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.

Out-of-stock products are a major frustration in online grocery shopping. When customers order their weekly essentials only to find that an item isn’t available, the quality of the suggested replacement can make or break their experience. In a recent tech blog, data scientists at Instacart shared how they tackled this challenge with a customized machine learning system built on two complementary models. - The first model leverages product category information to understand general similarity relationships across the catalog. This helps address the cold-start problem — when new or niche items lack sufficient engagement data to capture customer preferences. - The second model, known as the engagement model, learns directly from user behavior — such as which replacements were accepted or rejected. This enables the system to “remember” customer preferences for popular products and more accurately reflect how people perceive product similarity. During development, the team discovered an interesting bias: the model tended to favor well-known national brands that appear across multiple retailers, rather than local store brands. To fix this, they made the system retailer-aware by incorporating retailer IDs into its schema. This small but powerful adjustment led to more relevant and balanced recommendations — better aligned with customer expectations and price preferences. This project is a good example of how customized machine learning architectures can address real-world business challenges, and a nice read for anyone interested in applied machine learning. #DataScience #MachineLearning #Recommendation #Engagement #Customization #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gXA9N4dZ

12 Must-Know SQL Concepts Every Data Tech Should Master Here are the fundamental SQL concepts you need to know to excel in Tech Industry : 1. SELECT Mastery The foundation of data retrieval. Understanding complex SELECT statements, including subqueries and conditional logic, is essential for precise data extraction. 2. JOIN Operations Master INNER, LEFT, RIGHT, and FULL OUTER JOINs. These are your tools for connecting related data across multiple tables - crucial for meaningful analysis. 3. GROUP BY Expertise Beyond basic grouping, learn to use it with HAVING clauses and window functions for sophisticated data aggregation and analysis. 4. Index Optimization Know when and how to create indexes. They're vital for query performance, but remember - not every column needs an index. 5. Subquery Implementation From correlated subqueries to derived tables, these are your secret weapons for complex data operations. 6. Window Functions Learn PARTITION BY, ROW_NUMBER(), and LAG/LEAD functions. They're game-changers for advanced analytics. 7. Database Normalization Understanding 1NF through 3NF is crucial for efficient database design and data integrity. 8. Transaction Management Master ACID properties and transaction isolation levels for maintaining data consistency. 9. Views Usage Create and maintain views for data security and query simplification - essential for large-scale databases. 10. Constraint Implementation From PRIMARY KEYs to CHECK constraints, these are your guardians of data integrity. 11. Common Table Expressions Master CTEs for recursive queries and improved code readability - your key to maintainable code. 12. ACID Properties Understanding these principles ensures reliable database transactions and data consistency. Tip - Don't just memorize syntax - understand the underlying concepts and best practices.

95% of retailers reported using AI in their business. Adoption is widespread! But we also found that just 5% reported seeing clear scalable ROI on their investment according to our latest research in partnership with Voyado. It’s a stark statistic! We found the landscape of AI sophistication across the retail sector is very uneven. We mapped out the transitional journey, and most businesses businesses all into one of four stages. ➡️ Phase 1: Exploration, where retailers are testing AI through pilots and proof-of-concepts. ➡️ Phase 2: Pilot scaling, where AI is used in selected functions, but not yet embedded across workflows. ➡️ Phase 3: Operational, where AI is integrated into several core marketing and e-commerce processes. ➡️ Phase 4: Embedded strategy, where AI informs decision-making at a strategic level and is woven into planning, execution and optimization across the business. What this tells me is that the ambition is high, but many businesses are faltering because structure, data and culture haven’t caught up. Part of the reason for this is because as organisations scale, complexity increases faster than integration capability. They end up with fragmented systems and more complex governance layers, which slows down decision cycles. To this end, AI maturity rarely progresses in a straight line. As we've heard many times before, data is the key differentiator. But what makes the real difference is the structural integration of customer, product, and commercial data - all together. These three areas will be critical in determining how far AI can act autonomously and how confidently it can optimise decisions across the business. In the industry, we have spoken about personalisation for a very long time. But real personalisation sits at the intersection of data, decisions, and execution. For this to work, it requires unified customer signals, connected workflows across channels and organisational confidence in automated optimisation. This is not easy. Our research shows that lack of internal skills as the primary barrier to advancing AI - cited by 58% of retailers. Most retailers have access to advanced AI tools through platforms or vendors. But few have the in-house expertise to deploy, govern and optimize them at scale. This limits their ability to tune and refine models, and creates uncertainty as to how to measure AI-driven performance. What separates advanced retailers is how far that integration extends. We found that in more advanced organisations, AI is integrated earlier in the decision chain. It informs planning, prioritisation, and commercial trade-offs across functions. This can include: ➡️ Influencing budget allocation across channels ➡️ Informing pricing and promotional strategy ➡️ Shaping inventory and margin trade-offs Things are moving at warp speed at the moment. Keep up by downloading our latest research here: https://lnkd.in/eqJibUb4

