Scott Yockel’s Post

🎓 Bachelor's degree enough for a data scientist? YES, as 36.2% of data scientists hold a bachelor's degree. Here's the full list of educational degrees of data scientists in the US: 36.2% - Bachelor's degree 32.9% - Doctoral degree or more 22.5% - Master's degree 08.4% - Some college #DataScience #CareerJourney #tech #dataanalytics Data source: https://careerservices.fas.harvard.(edu)//labor-market-insights/

Data science is NOT the sexiest job of the 21st century. It's the most overrated. Thousands of people swarming job postings and asking for referrals, all for a job title that often: 👎 Pays less than data engineering and software engineering 👎 Is chalk full of ambiguity and suffers from a severe lack of leadership comprehension/support across industry 👎 Has a comical bias to postgraduate degrees over applied experience, even in highly applied roles (see above bullet on leadership comprehension) So why SHOULD you pursue data science? You should pursue it if you: 👍 Love working with and investigating messy data 👍 Love thinking about business problems and the world from an analytical perspective 👍 Are not only fine with ambiguity and having to wear random hats (like part-time data engineer), but can embrace them Don't get me wrong; there are DS positions where you get to work exclusively on cool ML, have well-defined problems, and get incredible pay. But for most people, that's not going to be their first or even second data science job. Be real about the industry and what makes sense for you as a newbie.

🚀 Navigating Your Data Science Career Path 🧭 Embarking on a career in data science is like setting sail on an exciting voyage. It's a journey filled with opportunities, challenges, and endless learning. 🌊💼 As I take my initial steps as a computer science student with a passion for data science, I'm eager to explore the various career paths within this field. From becoming a data analyst to a machine learning engineer, a data scientist, or even a data-driven decision-maker, the possibilities are diverse and promising. 📈🧠 I'd love to hear from seasoned professionals in the data science community: What advice do you have for someone just starting their data science career? 🙌 What were some pivotal moments or lessons in your journey that you'd like to share with aspiring data scientists? 🛤️ Let's connect, share insights, and help guide the next generation of data scientists in charting a successful career path. Please share your experiences and wisdom in the comments below. 🚀🗺️ #DataScience #CareerAdvice #DataScienceCareers"

😁 Eleven years ago, the fate of data scientists’ careers changed across the globe with the publication of a Harvard Business Review blog titled: “Data Scientist: The Sexiest Job of the 21st Century”. 🙌 The article sold the idea of becoming a data wizard to millions of students, resulting in the meteoric rise of computer science and statistics related degrees. Check out the article to learn more about the state of data scientists now, and what is happening with all these graduates #employment #jobs #datascientists

The data science landscape is evolving rapidly, and one of the notable casualties in this transformation is the traditional notebook-based data scientist. When I commenced my career back in the early 2010s, the role of a Data Scientist was often referred to as "the sexiest job of the 21st century." However, almost a decade later, it seems this designation hasn't aged very well. You might wonder why. The answer lies in the fact that in many big tech companies, the responsibility for handling mission-critical machine learning systems now typically falls to a combination of Research and Applied Scientists and Machine Learning Engineers (MLEs). If you examine the job descriptions for Data Scientists at these tech giants, you'll notice that many of them no longer require a strong emphasis on machine learning. Instead, the role is often akin to that of a data analyst. At present, the traditional, run-of-the-mill data scientist is rapidly becoming a dying breed. Those who cannot contribute beyond the confines of a Jupyter notebook environment will struggle to maintain relevance in the machine learning field. In my opinion, this field is approaching a full circle. Before the advent of Data Scientists, we had developers, analysts, statisticians, and economists. Moving forward, the role of data scientists, as we currently understand it, will likely diverge into distinct paths of analysts and developers. #machinelearning #datascience #softwareengineering #aiml

