Data Analysis Skills Training

Real-time data analytics is transforming businesses across industries. From predicting equipment failures in manufacturing to detecting fraud in financial transactions, the ability to analyze data as it's generated is opening new frontiers of efficiency and innovation. But how exactly does a real-time analytics system work? Let's break down a typical architecture: 1. Data Sources: Everything starts with data. This could be from sensors, user interactions on websites, financial transactions, or any other real-time source. 2. Streaming: As data flows in, it's immediately captured by streaming platforms like Apache Kafka or Amazon Kinesis. Think of these as high-speed conveyor belts for data. 3. Processing: The streaming data is then analyzed on-the-fly by real-time processing engines such as Apache Flink or Spark Streaming. These can detect patterns, anomalies, or trigger alerts within milliseconds. 4. Storage: While some data is processed immediately, it's also stored for later analysis. Data lakes (like Hadoop) store raw data, while data warehouses (like Snowflake) store processed, queryable data. 5. Analytics & ML: Here's where the magic happens. Advanced analytics tools and machine learning models extract insights and make predictions based on both real-time and historical data. 6. Visualization: Finally, the insights are presented in real-time dashboards (using tools like Grafana or Tableau), allowing decision-makers to see what's happening right now. This architecture balances real-time processing capabilities with batch processing functionalities, enabling both immediate operational intelligence and strategic analytical insights. The design accommodates scalability, fault-tolerance, and low-latency processing - crucial factors in today's data-intensive environments. I'm interested in hearing about your experiences with similar architectures. What challenges have you encountered in implementing real-time analytics at scale?

Time Series Residuals Analysis 101 👇🏼 When evaluating a time series forecasting model, residual analysis is one of the most important steps. Residuals—the differences between the actual values and the model’s predictions—help us understand whether the model has captured the underlying patterns or if important structure remains unexplained and what features are missing. In a good model, the residuals should be white noise (no patterns left) and normally distributed (required for model inference and reliable prediction intervals) 🎯 Here’s what each diagnostic plot helps us check: 🔹 Actual vs. Fitted Plot This plot shows how closely the model’s fitted values track the actual observations. It helps you visually spot systematic under- or over-prediction, missed trends, or structural breaks that the model failed to capture. 🔹 Residuals Plot (over time) Plotting residuals across time shows whether they fluctuate randomly around zero. Patterns such as trends, clusters, or seasonal waves indicate that the model has not fully captured the time-dependent structure. 🔹 Residuals ACF (Autocorrelation Function) The ACF plot checks whether residuals are correlated with their own past values. Significant autocorrelation at any lag suggests the model left some temporal structure unmodeled and could be improved. 🔹 Q–Q Plot (Residual Normality Check) The Q–Q plot compares the distribution of residuals to a theoretical normal distribution. Deviations from the diagonal line signal non-normality, which can affect inference and the validity of prediction intervals. 🔹 Residual Density Plot This shows the overall distribution of residuals. A symmetric, bell-shaped curve centered at zero indicates the model errors behave as expected; skewness or heavy tails may highlight model misspecification or outliers. Pro tips: 🔹 Overlay the residual standard deviation on the Actual vs. Fitted plot. I use a range of ±2σ to ±3σ (orange) and bands above ±3σ to immediately spot points where the model’s errors are unusually large, making it easier to diagnose poor fit or outliers. 🔹 Highlight seasonal lags in the residuals ACF. Marking seasonal lag positions (e.g., lag 7, 12, 24, 168—depending on your frequency) in a different color makes it simple to see whether any seasonal structure remains in the residuals, signaling that the model may not have fully captured seasonality. #timeseries #forecasting #datascience

