The Data Science Evolution: A Tale of Trends, Talent, and Time!🚀

Author(s): Kashish Rastogi

Originally published on Towards AI.

The Data Science Evolution: A Tale of Trends, Talent, and Time!🚀

This analysis isn’t a battle of the sexes it’s a deep dive into how men and women engage with different aspects of the Kaggle Data Science Survey. From tool preferences to career paths job titles to age demographics, we’ll uncover the unique ways they shape multiple data fields.

We’ll also zoom in on key roles like Data Scientists, Data Analysts, Research Scientists, and Machine Learning Engineers to see what skills and trends define their journeys. If you’re a student or aspiring data professional, this might help you decide your next move!

So, grab your favorite dataset (or coffee ☕), and let’s explore the numbers behind the people who power Data Science. Cheers! 🥂

Methodology

I have decided to take a different approach on this dataset:

A first look tells a story of how 💃Ladies and 🎩Gents navigate their careers, personal choices, preferred tools, machine learning frameworks, and recommendations for the future.

Second look is a Role-based comparison: Examining how different roles — Data Scientists, Data Analysts, Research Scientists, and Machine Learning Engineers — respond to various survey questions. This will help uncover patterns in skill preferences, career trajectories, and industry trends.

Analysis

All visualizations are created using Plotly for interactive charts, while Python (Pandas and other essential libraries) is used for data processing and analysis.

Most people are from India followed closely by the U.S. These countries together make up more than 50% of the entire population.

Who Showed Up to the Party? 🎉

The golden ratio remains strong: 20:80 for Women:Men!

No matter how many people filled out the survey each year, the gender split barely budged. We’re looking at a crowd of 80% 🎩 men and 20% 💃 women as if they got the invite in different batches.

Oh, and 2021? That was THE year!

More people answered the survey than ever before due to the increase in data science sources. Either Kaggle is getting more famous, or everyone just has extra time on their hands. 🤔

Does Age Define the Gap? 🎭

Looks like the “tech dream” starts strong in the early 20s, with over 1,000+ men and 100's of women jumping in. But as the years go by, the numbers shrink almost like a Netflix series that lost its hype after Season 3. 📉

Popularity of Data fields among younger talent is finding a way into the platform and taking the survey🏆

It’s great to see the older generation after 50 is also showing quite a interest in the data field.

Who’s Leading the Data Party?

The biggest group at the party? Data Scientists, followed by a strong showing from Software Engineers and Data Analysts.

The most popular title on the guest list is Data Scientist, taking the crown with a solid 24.64% of the responses! 🏆 They’re basically the rockstars of the party — analyzing, modeling, and probably making you wish you could predict the next big trend.

Now, let’s talk about the guest list! Ladies seem to be hitting the dance floor mostly as Data Scientists and Analysts, while the gents are rocking the Data Scientist and Software Engineer gigs.

Fun fact: Data Science is practically the VIP section, with both men and women flocking to it. But here’s a twist — when it comes to Machine Learning and Business Analyst roles, the ladies are similar.

Data Careers: A Young Person’s Game… or Is It?

When it comes to Data Science and Analytics, youth dominates — but experience isn’t backing down either!

🔹 25–29 is the Prime Time: This age group holds the highest number of Data Scientists (889) and Data Analysts (583). Looks like mid-20s is the sweet spot where most professionals step into these roles!

🔹 Early Birds vs. Late Bloomers: The field is seeing young prodigies with over 340 Data Scientists under 21, but also seasoned veterans — some still crunching numbers in their 60s and beyond! (Shoutout to the 11 Data Scientists aged 70+ who are still rocking it! 👏)

🔹 Machine Learning Engineers: A Rare Breed? Unlike their Data Science and Analyst counterparts, Machine Learning Engineers under 21 are almost mythical creatures (just 23!). Is ML a career that requires extra seasoning before diving in?

So, whether you’re an ambitious 18-year-old diving into data or a 50-year-old switching careers — there’s space for everyone in the world of analytics! 🚀💡 From fresh grads to industry veterans, data welcomes all! 🚀

🎓Degrees: The Unwritten Job Requirements?

Ever wondered if a Master’s degree is the golden ticket to a data career? Well, the numbers don’t lie! Across Data Scientists (47.7%), Data Analysts (44.8%), and Machine Learning Engineers (43.6%), a Master’s degree is king. But if you’re a Research Scientist, forget the Master’s — 56.2% went straight for a PhD. 🧑‍🔬

Meanwhile, Bachelor’s degrees dominate for Data Analysts (39.2%) and ML Engineers (33.7%), but if you’re a Data Scientist, they barely get you in the door (30.3%).

💡 Surprise! Some rebels exist — about 6% of Data Analysts and ML Engineers made it without a Bachelor’s degree. So yes, degrees help, but there’s always a way in! 🚀

What’s in Demographics?

Men are outnumbering women everywhere, making it a global “guy’s world”.

India: With 79% male, it’s like a never-ending boys’ night out. The men are definitely in charge here!

USA & UK: At around 78% male, it’s a bit more balanced, but the guys are still taking the lead. Time for some more women’s clubs for data fields?

Russia: 86% male — Russia’s officially a man cave, where the guys are running the show.