Every day brings new technology, but which skills are employers truly seeking? 💻 Our team has dug into the data 🕵️♀️, uncovering unexpected findings. We've noticed an upward trend. Employers seek data scientists with a broader skillset that falls outside the traditional scope of the role. Although this is partially due to the complexity and interconnectedness of data science roles, it’s clear that smaller firms are actively seeking data scientists with advanced skills and software related to the following areas: 🔧 Data engineering ☁️ Cloud computing 📐 Data architecture Does this mean the modern data scientist must be a master of all? No. But it does highlight the importance of advanced specialization. Mastering these areas—while not essential to the core data scientist skillset—can give you a competitive edge. Want to know more about the specific skills required in the 2024 data science job market? 👉 Check out our blog post about the job prospects of data science this year. Link ➡️ https://bit.ly/4abtJgU . . . #datascience #datascienceskills #advancedskills #datasciencecareers

Difference between Data Engineer, ML Engineer, Data Scientist, Data Analyst....... Did you ever confuse of these titles.....? This article uses the metaphor of a track team to differentiate between the role of a data analyst, data scientist, and machine learning engineer. We’ll start with the idea that conducting a data science project is similar to running a relay race. Hopefully, this analogy will help you make more informed choices around your education, job applications. #dataanalytics #datascience #machinelearningengineer

From Business Student to Data Scientist in Tech: The First-Years Chronicles of a Data Scientist in Tech A timeline of how I went from being scared of maths to becoming a full-fledged Data Scientist at a renowned Tech firm Amongst the most frequently asked questions I often receive on LinkedIn, there is one that consistently stands out: How did I switch overnight from Business to Engineering and made it to become a Data Scientist? So in this story, I will delve into my personal journey, and share with you the steps I followed, challenges I faced, and valuable lessons I gained that propelled me straight towards becoming a Data Scientist in Tech.Photo by carolyn christine on Unsplash There is no one specific way to becoming a Data Scientist. As the saying goes, all roads lead to Rome. However, I am here to share one of the multiple ways this can be done. Especially for those starting with a Business degree and little to no scientific background. Before delving into the how, we first need to lay down the foundations for the why. Trust me, you will not go too far if you don’t get your why straight and sound from the start. The journey to becoming a Data Scientist is an arduous one, but surely one of the most rewarding too. As a matter of fact, the list of reasons why Data Scientists have one of the coolest and most coveted jobs today, stretches long. For now, I will focus on the one that sits on top of my list. The Why To anyone asking me why Data Science? whether it be during interviews or simply curious people, my answer is always the same. I wanted to be a Detective, so I decided to become a Data Scientist. You might wonder how these two have anything to do with each other? The first Data Scientists I’ve ever met instantly felt like they embodied what a modern-day Sherlock Holmes would be. Data Scientists play with knowledge to solve puzzles everyday. Ultimately, they spend most of their time investigating numbers to bring solutions to complex problems that only a keen analytical mind can solve. That is exactly what detectives do.How I picture myself sometimes — Photo by alexey turenkov on Unsplash I have always felt a deep sense of excitement following the adventures of Sherlock Holmes who restlessly pursued one clue after the other until the mystery was unraveled. I wanted to be an adventurer like him, but I hardly pictured myself dropping my studies to go solve crimes with the cops. I guess that felt a bit too extreme for my taste, and I was kind of hoping not to antagonise the bad guys this early in life. So being a Data Scientist felt like the best of both worlds. It simply took some time for that epiphany to bless me with its light. And by then, becoming one felt like a big stretch from what I was pursuing at that time – a Business degree. Looking back, the only trait I shared with Sherlock Holmes was my knack for diving… #MachineLearning #ArtificialIntelligence #DataScience

Hello all my friends with a Data Engineering background! I'm looking for some advice. Imagine you are the CTO of an up & coming edu tech startup and are looking to hire your first Data Engineer. It's exciting times, the company is growing and maturing, and you are headed in all the right directions. However... your background is more in the Product Development arena, and data engineering & data science is all new to you. How would you go about giving a prospective hire a Data Engineering technical task? What would you be looking for? What would the candidate be expecting? I appreciate your thoughts! #dataengineering #technicaltask #datascience