Data Analysis Skills Training

A “data strategy” that starts with picking tools... ... is like a fitness plan that starts with buying a smartwatch. Sure, the gear is shiny. But you haven’t broken a sweat yet. Here’s what I keep seeing: → “Our data strategy is Databricks + dbt + Power BI” → “We’re migrating to XYZ Lakehouse, that’s our strategy” → “We hired a Staff Data Engineer, LET's GO! 🤘” No. That’s not strategy. That’s a shopping list. Strategy is: → What are the business goals? → What decisions do we want to improve? → What problems do people actually have? → How do we distribute data products to stakeholders? → What systems to we put in place to create value? → What outcomes (not outputs) will we deliver? → How do we hire, grow and retain talent? Only then should we ask: What’s the simplest stack that gets us there? Not the most modern. Not the one that makes your team feel smart. The one that lets your org move fast and focused. Because a $200K tool won’t fix the fact that your marketing team doesn’t trust your data. And a new data observability platform won’t save you if no one knows what actions to take after seeing the dashboard. Want to build a data strategy that creates real business impact? 👉 Join 3,000+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era: https://lnkd.in/ghg5-5U7 ♻️ Repost if your “data strategy” once started with a vendor pitch deck

Data Quality is the foundation to build a robust data strategy over Data Quantity. Robust data quality practices helps the entire analytics house from collapsing when business stakeholders need trust worthy insights Data Quantity:- ➖Data Quantity is intrinsic and keeps growing as the business grows. ➖It's always quantifiable in Kbs, Mbs, Gbs, Tbs, Pbs and so on. ➖It's even scaling more with use every second, but can be expensive. Data Quality:- ➖Data can be small in size but should be quality data that can help us get some insights. ➖Data quality assesses certain standards of data and surpasses a lot of data measures. ➖It helps to gather quality input to various models and solutions. 🔍Exploring Data Quality Frameworks is always helpful but leveraging the data quality fundamentals is about smart, strategic checks that catch 90% of issues before they become headaches: 1. Table Constraints Are Your First Defense:- Think of these like security guards for your data. Enforce uniqueness, block nulls, prevent garbage in, garbage out. 2. Business Context Is King:- Data doesn't speak for itself. Talk to domain experts. Understanding business logic is more powerful than any algorithm. 3. Schema Integrity = Data Health:- Your schema is like the DNA of your data pipeline. One mutation can break everything. Monitor it religiously. 4. Anomaly Detection: Your Early Warning System:- Unexpected metric shifts? That's not a bug, that's a feature waiting to be investigated. Standard deviations are your best friend. 5. Distribution Matters:- Consistent row counts and segment sizes aren't boring—they're beautiful. Sudden changes scream "investigate me!" 6. Reconciliation: No Data Left Behind:- Every row counts. Ensure what goes in comes out transformed, not lost. 7. Audit Logs: Your Data's Biography:- Transparency isn't just a buzzword. Track every transformation, every step. 💡 Pro Tip: Start small. Master business checks first. Scale up strategically. If you want to scale the aspects of data quality, leverage these "Top 10 Data Quality Tools" crafted by Deepak Bhardwaj. Are you too obsessed with clean data? Drop a comment, and share your go-to quality check or tool you prefer using! #Data #Engineering #DataQuality #dataanalytics #bigdata

Many teams overlook critical data issues and, in turn, waste precious time tweaking hyper-parameters and adjusting model architectures that don't address the root cause. Hidden problems within datasets are often the silent saboteurs, undermining model performance. To counter these inefficiencies, a systematic data-centric approach is needed. By systematically identifying quality issues, you can shift from guessing what's wrong with your data to taking informed, strategic actions. Creating a continuous feedback loop between your dataset and your model performance allows you to spend more time analyzing your data. This proactive approach helps detect and correct problems before they escalate into significant model failures. Here's a comprehensive four-step data quality feedback loop that you can adopt: Step One: Understand Your Model's Struggles Start by identifying where your model encounters challenges. Focus on hard samples in your dataset that consistently lead to errors. Step Two: Interpret Evaluation Results Analyze your evaluation results to discover patterns in errors and weaknesses in model performance. This step is vital for understanding where model improvement is most needed. Step Three: Identify Data Quality Issues Examine your data closely for quality issues such as labeling errors, class imbalances, and other biases influencing model performance. Step Four: Enhance Your Dataset Based on the insights gained from your exploration, begin cleaning, correcting, and enhancing your dataset. This improvement process is crucial for refining your model's accuracy and reliability. Further Learning: Dive Deeper into Data-Centric AI For those eager to delve deeper into this systematic approach, my Coursera course offers an opportunity to get hands-on with data-centric visual AI. You can audit the course for free and learn my process for building and curating better datasets. There's a link in the comments below—check it out and start transforming your data evaluation and improvement processes today. By adopting these steps and focusing on data quality, you can unlock your models' full potential and ensure they perform at their best. Remember, your model's power rests not just in its architecture but also in the quality of the data it learns from. #data #deeplearning #computervision #artificialintelligence

