Data Management Skills

Most professionals get stuck in reporting mode. You know, endless charts, dashboards, and status updates. But real impact happens when you show: Why it happened. What’s next. ...not just what happened last week/month/quarter. Here’s the ladder to level up your data skills: Level 1: Reporting You build dashboards, clean data, make charts. Tools: Excel, Sheets, Power BI. Make no mistake. This is foundational. This is called "Descriptive Analytics," and your leaders must have it. However, think of it like electricity. They'll only appreciate it when it's gone. Level 2: Exploratory Analysis Now you're asking: • What patterns are in the data? • What metrics truly matter? • Where are the outliers? This is where you get to why something happened. Tools: Excel, SQL, Python. Leaders value explanations - especially when things aren't going well. Level 3: Pattern Discovery (Unsupervised ML) You start finding structure in messy data. No labels. Just hidden groupings. Examples: • Customer segments • Product groupings Tools: K-means & DBSCAN. Start delighting leaders with your new insights. Use Python in Excel to get started. Level 4: Predictive Modeling (Supervised ML) Now you’re using data like a crystal ball: • Will a customer cancel? • Will a loan default? • Will a deal close? Tools: Decision trees & Random Forests. Successful predictions provide the "why." It's magical. Use Python in Excel to get started. Level 5: Mindset Are you already good at Excel?  You’re closer than you think. Steps 1 & 2?  You’ve probably got that down. Time to step up into 3 & 4. Remember - it isn't a leap. It's just the next rung on the ladder.

From BASIC to ADVANCED data engineering skills, begin your journey here 👉 The data engineering roadmap: 📍 Beginner Skills: a) Data Modeling:   • Understand relational database concepts   • Learn basic programming/SQL querying   • Practice creating Entity-Relationship Diagrams (ERDs) b) ETL Basics:   • Grasp the concept of Extract, Transform, Load (ETL)   • Use tools like Apache NiFi or Talend for simple data pipelines c) Data Storage:   • Familiarize with different database types (SQL, NoSQL)   • Learn basic data warehouse concepts d) Version Control:   • Master Git for code management   • Understand branching and merging strategies 📍 Intermediate Skills: a) Big Data Processing:   • Learn Apache Spark for distributed computing   • Understand batch vs. stream processing b) Data Warehousing:   • Implement star and snowflake schemas   • Use tools like Amazon Redshift or Google BigQuery c) Data Integration:   • Master Change Data Capture (CDC) techniques   • Implement data quality checks and data cleansing d) Cloud Platforms:   • Gain proficiency in AWS, GCP, or Azure data services   • Implement cloud-native ETL solutions 📍 Advanced Skills: a) Data Governance:   • Implement data lineage tracking   • Ensure compliance with regulations (GDPR, CCPA) b) Real-time Analytics:   • Design streaming data architectures   • Use technologies like Apache Kafka or Apache Flink c) Machine Learning Operations (MLOps):   • Design data pipelines for ML model training and deployment   • Implement feature stores for ML d) Data Mesh Architecture:   • Understand domain-driven design for data   • Implement data products and self-serve data platforms Key Architectural Considerations: 1. Scalability 2. Performance 3. Security 4. Cost-Effectiveness 5. Data Quality 6. Metadata Management 7. Interoperability Some important data platforms to explore are Snowflake, dbt, Databricks, and Matillion. As a data engineer, Good data engineering practices lay the foundation for trustworthy analytics and machine learning initiatives. If you're looking for a good list of resources and projects, find them below in the comments section!👇 It won't happen overnight. 𝗢𝗻𝗲 𝘀𝘁𝗲𝗽 𝗮 𝗱𝗮𝘆 𝗸𝗲𝗲𝗽𝘀 𝘁𝗵𝗲 𝘀𝘁𝗮𝗴𝗻𝗮𝘁𝗶𝗼𝗻 𝗮𝘄𝗮𝘆. Happy Learning :) Stay tuned for more! 🙂 #data #engineering #bigdata #cloud

✨3 Data Skills That Aren’t Talked About Enough✨ • 1️⃣ Ability to Understand Understanding goes beyond just fulfilling a request. Often, data requests come with little context. Your role isn’t just to pull data—it’s to understand why the request is being made and what business questions need to be answered. This leads to the next key skill. 2️⃣ Ability to Ask Questions To truly understand a request, you need to ask the right questions—digging into the why behind the why. Example: “Can you pull this data?” → Why? “Because XYZ person asked for it.” → Why? “Because we need to solve ABC business problem.” Getting to the real reason behind a request is critical before diving into data cleaning or analysis. Spoliers: It’s not always this simple! 3️⃣ Ability to Say No Prioritization is a learned skill. If you say yes to every request, your role can quickly shift from being strategic to just taking orders. Knowing when to say no (or push back) ensures that your work aligns with business goals. Again, asking the right questions and understanding the why helps you make this decision effectively. And if you are new and unsure what to prioritize, lean on your manager for support. • Anything else you’d like to add? 🤔 Do you remember the first time you said NO to a request? Ps. I have been reflecting a bit more on communication skills so may write more on that. • #dataanalytics #peopleanalytics #dataskills #peopleskills #communicationskills

