Data Science Career Guide

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?

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.

𝐘𝐨𝐮 𝐃𝐎𝐍'𝐓 𝐧𝐞𝐞𝐝 50 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠. You need 7 books. That's it. Most beginners jump straight into tools — Spark, Kafka, Airflow — without understanding how data systems actually work. Then they wonder why nothing connects. These 7 books fix that 👇 📘 Fundamentals of Data Engineering — Joe Reis & Matt Housley → Read this FIRST. Gives you the full picture before you touch any tool. 📘 Designing Data-Intensive Applications — Martin Kleppmann → The bible of distributed systems. Explains WHY systems fail at scale. 📘 Streaming Systems — Tyler Akidau → Makes Kafka, Flink, and Spark Streaming actually make sense. 📘 The Data Warehouse Toolkit — Ralph Kimball → Old but gold. Dimensional modeling that every DE should know. 📘 Data Engineering with Python — Paul Crickard → Theory to code. Build real pipelines with Python + Airflow. 📘 Data Pipelines Pocket Reference — James Densmore → Quick reference for pipeline patterns. Keep it on your desk. 📘 Designing Cloud Data Platforms — Zburivsky & Partner → Cloud architecture decisions explained clearly. Here's the order I'd recommend: 1 → Fundamentals of Data Engineering (understand the system) 2 → DDIA (understand how systems break) 3 → Data Warehouse Toolkit (understand modeling) 4 → Streaming Systems (understand real-time) 5 → Data Engineering with Python (start building) 6 → Data Pipelines Pocket Reference (quick patterns) 7 → Designing Cloud Data Platforms (cloud architecture) Reading builds intuition. Practice builds skills. You need both. I wrote a detailed breakdown of each book — what it teaches, what it won't help with, and when to read it. You can read it below ⬇️ Save this for later. Share it with someone starting out. ---- Follow Darshil Parmar for more data engineering content.

If I had to start my job search in data science today, I wouldn’t be chasing tech startups or AI research labs. I’d be looking at banks and hospitals first. After working with banks for three years, I’ve seen firsthand how they leverage data science at scale, but I’ve also seen the massive gaps in talent they’re trying to fill. And it’s the same in healthcare. 1. These institutions handle more data than almost any other industry + Banks and hospitals sit on mountains of data. Every transaction, loan, medical record, and patient interaction is a potential goldmine for analytics, risk modeling, and predictive insights. Unlike tech startups that often struggle to get quality data, these industries are drowning in it. 2. They have real business impact + Tech companies use data to optimize ad clicks. Banks and hospitals use data to save billions and, in some cases, save lives. + In banking, data science powers fraud detection, risk modeling, credit scoring, and regulatory compliance. In healthcare, it’s used for predictive patient care, operational efficiency, and disease modeling. +The stakes are higher, and so is the impact. 3. They are willing to train & invest in data talent + Unlike many tech companies that expect candidates to already have elite experience, banks and hospitals actively invest in upskilling their workforce. Many have internal training programs, partnerships with universities, and well-structured career paths for data professionals. 4. They offer more stability in a volatile job market + The past two years have shown that tech layoffs are brutal. Banks and hospitals, on the other hand, aren’t going anywhere. Even in downturns, they remain essential industries that need data professionals to operate efficiently. 5. They are expanding their use of AI & ML + Financial institutions are automating risk assessments, improving fraud detection, and optimizing investments using AI. Hospitals are using machine learning for predictive diagnostics and patient outcome modeling. These industries are aggressively hiring data talent to modernize their operations. If I were breaking into data science today, I’d be looking where the data is abundant, the impact is real, and the hiring demand is strong. And right now, banks and hospitals check all three boxes. If you’re job hunting, have you considered these industries?