If you are an aspiring data scientist, you need to deeply understand how time series analysis works. Time series analysis is one of those core foundations that quietly powers a huge number of real business decisions. Anywhere data changes over time, this shows up. At its core, time series analysis is about modeling patterns across time like trends, seasonality, cycles, and noise so we can forecast what comes next and understand why things move the way they do. You see it everywhere in practice: ✦ Demand forecasting in retail and supply chain planning ✦ Revenue, churn, and growth forecasting in SaaS businesses ✦ Anomaly detection in finance and fraud monitoring ✦ Capacity planning and reliability metrics in infrastructure systems ✦ Forecasting user engagement, traffic, and conversions in product analytics What trips people up is that time series is not just “run a model and plot a line.” There are a lot of nuances once you start building real systems: ✦ Stationarity vs non-stationarity ✦ Seasonality and regime shifts ✦ Choosing between classical, statistical, and ML-based models ✦ Picking the right evaluation metrics for forecasts ✦ Understanding when forecasts break and why That’s exactly why I put together a time series cheat sheet. It’s designed to give you a quick, structured overview of: ✦ Common models and when to use them ✦ Key assumptions to watch out for ✦ Metrics interviewers actually expect you to know ✦ The big picture of how time series is applied in real business settings Super useful both for interview prep and as a fast refresher when you’re working on real problems. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Behind every great insight is a solid statistical foundation. Here are the 4 methods every data analyst must master: 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Data visualization is just the tip of the iceberg. The real power comes from understanding the statistical methods that reveal relationships, patterns, and predictive insights. 𝐓𝐡𝐞𝐬𝐞 4 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐦𝐞𝐭𝐡𝐨𝐝𝐬 𝐩𝐨𝐰𝐞𝐫 𝐞𝐯𝐞𝐫𝐲 𝐝𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧: 1. 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 → Predict outcomes and identify what drives them → "How does marketing spend impact revenue?" → Master: R² for model fit, RMSE for prediction accuracy → Pro tip: Always check residuals - they tell the real story 2. 𝐇𝐲𝐩𝐨𝐭𝐡𝐞𝐬𝐢𝐬 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 → Make confident, evidence-based decisions → "Is this A/B test result actually significant?" → Master: t-tests for comparing means, ANOVA for multiple groups → Remember: Statistical significance ≠ business significance 3. 𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 → Measure relationships between variables → "How strongly do these factors move together?" → Master: Pearson for linear, Spearman for non-linear → Warning: Correlation ≠ causation (but you knew that) 4. 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 → Uncover trends, cycles, and seasonality → "What will demand look like next quarter?" → Master: ARIMA for trends, Exponential Smoothing for patterns → Always: Decompose first to understand components 𝐖𝐡𝐲 𝐦𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐞𝐬𝐞 𝐧𝐨𝐰: ↳ Every dashboard needs statistical validation ↳ Every recommendation requires evidence ↳ Every model must be interpretable ↳ Master these = become indispensable The best part? Once you think statistically, data tells stories you never noticed before. Master the stats. Master the insights. Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp ♻️ Save this for your next analysis 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 18,000+ readers here → https://lnkd.in/dUfe4Ac6

A poor demand forecast destroys profits and cash. This infographic shows 7 forecasting techniques, pros, cons, & when to use: 1️⃣ Moving Average ↳ Averages historical demand over a specified period to smooth out trends ↳ Pros: simple to calculate and understand  ↳ Cons: lag effect; may not respond well to rapid changes ↳ When: short-term forecasting where trends are relatively stable 2️⃣ Exponential Smoothing ↳ Weights recent demand more heavily than older data ↳ Pros: responds faster to recent changes; easy to implement ↳ Cons: requires selection of a smoothing constant ↳ When: when recent data is more relevant than older data 3️⃣ Triple Exponential Smoothing  ↳ Adds components for trend & seasonality ↳ Pros: handles data with both trend and seasonal patterns ↳ Cons: requires careful parameter tuning ↳ When: when data has both trend and seasonal variations 4️⃣ Linear Regression ↳ Models the relationship between dependent and independent variables ↳ Pros: provides a clear mathematical relationship ↳ Cons: assumes a linear relationship ↳ When: when the relationship between variables is linear 5️⃣ ARIMA ↳ Combines autoregression, differencing, and moving averages ↳ Pros: versatile; handles a variety of time series data patterns ↳ Cons: complex; requires parameter tuning and expertise ↳ When: when data exhibits autocorrelation and non-stationarity 6️⃣ Delphi Method ↳ Expert consensus is gathered and refined through multiple rounds ↳ Pros: leverages expert knowledge; useful for long-term forecasting ↳ Cons: time-consuming; subjective and may introduce bias ↳ When: historical data is limited or unavailable, low predictability 7️⃣ Neural Networks ↳ Uses AI to model complex relationships in data ↳ Pros: can capture nonlinear relationships; adaptive and flexible ↳ Cons: requires large data sets; can be a "black box" with less interpretability ↳ When: for complex, non-linear data patterns and large data sets Any others to add?

