Essential First Steps in Data Science

I helped 8 beginners land their first data job in 2024. These 4 costly mistakes held them back before we started to work together in my mentoring program. Out of the 22 clients I supported in my DataShip community, 8 of them were complete beginners in the field of Data. Here’s what I suggested to them to simplify their approach: Avoid: 🚫 Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once. 🚫 Spending months on theoretical concepts without hands-on practice. 🚫 Overloading your resume with keywords instead of impactful projects. 🚫 Believing you need a Ph.D. to break into the field. Instead: ✅ Start with Python or R and focus on mastering one language first. ✅ Learn how to work with structured data (Excel or SQL) - this is your bread and butter. ✅ Dive into a simple machine learning model (like linear regression) to understand the basics. ✅ Solve real-world problems with open datasets and share them in a portfolio. ✅ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests. Focus on this for your first 6 months. The key isn’t knowing everything It shows you can solve meaningful problems.

Job seekers are trapped in the "need experience to get a job, but need a job to get experience" cycle. Here is how you can break it: • Gain experience using public datasets: it's not about fancy machine learning projects. Start with cleaning, aggregating, and visualizing data in tools like Excel or Python in Google Colab. Find an interesting datasets from platforms like Kaggle, or US Goverment Open Data (https://data.gov/ ), or data from your city (e.g. Seattle's real-time fire 911 calls https://lnkd.in/gNEdS9Yk). ALWAYS create an artifact—a blog post, a GitHub repository, something to showcase. • Seek opportunities near you: Your uncle is running small business? They might need data insights. Your professor might be eyeing for someone to dissect student performance data. Reach out, offer your skills. Maybe you can collect your own data on your diet or sleep, and analyze it for yourself. (Data science YouTuber Ken Jee analyzed his own health data: https://lnkd.in/gf2SWNDq) No one is offering you a job? Create a job for yourself. • Leverage your current experience: maybe you are just learning data science but you have experience in other industries like marketing, finance, etc. You might not be the best data person, but you could be the person that knows more about the industry than an average data person, and knows more about about data than the average retailer. Leverage your current domain knowledge as a stepping stone, you don't have to start over completely. In the realm of data analytics, the world is your playground. Forget the traditional paths—carve out your own. There are multiple guests on my podcast started their career in non-tech roles. Experience isn't confined to job titles; it's crafted through initiative and passion. I interviewed a career coach who got into Google from non-tech background, learn more from our conversation: Apple: https://lnkd.in/gaM_cWP9 YouTube: https://lnkd.in/gCHTU94N Spotify: https://lnkd.in/g6fGuXzP #Datascience #Career

I’m 53. I've been doing analytics for 13+ years. Here are 8 no-BS steps I've learned to build DIY data science skills: 1) Crawl-Walk-Run Social media would lead you to believe that you must: Work with gigantic datasets. Use deep neural networks, LLMs, etc. Be an advanced mathematics wizard to do anything. It's simply not true.  Here's what you do... 2) Crawl - learn decision trees The dirty little secret of business analytics is that you usually use simple techniques. Prime example - decision tree machine learning: Works excellent with data tables. You can learn them using your intuition. When you're ready, you can learn the math. 3) Crawl - Use simple datasets When you first start, do yourself a favor and use simple datasets. While they get a lot of hate on social media, you want to concentrate on how decision trees learn from data. That's much easier when you use simple datasets. Worry about data wrangling later. 4) Walk - build ML fundamentals Once you build your intuitive understanding of decision trees, it's time for building skills with: Data profiling Feature engineering The bias-variance tradeoff Tuning decision tree models With these skills you're ready for... 5) Walk - the mighty random forest As the name suggests, random forests are collections of decision trees. In ML, this is known as an "ensemble." Ensembles of decision trees are state-of-the-art for real-world DIY data science. Next up, it's about the data. 6) Walk - data wrangling "Data wrangling" is a term for all the work needed to prepare data for DIY data science. Here's the thing, though. Building data wrangling skills is much easier when you know ML. This knowledge provides the context for what you need to do with the data. Moving on... 7) Run - apply it at work While I'm a big fan of using Kaggle to build initial skills for crawling and walking, Nothing beats applying what you've learned at work. Even if nobody ever sees it, the experience you will build is invaluable. P.S. - Don't do anything that will get you fired. 8) Run - expand With some work projects under your belt, it's time to expand your skills. Start with cluster analysis: K-means DBSCAN A powerful combination for DIY data science is cluster analysis + ML models for interpretation. Be sure to use this combo at work! Ready to build DIY data science skills? Join 6,175 professionals learning Python and ML with my free crash courses: https://lnkd.in/e7fVrjxC

Don’t let a lack of experience hold you back from pursuing a data science career… Before I broke into data science, I was constantly learning new things but didn’t have much work experience to show for it. Here’s what I did: 1. Built a portfolio I worked on personal projects to showcase my skills. 2. Joined online communities Networking with others in the field helped me understand the industry better and opened up opportunities. 3. Practiced coding interviews I spent time solving problems and practicing coding challenges. This was crucial in helping me get comfortable with technical interviews. 4. Highlighted my learning journey During interviews, I spoke about my dedication to learning and the projects I completed. ✨ My projects and learning outside of my data Bootcamp landed me my first job. There’s always another way to show your value. Don’t give up easily! P.S. What other strategies have you used to overcome the lack of experience in your job search? #DataSistah ♻️Reshare and help others.