucial first step towards a good data science project.

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Photo by True Agency on Unsplash

It’s time to grab your favorite blanket, snacks, and sweats and cozy up with your…

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