📊 𝐇𝐨𝐰 𝐦𝐲 𝐬𝐭𝐮𝐝𝐞𝐧𝐭𝐬 𝐥𝐞𝐚𝐫𝐧𝐞𝐝 𝐭𝐡𝐚𝐭 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐢𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐣𝐮𝐬𝐭 𝐦𝐚𝐤𝐢𝐧𝐠 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬. A lot happened before and after the visual you see here. Let me walk you through how a real 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐟𝐥𝐨𝐰 works 👇 1️⃣ 𝐓𝐡𝐞 𝐀𝐬𝐤 Every project begins with a 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐭𝐨 𝐛𝐞 𝐬𝐨𝐥𝐯𝐞𝐝. You must understand stakeholder needs, expected deliverables, and the timeline. Skip this and the rest loses value. 2️⃣ 𝐃𝐚𝐭𝐚 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐢𝐨𝐧 & 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 We explored the dataset. In the real world you may pull data from 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 — databases, files, APIs. We made sure the data was 𝐜𝐥𝐞𝐚𝐧, 𝐜𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞, 𝐚𝐧𝐝 𝐫𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐯𝐞. 𝐏𝐨𝐰𝐞𝐫 𝐐𝐮𝐞𝐫𝐲 handled the heavy lifting. 3️⃣ 𝐄𝐓𝐋 & 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 After ETL we normalized the tables, created relationships, and set keys. We built the model in Excel with 𝐏𝐨𝐰𝐞𝐫 𝐏𝐢𝐯𝐨𝐭, added 𝐝𝐚𝐭𝐞 𝐭𝐚𝐛𝐥𝐞𝐬, and wrote calculated columns with 𝐃𝐀𝐗. 4️⃣ 𝐃𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐊𝐏𝐈𝐬 & 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 We identified the 𝐤𝐞𝐲 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 to track. Using 𝐃𝐀𝐗 we answered the business questions and turned raw numbers into actionable insights. 5️⃣ 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 𝐃𝐞𝐬𝐢𝐠𝐧 We sketched 𝐰𝐢𝐫𝐞𝐟𝐫𝐚𝐦𝐞𝐬 first, then built visuals in Excel that communicate clearly to stakeholders. 6️⃣ 𝐃𝐫𝐢𝐯𝐢𝐧𝐠 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 Insights are only useful if they lead to action. We documented findings in a 𝐫𝐞𝐩𝐨𝐫𝐭 and supported them with a 𝐏𝐨𝐰𝐞𝐫𝐏𝐨𝐢𝐧𝐭 for decision-makers. ✨ 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 → 𝐃𝐚𝐭𝐚 → 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 → 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. If this helped, share it with someone starting their data journey and hit 𝐟𝐨𝐥𝐥𝐨𝐰 for more practical tips. Which stage do you find most challenging in your projects? #Excel #ExcelTricks #DataFam #DataAnalysis

📉 “Time Series Concepts Every Analyst Must Know” In quantitative finance and analytics, one of the most valuable yet complex tasks is 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲. Whether it's forecasting returns, or modelling interest rates, you’re not just working with data, You’re working with data through time. That’s where time series analysis comes in. 𝗕𝘂𝘁 𝗵𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗰𝗮𝘁𝗰𝗵: But time series data brings unique problems: • Autocorrelation • Non-stationarity • Lag effects So here’s a 𝗾𝘂𝗶𝗰𝗸 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗼𝗳 𝘁𝗶𝗺𝗲 𝘀𝗲𝗿𝗶𝗲𝘀 concepts/models every analyst should know, whether you're just starting or brushing up. 🔑 𝗖𝗼𝗿𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀: 𝟭. 𝗦𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗿𝗶𝘁𝘆 A stationary series has constant mean and variance over time. Required for most traditional models. 📌 Non-stationary data? Use differencing, log transforms, or seasonal adjustment. 𝟮. 𝗠𝗼𝘃𝗶𝗻𝗴 𝗔𝘃𝗲𝗿𝗮𝗴𝗲 (𝗠𝗔) MA(q): current value = weighted sum of past q errors It models depend on past error terms. Useful for smoothing and modelling short-term shocks. 𝟯. 𝗔𝘂𝘁𝗼𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 (𝗔𝗥) AR(p): current value = weighted sum of p past values It models depend on past values. Common in asset return modeling and demand forecasting. 𝟰. 𝗔𝘂𝘁𝗼𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻 (𝗔𝗖𝗙) Tells you how strongly your current value is linked to past lags. 📌 Helps identify MA order. 𝟱. 𝗣𝗮𝗿𝘁𝗶𝗮𝗹 𝗔𝘂𝘁𝗼𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻 (𝗣𝗔𝗖𝗙) Captures the direct influence of lags, removing indirect effects. 📌 Helps identify AR order. 𝟲. 𝗔𝗥𝗠𝗔 (𝗽, 𝗾) Blends AR and MA components. Great for modelling stationary time series with short-term memory. 𝟳. 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗶𝗻𝗴 Take the difference between consecutive points to remove trend/seasonality and make the data stationary. 𝟴. 𝗔𝗥𝗜𝗠𝗔 (𝗽, 𝗱, 𝗾) Adds 'd' for differencing applied to non-stationary data. Widely used for real-world forecasting. 𝟵. 𝗦𝗔𝗥𝗜𝗠𝗔 / 𝗦𝗲𝗮𝘀𝗼𝗻𝗮𝗹 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 Extends ARIMA to model seasonality, such as monthly sales, etc. 🛠 𝗧𝗼𝗽 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝗳𝗼𝗿 TS 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: • statsmodels → Traditional models (ARIMA, SARIMA) • pmdarima → Auto ARIMA tuning • Prophet → Forecasts with holidays/seasonality • sktime → Unified ML + statistical TS toolkit • tsfresh → Feature extraction from TS 📚 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: • “Time Series Analysis” – James D. Hamilton • “Forecasting: Principles and Practice” – Hyndman & Athanasopoulos • “Practical Time Series Analysis” – Aileen Nielsen 💡 𝗧𝗟;𝗗𝗥: Time series isn’t just a data format, it’s a mindset. If you're in finance, economics, or quant, it’s non-negotiable. 🔔 Follow Puneet Khandelwal for more insights into the world of quants, ML, and finance. 👇 Which TS concept or library do you use most often? 🔁 Repost if this helped simplify time series for you. #TimeSeries #QuantFinance #Forecasting #DataScience #ARIMA #MachineLearning #Quant