I tried 50+ data analyst courses – Here are my top 3 After burning through countless tutorials and bootcamps, only three actually moved the needle on my career. 𝟏. 𝐀𝐬𝐬𝐨𝐜𝐢𝐚𝐭𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐢𝐧 𝐒𝐐𝐋 𝐖𝐡𝐲 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: SQL is non-negotiable (Never ignore SQL, or you’ll be ignored). This track doesn't just teach syntax; it shows you how to think in queries. Real datasets, practical scenarios. You'll be writing CTEs and window functions like a pro. 𝐋𝐢𝐧𝐤: https://lnkd.in/d_V7d7v5 𝟐. 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 The game-changer: Goes beyond pandas basics. You'll build dashboards, automate reports, and actually understand statistical analysis. Plus, the certification carries weight with recruiters. 𝐋𝐢𝐧𝐤: https://lnkd.in/dBBU9Q6D 𝟑. 𝐆𝐨𝐨𝐠𝐥𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐞 (𝐅𝐫𝐞𝐞 𝐭𝐨 𝐚𝐮𝐝𝐢𝐭) What sets it apart: Built by Google's own data team. You can audit the course for free on Coursera.  𝐋𝐢𝐧𝐤: https://lnkd.in/dxfmmwZS 𝐖𝐡𝐚𝐭 𝐦𝐚𝐝𝐞 𝐭𝐡𝐞𝐬𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭? • Hands-on from day one (no 10-hour theory marathons) • Industry-relevant projects you can actually showcase • Clear learning paths, not random skill collecting • Active communities where you get real answers 𝐌𝐲 𝐚𝐝𝐯𝐢𝐜𝐞? Start with ONE course and commit to finishing it. Too many people collect courses like trophies but never complete them. Pick based on your current situation: • Need SQL fast for a job interview? → DataCamp SQL track • Want to automate boring Excel tasks? → DataCamp Python track  The best part? You can start any of them today and have portfolio-worthy projects within 30 days. 𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫: The course that gets you hired isn't the most expensive one, it's the one you actually finish. Portfolio projects will get you hired! ♻️ Save this for your learning roadmap! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 16,000+ readers here → https://lnkd.in/dUfe4Ac6

SQL, Excel, Python… All are useless if you don’t follow the analytics framework. Everyone thinks data analytics = tools. But tools are only 10%. The real work is a FRAMEWORK. Here’s the 5–step flow you should know 👇 1. Define the Problem Don’t jump into Excel or SQL yet. Ask: What’s the business question? 2. Collect Data From databases, APIs, surveys, or logs. Garbage in = garbage out. 3. Clean & Prepare Fix missing values. Remove duplicates. Make the dataset analysis-ready. 4. Analyze & Explore Use statistics, SQL, Excel, or Python. Look for trends, patterns, and insights. 5. Communicate Findings Dashboards, reports, or storytelling. Because insights are useless if no one understands them. This is the core framework. Whether you’re in Excel, Power BI, or Python, the steps remain the same. Master this flow → you can adapt to any tool. P.S. I still don’t get why so many beginners depend only on tools… 🤔 Do you want me to show how to complete a full analytics project from start to finish?

