How to Develop Essential Data Science Skills for Tech Roles

It took me 6 years to land my first Data Science job. Here's how you can do it in (much) less time 👇 1️⃣ 𝗣𝗶𝗰𝗸 𝗼𝗻𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 — 𝗮𝗻𝗱 𝘀𝘁𝗶𝗰𝗸 𝘁𝗼 𝗶𝘁. I learned SQL and Python at the same time... ... thinking that it would make me a better Data Scientist. But I was wrong. Learning two languages at once was counterproductive. I ended up being at both languages & mastering none. 𝙇𝙚𝙖𝙧𝙣 𝙛𝙧𝙤𝙢 𝙢𝙮 𝙢𝙞𝙨𝙩𝙖𝙠𝙚: Master one language before moving onto the next. I recommend SQL, as it is most commonly required. ——— How do you know if you've mastered SQL? You can ✔ Do multi-level queries with CTE and window functions ✔ Use advanced JOINs, like cartesian joins or self-joins ✔ Read error messages and debug your queries ✔ Write complex but optimized queries ✔ Design and build ETL pipelines ——— 2️⃣ 𝗟𝗲𝗮𝗿𝗻 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝗮𝗽𝗽𝗹𝘆 𝗶𝘁 As a Data Scientist, you 𝘯𝘦𝘦𝘥 to know Statistics. Don't skip the foundations! Start with the basics: ↳ Descriptive Statistics ↳ Probability + Bayes' Theorem ↳ Distributions (e.g. Binomial, Normal etc) Then move to Intermediate topics like ↳ Inferential Statistics ↳ Time series modeling ↳ Machine Learning models But you likely won't need advanced topics like 𝙭 Deep Learning 𝙭 Computer Vision 𝙭 Large Language Models 3️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 & 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝗲𝗻𝘀𝗲 For me, this was the hardest skill to build. Because it was so different from coding skills. The most important skills for a Data Scientist are: ↳ Understand how data informs business decisions ↳ Communicate insights in a convincing way ↳ Learn to ask the right questions 𝙇𝙚𝙖𝙧𝙣 𝙛𝙧𝙤𝙢 𝙢𝙮 𝙚𝙭𝙥𝙚𝙧𝙞𝙚𝙣𝙘𝙚: Studying for Product Manager interviews really helped. I love the book Cracking the Product Manager Interview. I read this book t𝘸𝘪𝘤𝘦 before landing my first job. 𝘗𝘚: 𝘞𝘩𝘢𝘵 𝘦𝘭𝘴𝘦 𝘥𝘪𝘥 𝘐 𝘮𝘪𝘴𝘴 𝘢𝘣𝘰𝘶𝘵 𝘣𝘳𝘦𝘢𝘬𝘪𝘯𝘨 𝘪𝘯𝘵𝘰 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦? Repost ♻️ if you found this useful.

Are you contemplating to pivot into data analytics & data science field? As someone who has been in the field since 2013, and who's been mentoring and coaching others in the data field for the past 7 years, here are my thoughts: 𝐓𝐢𝐦𝐞-𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐠𝐢𝐯𝐞𝐧 𝐭𝐨𝐝𝐚𝐲’𝐬 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐭𝐞𝐜𝐡 𝐥𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞: 𝟏) 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬 𝐨𝐯𝐞𝐫 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐬𝐜𝐫𝐚𝐭𝐜𝐡 Instead of learning SQL or Python from scratch, focus on using AI tools to meet existing analysis needs. For example, master how to craft prompts to generate SQL or Python code, or use GenAI to build processes, streamline data workflows, and uncover insights faster. You can also harness LLMs to enhance your analysis and insights generation, rather than slowly building your portfolio through years of hands-on experience. Use LLMs to critique and refine your insights and recommendations, ensuring that what you propose aligns with business goals and stakeholder questions. 𝟐) 𝐓𝐚𝐫𝐠𝐞𝐭 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐞𝐬 𝐰𝐢𝐭𝐡 𝐠𝐫𝐨𝐰𝐭𝐡 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 Focus on industries with bright futures like GenAI, healthcare, cybersecurity, green energy, or mental health. These sectors are more likely to need data professionals to drive growth through analysis and insights. Do your research by searching for industry reports or talking to seasoned practitioners to identify promising industries. Reports or analyses published by organizations such as below can be your start, e.g. US Bureau of Labor Statistics, McKinsey Global Institute, World Bank, CB Insights, or Gartner. 𝐒𝐨𝐦𝐞 𝐭𝐢𝐦𝐞𝐥𝐞𝐬𝐬 𝐚𝐝𝐯𝐢𝐜𝐞: 𝟏) 𝐆𝐞𝐭 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐟𝐢𝐫𝐬𝐭, 𝐜𝐫𝐞𝐝𝐞𝐧𝐭𝐢𝐚𝐥𝐬 𝐚𝐧𝐝 𝐩𝐞𝐫𝐟𝐞𝐜𝐭𝐢𝐨𝐧 𝐥𝐚𝐭𝐞𝐫 Instead of pursuing yet another bootcamp or credential (though you do need baseline technical skills), start by volunteering, interning, or offering to help current practitioners with projects. Build a portfolio using open-source data, freelance on platforms like Fiverr or Upwork, and secure your first data job—even if it’s not a 100% match to your current criteria. The ideal industry or company will come later once you’re in the door. 𝟐) 𝐍𝐞𝐯𝐞𝐫 𝐬𝐭𝐨𝐩 𝐧𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠 Whether it’s validating a specific industry’s need for your skills, creating opportunities for referrals, or honing your pitch for future interviews, networking is critical for career transitions and building long-term influence in your field. Identify “hubs” of people or communities that can help you gain new opportunities. Communities such as Women in Big Data, Women in Data Science (WiDS) Worldwide, or Data Science Association (that I helped co-found), can be your starting point. If you've been contemplating or ready to make the switch, book a Discovery session (via my profile) as your first step! Let’s explore how I can help you in our 1:1 coaching space—where to focus, and what steps to take to launch your new career in data analytics.