Data engineering isn't Apache Spark. Data engineering isn't Apache Kafka. Data engineering isn't Apache Airflow. Data engineering isn't Snowflake. Data engineering isn't Apache Hadoop. Data engineering isn't Google BigQuery. Data engineering isn't Apache Cassandra. Data engineering isn't Databricks. Data engineering isn't Apache Flink. Data engineering isn't Amazon Redshift. Data engineering isn't just code. It's about understanding data flow. It's database design and optimization. It's data modeling and schema evolution. It's ensuring data quality and consistency. It's building scalable and resilient systems. It's optimizing query performance. It's designing ETL and ELT processes. It's managing data lineage and governance. It's balancing consistency, availability, and partition tolerance. It's turning raw data into valuable insights. Tools and platforms are enablers. The core of data engineering is architecture. Without solid principles, the pipelines are fragile. Tools come and vanish, but principles endure. Today's cutting-edge platform is tomorrow's legacy system. Master the fundamentals, and you can adapt to any tool. Note: Data engineering isn't about fancy tools—it's about how those tools are leveraged to create robust, scalable, and efficient data ecosystems. #DataEngineering #BigData #DataArchitecture #ETL #DataPipelines

5 roles I think we'll see sooner rather than later in our emerging AI-Forward world: 🤝 (1) Human-AI Interaction Designer Crafting AI personalities that adapt seamlessly to diverse users and contexts. They'll design clear boundaries for when AI defers to humans, enhancing our abilities without fostering dependency or imbalance. E.g., ensuring AI interactions with healthcare patients remain empathetic, supportive, and deferential to professional judgment 🧠 (2) AI Behaviour Therapist Diagnosing unexpected AI behaviours by tracing issues through data, model architecture, or emergent patterns. They'll implement targeted interventions—like fine-tuning and retraining—to ensure AI behaves predictably and ethically. E.g., addressing biased decision-making in AI hiring tools 🧪 (3) Synthetic Data Designer Masterfully blending real and synthetic datasets to shape precise AI outcomes. These experts will fine-tune data combinations to enhance capabilities and proactively eliminate bias. E.g., creating tailored synthetic data to train fraud detection systems in financial services ⚖️ (4) AI Compliance Officer Translating complex, evolving global AI regulations into actionable technical guidelines. They’ll bridge law, ethics, and technology, ensuring AI systems remain compliant yet highly functional. E.g., ensuring financial algorithms meet regulatory fairness standards 🛡️ (5) Cognitive Firewall Engineer Building invisible safeguards that protect essential human decision-making authority. They’ll prevent "automation creep" by ensuring human oversight at critical decision points across workflows. E.g., safeguarding human approval in automated medical diagnoses. Three characteristics span across these emerging roles: 1️⃣ Setting AI-Human Boundaries: Clearly defining the limits between human and machine intelligence, empowering rather than replacing human judgment 2️⃣ Interpreting Emergent Behaviours: Tackling unpredictable AI behaviours through continuous observation and dynamic, adaptive responses 3️⃣ Guarding Human Agency: Preserving meaningful human control amidst growing AI integration, ensuring technology remains a powerful tool rather than an unchecked force Which roles resonate? And which emerging roles did I miss? #AIForward #FutureRoles

Your existing skills may already fit AI. You may not need to become a machine learning engineer to build a career in this space. AI is creating new roles for strategists, communicators, builders, operators, domain experts, and governance professionals. Here are 7 emerging roles to watch: → 𝗔𝗜 𝗖𝗵𝗶𝗲𝗳 Leads enterprise AI strategy, investment, governance, risk management, and organization-wide adoption. → 𝗔𝗜 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗲𝗿 Translates complex AI products, research, and ideas into clear stories for customers, leaders, investors, and the public. → 𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 Works directly with customers to understand business problems and build tailored AI solutions using company technology. → 𝗔𝗜 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿 Helps teams adopt AI tools, automate repetitive work, redesign processes, and turn experiments into repeatable workflows. → 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗲𝗿 Uses generative AI, APIs, and low-code platforms to rapidly create prototypes, applications, automations, and digital products. → 𝗔𝗜 𝗚𝗶𝗴 𝗪𝗼𝗿𝗸𝗲𝗿 Provides specialist knowledge, human feedback, evaluation, and real-world examples to train and improve AI systems. → 𝗔𝗜 𝗣𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝗲𝗿 Studies how AI should behave and helps align systems with ethics, safety, policy, governance, and human values. The opportunity is broader than technical development. Business leadership, communication, consulting, product thinking, research, and domain expertise can all become valuable AI career advantages. Which of your existing skills could transfer into one of these AI roles?