𝗜𝘀 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘀𝗲𝗰𝗿𝗲𝘁𝗹𝘆 𝘀𝗮𝗯𝗼𝘁𝗮𝗴𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗥𝗢𝗜? (𝗧𝗵𝗿𝗲𝗲 𝗵𝗮𝗿𝗱-𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝗹𝗲𝘀𝘀𝗼𝗻𝘀 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝘄𝗶𝘁𝗵 𝗔𝗜) A data strategy without alignment is just a budget drain waiting to happen. In my work with data-driven companies over the years, I’ve experienced firsthand pretty tough lessons on scaling with data and AI. Today I want to share three of these hard-earned insights with you, along with one actionable strategy you can use to accelerate your growth trajectory. 𝗟𝗲𝘀𝘀𝗼𝗻 #𝟭: 𝗗𝗮𝘁𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗶𝘀 𝗿𝗶𝘀𝗸𝘆 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 I’ve seen companies invest heavily into data infrastructure only to realize that their KPIs and data initiatives weren’t aligned with core growth objectives. To make sure your data strategy is a growth driver, you have to map back every data project to a specific business outcome and assign ownership across departments. This not only maximizes ROI, but it also builds essential cross-functional accountability. 𝗟𝗲𝘀𝘀𝗼𝗻 #𝟮: 𝗬𝗼𝘂𝗿 “𝘄𝗶𝗻𝗻𝗶𝗻𝗴 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲” 𝘄𝗶𝗹𝗹 𝗺𝗮𝗸𝗲 𝗼𝗿 𝗯𝗿𝗲𝗮𝗸 𝘆𝗼𝘂𝗿 𝗴𝗿𝗼𝘄𝘁𝗵 𝗴𝗼𝗮𝗹𝘀 In many cases, leaders become paralyzed by the overwhelming number of options that are available when looking to move forward on an AI implementation. The most effective approach: Vet potential use cases and select the one with the highest ROI potential — what I call your "Winning Use Case." It’s focus like this that truly empowers leaders to allocate resources wisely and drive measurable results. 𝗟𝗲𝘀𝘀𝗼𝗻 #𝟯: 𝗟𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗱𝗲𝗺𝗮𝗻𝗱𝘀 𝗲𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 In today's regulatory climate, overlooking ethical AI isn’t just risky — it’s downright unsustainable. As early as possible, set benchmarks for data privacy and bias checks. This commitment will pay off in both risk mitigation and brand equity over time. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗔𝗰𝘁𝗶𝗼𝗻: 𝗖𝗼𝗻𝗱𝘂𝗰𝘁 𝗮 “𝗴𝗿𝗼𝘄𝘁𝗵 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗮𝘂𝗱𝗶𝘁” 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀 If you’re investing in AI or planning to do so, try this: audit your AI initiatives by evaluating their alignment with three criteria — scalability, strategic impact, and ethical compliance. Prioritize projects that meet all three and realign or rethink those that don’t. This approach ensures that your data- and AI- investments contribute directly towards reaching your growth targets, without unintended consequences. In 𝘛𝘩𝘦 𝘋𝘢𝘵𝘢 & 𝘈𝘐 𝘐𝘮𝘱𝘦𝘳𝘢𝘵𝘪𝘷𝘦, I share every nook and cranny of my signature STAR Framework in order to help you identify high-impact AI initiatives, align data strategies with growth, and maximize your ROI. Pre-order your copy now to get the full blueprint: https://lnkd.in/gMGraK32 #datastrategy #growth #AI #ROI #data #kpis #alignment #business

Data-quality “dimensions” like completeness, accuracy, timeliness, consistency, etc. come from management theory. They’re useful for audits and KPIs, but they don’t help much when you sit down to implement tests in a pipeline. Why? Engineers and analysts usually write checks only after a concrete failure: • Broken joins → foreign-key mismatch, unexpected NULLs  • Wrong revenue → aggregation logic changed, currency drift  • Missing records → late-arriving files, partition gaps  • Silent drops → schema evolution not propagated Notice none of those map cleanly to a single “dimension.” Each failure touches several at once. Instead we can try classifying checks by the failure they prevent and the action they trigger: My friend and mentor Malcolm created this overview of high-level check types that can be used to build test cases, but more importantly, to classify data quality issues with. How are you using Data Quality Dimensions in practice?