Data skills aren't about knowing everything. They're about mastering what truly matters. 12 indispensable data analyst skills (and books to learn them): In a world drowning in data, analysts who master these core skills become irreplaceable. Here are 12 essential skills that separate good analysts from great ones 👇 1. SQL mastery beyond basics  ↳ "SQL for Data Analytics" by Upom Malik - learn how to write queries that extract exactly what you need 2. Statistical thinking, not just calculations  ↳ "Practical Statistics for Data Scientists" by Peter Bruce/Andrew Bruce - understand why stats matter, not just how to run them 3. Data visualization that tells stories  ↳ "Storytelling with Data" by Cole Knaflic - transform numbers into narratives that drive decisions 4. Python automation for repetitive tasks  ↳ "Automate the Boring Stuff with Python" by Al Sweigart - free yourself from manual data processing 5. A/B testing beyond basic comparison  ↳ "Trustworthy Online Controlled Experiments" by Ron Kohavi - design experiments that reveal actual causality 6. Ethical data handling as standard practice  ↳ "Weapons of Math Destruction" by Cathy O'Neil - ensure your analysis doesn't reinforce harmful biases 7. Business domain expertise, not just technical skills  ↳ "Data Science for Business" by Foster Provost/Tom Fawcett - connect your analysis to actual business outcomes 8. Dashboard design that drives action  ↳ "Information Dashboard Design" by Stephen Few - create visuals that prompt decisions, not just admiration 9. Personal productivity that creates impact ↳ "Getting Things Done" by David Allen - organize your work to become effective with a purpose 10. Version control for data work  ↳ "Git Pocket Guide" by Richard Silverman - track changes and collaborate without chaos 11. Effective communication of complex findings  ↳ "Say It With Charts" by Gene Zelazny - translate technical insights for non-technical stakeholders 12. Data cleaning as a strategic process  ↳ "Bad Data Handbook" by Q. McCallum - master the skill that consumes 80% of analysis time Value doesn't come from knowing the most tools. It comes from applying the right ones with expertise. Which skill will you master first? ♻️ Repost to help fellow data professionals grow 🔔 Follow Don Collins for insights on becoming an indispensable data professional

🎯 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 — 𝗥𝗼𝗹𝗲 𝗯𝘆 𝗥𝗼𝗹𝗲 𝗕𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 Ever wondered what skills truly matter for each role in the data world? Here's a no-fluff, clear breakdown so you can align your learning with the job you’re aiming for 👇 🔍 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 Key Skills ✅ Data cleaning & wrangling (Excel, Python + Pandas) ✅ SQL — writing efficient, optimized queries ✅ Data Visualization (Power BI, Tableau, Looker) ✅ Reporting that turns raw numbers into actionable insights 🎯 Focus: Business intelligence, KPI tracking, decision support 🧠 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 Key Skills ✅ Advanced Python mastery ✅ ML frameworks: scikit-learn, TensorFlow, PyTorch ✅ Model training, hyperparameter tuning, performance optimization ✅ Deployment using Flask, FastAPI, or cloud tools 🎯 Focus: Building and scaling ML models into production-ready systems 📊 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 Key Skills ✅ Statistical analysis & hypothesis testing ✅ EDA with NumPy, Pandas, Matplotlib, Seaborn ✅ ML: Feature engineering, ensemble methods, evaluation ✅ NLP: NLTK, spaCy, Transformers ✅ Big Data tools: Spark, Hadoop, SQL 🎯 Focus: Deriving insights, crafting predictive models, guiding strategy 🛠️ 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 Key Skills ✅ Building scalable ETL pipelines ✅ Mastery of SQL, PostgreSQL, MongoDB, Cassandra ✅ Frameworks: Apache Spark, Kafka, Hadoop ✅ Cloud: AWS Redshift, BigQuery, Azure Data Factory ✅ Workflow orchestration: Apache Airflow 🎯 Focus: Robust, reliable, and scalable data infrastructure 🚀𝗠𝗟𝗢𝗽𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 Key Skills ✅ CI/CD for ML workflows ✅ Docker + Kubernetes for containerization ✅ Monitoring: Prometheus, Grafana, model drift tools ✅ Cloud ML platforms: SageMaker, GCP AI Platform, Azure ML ✅ Infrastructure as Code: Terraform, Ansible 🎯 Focus: Seamless transition from model development to deployment 💡 𝗪𝗵𝘆 𝗥𝗼𝗹𝗲 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Not every data job is created equal. Different roles → different skills → different tools. The best way to grow? Master tools that fit your dream role, and pair them with strong problem-solving abilities. 𝗕𝗼𝗻𝘂𝘀 𝗧𝗶𝗽: Want to boost your skills for a Data science role? Focus on key areas like machine learning, data science, SQL, and Python. For practical, up-to-date learning, I recommend exploring 𝗧𝗲𝗰𝗵𝘃𝗶𝗱𝘃𝗮𝗻. 🔗 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗵𝗲𝗿𝗲:-https://lnkd.in/d46Eqtux Good luck on your data science journey! 🚀 — If you found this helpful, drop a like or share it with someone who’s building their data career! 💬 Let’s keep learning, growing, and building smarter solutions.