From Data Analyst to Data Scientist with a $95k offer in under 6 months without a “perfect” portfolio. Here’s what made the difference for a client of mine: 𝟭) 𝗕𝘂𝗶𝗹𝘁 𝗮 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: We started with a tailored roadmap, breaking down each step into daily actions. Instead of trying to learn everything, we targeted just a few tools, each relevant to the skills and roles they were aiming for. 𝟮) 𝗚𝗿𝗲𝘄 𝗧𝗵𝗲𝗶𝗿 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲: This client often felt behind when comparing their skills to others. We focused on what they already excelled at, pushing them to apply and interview before they felt “ready.” With each attempt, confidence grew, a reminder that nothing reinforces skill like action. 𝟯) 𝗡𝗶𝗰𝗵𝗲𝗱 𝗧𝗵𝗲𝗶𝗿 𝗕𝗿𝗮𝗻𝗱: Previously, their profile was too broad. We focused their portfolio on health care and NLP, then optimized it with keywords that attracted the right employers. Employers started noticing, even before they’d wrapped up their main project. 𝟰) 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗦𝗲𝘁𝗯𝗮𝗰𝗸𝘀: They’d had a pattern of starting courses, then quitting. Through daily check-ins, clear action steps, and real-time feedback, they overcame self-sabotage and kept momentum even when it was tough. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀? - 6 months of targeted effort, with 4 focused on skill-building - 50 applications, 3 interviews, and 1 offer This transition didn’t happen by waiting for “perfect.” It happened by taking daily action, building on existing skills, and niching their value. If you’re waiting to feel “ready,” this is your reminder: take the first step today. What’s one skill you want to strengthen next?

During undergrad, I did 11 internships, yep, 11. Not because I had a perfect resume, but because I treated each opportunity like a mini-lab, where I could test, learn, and iterate fast. Data science isn’t just about writing Python scripts. It’s about turning ambiguity into insights and building conviction through evidence. If you’re looking to land your first data science internship, here are 10 strategies that go beyond the obvious, and actually work: 1️⃣ Start with one tangible business problem Don’t start with models - start with pain points. Find a local business, club, or nonprofit and ask: What decision do you struggle with the most? Then solve it with data. 2️⃣ Document the why, not just the how It’s not impressive that you used XGBoost. What’s impressive is why you chose it, what didn’t work before, and how your decisions reduced error rates by 20%. 3️⃣ Master one “power tool” deeply Pick SQL, Pandas, or scikit-learn - then go really deep. I don’t mean just syntax. Learn edge cases, performance trade-offs, debugging. You’ll stand out for how you think, not just what you know. 4️⃣ Quantify impact on your resume “Built a dashboard” is vague. “Built a dashboard that saved 3 analysts 5+ hours/week” speaks volumes. Tie your work to time, money, or decisions. 5️⃣ Contribute to open-source meaningfully Don’t just fix typos. Pick a bug tagged “good first issue,” and make sure it’s non-trivial. This shows real-world code fluency and willingness to work within large codebases. 6️⃣ Ask for code reviews - even informal ones DM someone you admire and ask: Can I get your feedback on a small project? I’d love to hear what I’m missing. Most won’t respond. But the 1 who does? that is your edge! 7️⃣ Practice a two-minute “whiteboard walkthrough” Internship interviews are not Kaggle competitions. Can you clearly explain your project, decisions, results, and trade-offs without opening your laptop? 8️⃣ Leverage hidden-curriculum courses You don’t need another Coursera cert. Find courses that teach how to think like a DS, not just “how to build a model.” I loved fast.ai and made custom notes I still refer to. 9️⃣ Align with the team’s stack Before you apply, reverse-engineer the role. Do they use Airflow? Snowflake? Hugging Face? Tailor your personal projects and resume accordingly. Match their environment. 🔟 Treat the interview like hypothesis testing You’re not there to impress. You’re there to validate a fit. Ask sharp questions about the role, data maturity, and mentorship culture. You’re evaluating them too. Internships aren’t just about “getting in”. They’re about compounding your learning so fast that by the time you graduate, you’re not looking for your first job - you’re choosing it. ♻️ Share it with someone who’s stuck in the “I need experience to get experience” loop Follow me on IG https://lnkd.in/denE_Zpw for beginner-friendly tips, tools, and insights to get started!