Noisy data makes trends hard to identify. Being unable to see trends causes two major issues: 1. Misguided decisions (not seeing the forest through the trees) 2. Wasted Resources (variation is normal, not every change needs an answer) So, how do we shed light on patterns in noisy data? Stock prices are a great example of noisy data. Stocks generate new data every nanosecond, so, analysts use two key methods to draw out their trends over the course of days, weeks, months, & years. 1. Smoothing This is easily accomplished by placing a moving average on top of the time series data, creating a smooth line that considers a larger set of data to off set near term price fluctuations. 2. Changing Time Frequency Looking at data by the nanosecond is not feasible for a human, so we naturally compress time to hours, days, week, etc. so that we can see how the data performed in those time period. Stock prices typically use the candlestick charts below that show min, max, and the open/close prices. Smoothing and zooming out in time are some of the best ways to handle noisy data, especially in time series data. Below is a basic example you can play with in Python to get a better sense of how these work 👇 -------- import pandas as pd import numpy as np import matplotlib.pyplot as plt # Generate a hypothetical stock price dataset for 6 months (approximately 180 days) np.random.seed(0) dates = pd.date_range(start="2023-01-01", periods=180) prices = np.random.normal(0.5, 0.75, size=180).cumsum() + 100 # Create a DataFrame stock_data = pd.DataFrame(data={'Price': prices}, index=dates) # Apply a 30-day moving average for smoothing stock_data['30D_MA'] = stock_data['Price'].rolling(window=30).mean() # Reduce the time frequency to weekly, taking the last price of the week # Can change to 'M', 'Q', or 'D' for differing time frequencies weekly_data = stock_data.resample('W').last() # Plotting plt.figure(figsize=(14, 7)) plt.plot(stock_data['Price'], label='Daily Prices', alpha=0.5) plt.plot(stock_data['30D_MA'], label='30-Day Moving Average', linewidth=2) plt.plot(weekly_data.index, weekly_data['Price'], label='Weekly Prices', marker='o', linestyle='-', linewidth=2) plt.title('Stock Price with Smoothing and Time Frequency Reduction') plt.xlabel('Date') plt.ylabel('Price') plt.legend() plt.grid(True) plt.show() ------ Hi I'm Joe 👋 I work with young medtech, healthcare, & life science companies to help them understand their data and win in the market. #dataanalytics #dataanalyst #datavisualization #powerbi #tableau

Learning the tools is the 𝘦𝘢𝘴𝘺 part. Learning them in the 𝗿𝗶𝗴𝗵𝘁 𝗼𝗿𝗱𝗲𝗿? That’s where most aspiring analysts fall off. Here’s the progression I always recommend:  1. 𝗘𝘅𝗰𝗲𝗹: Learn to clean, transform, and analyze data fast.  2. 𝗦𝗤𝗟: Learn to query data like a pro.  3. 𝗕𝗜 𝘁𝗼𝗼𝗹𝘀 (𝗧𝗮𝗯𝗹𝗲𝗮𝘂 / 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜): Learn to visualize insights clearly.  4. 𝗣𝘆𝘁𝗵𝗼𝗻: Learn to automate, model, and scale. Each step prepares you for the next one. When you master Pivot Tables in Excel... ↳ You'll better understand GROUP BY in SQL. When you visualize in Tableau... ↳ You’ll appreciate how queries power dashboards. Python is amazing. But it’s the 𝘤𝘩𝘦𝘳𝘳𝘺 𝘰𝘯 𝘵𝘰𝘱, not the base layer. Master the first three. Then go Python. If you're just starting → copy this progression. It’ll save you months of confusion. Repost to help someone learning data right now ♻️ 

Estimating timelines and workloads is a challenging task for data analysts. Here's a structured approach to bring clarity to the unknown: 1. 𝗕𝗿𝗲𝗮𝗸 𝗜𝘁 𝗗𝗼𝘄𝗻: Start by breaking the project into smaller, manageable tasks. Think about data collection, cleaning, analysis and visualization. It's easier to estimate pieces than the whole puzzle.     2. 𝗣𝗮𝘀𝘁 𝗮𝘀 𝗮 𝗚𝘂𝗶𝗱𝗲: Look back at similar projects you've tackled. Use these as benchmarks. No exact matches available? Break down the differences and adjust your estimates accordingly.     3. 𝗕𝘂𝗳𝗳𝗲𝗿 𝗳𝗼𝗿 𝗨𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆: Always include a buffer time for unforeseen challenges (because they will come). A good rule of thumb? Add 20% more time to your initial estimate.     4. 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: Present your initial structure and timeline to the team or stakeholders early on. Their insights might highlight areas you've overlooked or suggest shortcuts you hadn’t considered.     5. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗔𝗱𝗷𝘂𝘀𝘁: As the project progresses, keep an eye on timelines versus actual progress. Be ready to adjust your estimates and communicate changes proactively.     6. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀: After project completion, reflect on the accuracy of your estimates. What went as planned? What didn’t? Documenting these learnings will refine your future estimates. Estimating is as much an art as it is a science. It requires understanding the scope, drawing on experience and anticipating the unexpected. Embrace this process and with each project, your forecasting will get better. How do you forecast the timelines for your data projects? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #businessanalytics #projectmanagement #projecttimeline #estimation