Are you ready to master SQL as a data analyst? Here are some tips to start your journey! 1. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀: Start with the fundamental concepts like SELECT statements, WHERE clauses, and logical operations. These are your building blocks for querying your databases.     2. 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲: Practice on platforms like LeetCode, HackerRank, and Mode Analytics to solve SQL problems and build your confidence.     3. 𝗟𝗲𝗮𝗿𝗻 𝗝𝗼𝗶𝗻𝘀 𝗮𝗻𝗱 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀: Mastering different types of joins (INNER, LEFT, RIGHT, FULL) and subqueries is important. These skills are needed for complex data manipulation over multiple tables.     4. 𝗪𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗖𝗧𝗘𝘀: Common Table Expressions (CTEs) can simplify your queries and make them more readable. Learn how to use CTEs to break down complex problems into manageable parts.     5. 𝗨𝘀𝗲 𝗥𝗲𝗮𝗹 𝗗𝗮𝘁𝗮: Work with real datasets to understand the context and nuances of data analysis. Kaggle or governmental statistical sites are a great resource for finding interesting datasets to practice on.     6. 𝗥𝗲𝗮𝗱 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Familiarize yourself with the SQL documentation for the specific database management system (DBMS) you’re using, whether it’s MySQL, PostgreSQL, or SQL Server.     7. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗤𝘂𝗲𝗿𝗶𝗲𝘀: Learn about query optimization techniques. Efficient queries can significantly improve performance, especially with large datasets.     8. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: Use version control systems like Git to manage your SQL scripts. This helps in tracking changes and collaborating with others.     9. 𝗕𝘂𝗶𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Build small projects that interest you. Creating your own database and running queries on it makes learning more enjoyable and practical. Follow these tips and you’ll build a strong SQL foundation. While SQL is not the only skill you will need to start a career as a data analyst, it's the most important one for most positions. What are your favorite resources for learning SQL? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #sql #learningpath #careergrowth

🧱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 ≠ 𝗝𝘂𝘀𝘁 𝗠𝗼𝘃𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 That myth limits your growth before it begins. It is the foundation layer. Without it, AI/ML teams can’t scale. Data engineering isn’t about pipelines alone— It’s about building platforms that power decisions and collaborating across teams to make data truly valuable. 🚫 𝗧𝗵𝗲 𝗠𝗶𝘀𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝗶𝗼𝗻 Most people think data engineering ends at ETL. But in reality, we architect the systems that make data usable, trustworthy, and scalable—in partnership with analysts, product teams, and engineers. 🤝 𝗪𝗵𝘆 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Modern data engineering is not a solo act. You’re not just building pipelines—you’re enabling:  • Analysts to explore and visualize data  • Product teams to make informed decisions  • Engineers to integrate data into applications  • Governance teams to ensure compliance and trust  • Without collaboration, even the best pipelines go unused. 🧩 Two Evolution Paths for Data Engineers - 📊 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀-𝗙𝗼𝗰𝘂𝘀𝗲𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 - The Foundation Builders They ensure business teams have clean, well-modeled, and governed data to work with. - What they do:  • Build batch pipelines and data marts  • Design semantic layers and data contracts  • Partner with analysts and BI teams - Core skills:  • SQL & dimensional modeling  • Apache Spark, Airflow, dbt  • Data warehouse tuning (Snowflake, BigQuery)  • Data quality frameworks 🏗️ 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 - The Infrastructure Enablers They build the systems that scale data across teams, products, and use cases. - What they do: • Architect real-time and event-driven pipelines  • Build self-service data platforms  • Collaborate with infra, security, and product teams - Core skills:  • Stream processing (Kafka, Flink)  • Data lakehouse architecture (Delta, Iceberg)  • API design & metadata management  • Infra-as-Code (Terraform, CDK) 🎯 𝗧𝗵𝗲 𝗚𝗼𝗮𝗹: 𝗕𝗲 𝗧-𝗦𝗵𝗮𝗽𝗲𝗱  • Deep in your core path (analytics or platform)  • Broad across the data lifecycle  • Collaborative across teams and domains ✅ 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸  • Analytics engineers must understand how data is consumed  • Platform engineers must understand how data is used  • Both must design for collaboration, scale, and change 🧭 𝗖𝗵𝗼𝗼𝘀𝗲 𝗬𝗼𝘂𝗿 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 📊 Love modeling and enabling insights? → Analytics Foundations 🏗️ Love building systems and infra? → Platform Engineering But remember: Data engineering is a team sport. Start deep in your strength. Grow into the ecosystem. Collaborate to scale your impact. 💬 Your Turn Which path are you on: Analytics-Focused or Platform-Oriented? Stay tuned with me(Pooja Jain) for more on #Data #Engineering!