Code With Confidence (Or Not?) 💻

Seems like Data Analysts are still figuring out coding, with over 1/4th having less than a year of experience! 🤷‍♂️ Meanwhile, Data Scientists and Machine Learning Engineers are the “freshers” of the tech world, with most of them rocking 1–3 years of coding. But hey, Research Scientists have been around for a while — looks like they’ve been coding since the “good ol’ days.” 🧑‍🔬📚

Programming Language Hunger Games 🎯

Who won? Python and SQL are running the show, while Julia and Swift are just waiting for their glow-up. 🌟

1️⃣ Python is the King 👑 — No surprises here! Data Scientists (3,318) and Data Analysts (1,778) are practically Python cult members. seems like Python is the universal love language of data! 🐍❤️

2️⃣ SQL is the Sidekick 🛠️ — Data Analysts (1,376) and Data Scientists (1,951) use SQL like it’s their daily caffeine fix ☕. But Machine Learning Engineers (482) and Research Scientists (339)? Eh, they just nod at it from a distance. 😅

3️⃣ R is the Cool Nerd 🤓 — Data Scientists (1,111) love R, but Machine Learning Engineers (141)? Not so much they give them cold shoulder. ❄️

All the other languages are just making a dent, these languages are like that indie band only a few hardcore fans swear by. 🎸

The Great Coding Age Gap!

The 1–3 year coders are dominating with 2,959 men and 679 women — looks like everyone’s trying to break into tech… or at least pass a LeetCode easily. 🚀

The 20+ year veterans? Only 1,322 men and 111 women left — guess some retired, or they finally rage-quit debugging that one nightmare project. 💀👨‍💻

“Never coded” crew? 463 men, and 164 women — probably filling out this survey just to watch the chaos. 📊😂

Global Coding Trends: Where the World’s Programmers Are at!

Looks like India is the coding superstar, with a whopping 3381 responses, including a lot of newbies (770 with <1 year of experience).

Meanwhile, the USA brings a nice balance with a good mix of fresh faces and seasoned pros. Russia and Germany are all about the veterans, with a lot of people rocking 10+ years of coding experience.

Brazil and Nigeria are steadily growing, with Brazil showing a balanced spread and Nigeria having a strong presence in the early coding years.

Q. Recommend a Language and Notebook

Aspiring Data Scientists: Where to Start? Master These First!

Python reigns supreme once again, leaving R in the dust across all job roles! No surprises here — students love Python, and with most Kaggle notebooks written in it, plus a sea of learning resources, it’s the undisputed fan favorite. 🐍💙

When it comes to Data Scientists, Python and SQL are the dynamic duo, while Swift barely gets a seat at the table. Meanwhile, Research Scientists have a soft spot for R and MATLAB — something Machine Learning Engineers don’t seem to vibe with. Guess some tools are just an acquired taste! 🤓

The Battle of the Code Editors: Who Wins?

Looks like VS Code is the Beyoncé of coding editors — everyone loves it, and it’s leading the charts across all job titles! 🏆 Jupyter is also holding strong, proving that notebooks aren’t just for middle school.

Meanwhile, Machine Learning Engineers have a strong bond with PyCharm (20.51%), while Research Scientists still haven’t moved on from MATLAB (9.93%) — guess nostalgia is real. 😆

And can we talk about Vim/Emacs? Only Research Scientists seem to use it significantly (6.32%). Maybe they enjoy a challenge? Or maybe they just like suffering. 🤷‍♂️

At the bottom, Sublime Text and Notepad++ are like the forgotten mixtapes — still around, but not really making waves. 🚀

Hosted Notebook Wars: The Battle of the Cloud Titans!🚀

If you’re starting out, stick to Kaggle & Colab! If you’re feeling fancy, Azure, Databricks, or Sagemaker could be worth exploring. 🚀

• Colab & Kaggle Notebooks reign supreme! 🏆 These two are the clear MVPs, with Colab (1,540 Data Scientists) and Kaggle (1,360 Data Scientists) leading the pack. Looks like Google’s playground is where the real data magic happens!
• Machine Learning Engineers love Colab & Kaggle too! With 751 using Colab and 598 on Kaggle, they clearly enjoy free GPUs. Who doesn’t love some extra compute power for free? 💻⚡
• IBM Watson & Azure Notebooks are holding their ground. With 168 Data Scientists using Watson and 250 on Azure, they’re like the old-school pros still getting the job done.
• Amazon Sagemaker & Databricks are favorites for Data Scientists. Their numbers (243 & 213, respectively) show that cloud-based ML pipelines are a thing. Big data, big models, big budgets! 💰

Conclusion

This analysis highlights key trends in data science careers, educational backgrounds, tool preferences, and demographic insights. While gender disparities persist, the field remains accessible to professionals of all ages and backgrounds. Additionally, the demand for Python, SQL, and interactive coding environments continues to shape the industry’s future.

For aspiring data professionals, these insights provide valuable guidance on industry trends, necessary skills, and career planning. The evolution of data science continues, and opportunities remain abundant for those eager to learn and adapt.

All the cleaning and visualization processes are done in Python and Plotly.

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Published via Towards AI