Turning retail data into real business results can be a tough problem, but the solution isn’t more tech. It starts with grounding your data strategy in business outcomes and building from there. In this article, I break down how retail teams can move from disconnected dashboards and siloed analytics to a practical, value-focused data strategy. Key lessons include: 1️⃣ Start with the business problem not the tools + Clarify the goal first (like reducing churn or improving customer experience) 2️⃣ Empower better questions across teams so insights actually lead to action 3️⃣ Put data governance in place early to build trust in your numbers 4️⃣ Balance pace with capacity so you deliver wins without burning out 5️⃣ Build data literacy and ownership so everyone speaks the same language I also cover where the retail data push usually starts, how AI fits in practically (not hype), and what future trends are shaping smarter operations. If you work in retail or data and want strategies that actually move the needle, I hope this will help you: https://lnkd.in/gEubetHW

When most people think of data science in banking, they assume it’s all about fraud detection and credit risk modeling. While those are important, they barely scratch the surface of how financial institutions use data. After three years working in finance, I’ve seen firsthand how banks leverage data science to make billion-dollar decisions and the unique challenges that come with it. So... what does it actually looks like? - Regulatory Stress Testing –> Banks must forecast how they’d perform in a financial crisis. Data scientists help model these scenarios, ensuring institutions can survive worst-case economic conditions. - Algorithmic Trading & Market Risk –> Trading desks rely on machine learning to predict market movements, optimize portfolios, and manage risk in real time. - Anti-Money Laundering (AML) & Fraud Detection –> Detecting fraudulent transactions focused on reducing financial exposure, ensuring compliance, and protecting reputations. - Customer Insights & Personalization –> From predicting loan defaults to recommending investment products,banks use data to understand customer behavior at scale. - Operational Efficiency & Cost Reduction –> Everything from loan approvals to call center analytics is optimized using data science to improve speed, accuracy, and profitability. With that said, you might be asking yourself why banks are investing in data talent now more than ever? + Regulatory Pressure is Increasing –> Global financial regulations require banks to have more sophisticated risk models and better reporting mechanisms. + Competition from Fintech –> Traditional banks are competing with tech-driven financial companies that operate faster and more efficiently using data. + AI & Automation Are Reshaping Finance –> From AI-powered chatbots to automated underwriting, banks are leveraging data to scale decision-making. However, banking does come with some caveats. - Regulations Slow Everything Down –> Unlike tech companies, banks can’t just deploy a new model overnight. Everything must be tested, validated, and approved before going live. - Data Privacy is a Massive Concern –> Financial institutions handle highly sensitive data, meaning strict security protocols and compliance laws add complexity. - Legacy Systems Make Implementation Harder –> Many banks still rely on outdated infrastructure, making data integration and real-time analytics more difficult than in other industries. If you’re in data science, have you ever worked in a regulated industry? What’s the most surprising use case you’ve seen?

There are two ways to solve a problem in Finance and FP&A Analogy: "We’ve always done it this way, let’s just tweak the template." First Principles: "What is undeniably true about this data, and how can we build it from scratch?" Method #1 keeps you stuck in manual reporting loops. Method #2 opens the door to Python, AI Agents, and full automation. Use this guide to start: https://lnkd.in/eB9Rvwbw I’m re-reading Thinking Like a Rocket Scientist by Ozan Varol, and I thought how to apply First Principles Thinking to AI in Finance: Step 1: Identify & Challenge Assumptions We carry invisible baggage that limits us. You need to identify these myths: ❌ "AI is too complex—only data scientists can use it." ❌ "Automation means buying expensive software." ❌ "Python is for coders, not finance people." Step 2: Break Down to First Principles Ask: What is undeniably true here? ✅ Data is Data. Whether it's a P&L or a CSV, it's just structured information. ✅ Python is Logic. It’s not "code"; it’s a language for expressing logic. ✅ LLMs are Engines. They can read your logic and write the code for you. Once you accept these truths, the barrier to entry disappears. Step 3: Rebuild from the Ground Up Here is your new roadmap to build an AI-powered Finance function: 1. Define the Workflow Don't automate "everything." Pick one high-value task: Variance Analysis, Cash Flow Projections, or Management Reporting. 2. Translate to Logic Map it out: Input (Excel) → Process (Calculate Variance) → Output (Email Summary). 3. Use LLMs as your Co-Developer You don't need to know the syntax. Just prompt: "Write a Python script to load this CSV, calculate the variance between Col A and Col B, and summarize the top 3 drivers." Detailed Guide here: https://lnkd.in/eF6ZxY4t 4. Build in Google Colab You don't need to download any software. Use cloud-based notebooks to test your ideas instantly. https://lnkd.in/eVjchqAS 5. Build Analytics Superpowers Move beyond dashboards. Use AI to generate "what-if" scenarios and predictive insights that Excel simply can't handle. A few final tips for the journey: Test small: Automate one report first. Question everything: "Why are we doing this manually?" Scale responsibly: Always validate the output. I think the future of Finance belongs to those who think like Rocket Scientists, not those who just copy last year's template. Which of those "Assumptions" in Step 1 is holding your team back the most? Let me know in the comments.