Causal inference using real-world evidence (#RWE) is a methodology focused on establishing causal relationships between exposures or interventions and outcomes. It utilizes observational data from real-world settings like electronic health records, claims databases, and patient registries. RWE complements randomized controlled trials (RCTs) by assessing effectiveness across diverse populations and guiding healthcare decisions. The credibility of RWE for causal inference hinges on clear study design, appropriate real-world data (#RWD), effective communication, and robust statistical analysis. Regulatory efforts, such as the FDA’s Advancing Real-World Evidence Program, aim to enhance evidence generation by incorporating patient perspectives and promoting RWE alongside traditional research methods. Despite its valuable insights, RWE encounters limitations in determining causality due to potential biases. To mitigate these challenges, techniques like target trial emulation and causal frameworks are suggested. By integrating RWE with RCTs, a more holistic understanding of healthcare interventions and their real-world impacts can be attained. This integration facilitates the advancement of evidence-based healthcare practices, ensuring a comprehensive evaluation of healthcare strategies and outcomes.

NEW PUBLICATION - Results from our latest Real-World Evidence Pilot demonstrate how treatment response rate can be measured using real-world data. Data obtained from clinical practice, or real-world data (#RWD), can provide valuable insights about treatment outcomes - particularly for patient populations not fully represented in prior clinical studies or for exploring potential other uses for new medicines. However, use of RWD as a research tool can require different methods and study considerations. In our new study published in JCO CCI, we show that different sources of data can be used to implement a common approach and consistently evaluate response rates. This aligned method and reproducibility in results show that rwResponse can be a valuable metric to assess treatment effectiveness outside of traditional clinical trials. Full publication: https://lnkd.in/diTaFg2Z RWE project page: https://lnkd.in/eEaJqjH9 Receive regular updates: https://lnkd.in/dCvFvkeX Many thanks to our collaborators: American Society of Clinical Oncology (ASCO), ConcertAI, COTA, FDA, Flatiron Health, Friends of Cancer Research, Guardian Research Network, IQVIA, Memorial Sloan Kettering Cancer Center, Ontada, Syapse, Syneos Health, and Tempus AI #RWEFriends #cancerresearch

🔬 Invited to assess research novelty in Real-World Evidence — and one paper stood out Last week, I was invited by the University of Sussex Metascience Unit (UK Government) to contribute expert assessments to the Metascience Novelty Indicators Challenge. The task was refreshingly thoughtful. 📍 Instead of “rate this paper good/bad,” the survey asked reviewers to judge: • methodological originality • conceptual advancement • practical impact for the field • and whether ideas genuinely change practice A simple but mindful scale that separates true innovation from incremental noise. I reviewed five publications in Real-World Evidence (RWE), spanning ethics, transportability, AI imaging, and data science methods. One clearly stood above the rest: 📄 Data Science Methods for Real-World Evidence Generation in Real-World Data — Fang Liu, Annual Review of Biomedical Data Science ❓ Why? Because it doesn’t propose just another technique. It reframes the entire way we generate evidence from real-world data. 📍 Key messages from the paper: ✅ RWD are messy, heterogeneous, incomplete — traditional RCT-era methods are insufficient ✅ RWE requires an end-to-end pipeline, not isolated analytics ✅ Study design matters first (target trial emulation, pragmatic trials) ✅ Causal inference + ML must be combined, not confused ✅ Trustworthiness is non-negotiable: validity, uncertainty quantification, explainability, privacy, fairness ✅ Evidence must be regulatory-grade, not exploratory dashboards 🔊 In short: methodology + governance + ethics = credible RWE This resonates strongly with what we see daily across regulators, HTA bodies, and pharma teams. At Helios Academy Ltd – Where Science Meets Compassion, this is exactly the space we operate in: 🔺 Helping organisations move beyond “data access” toward decision-grade evidence that stands up to scrutiny. 🔺 Not more dashboards. 🔺 Better science. If you work in RWE, HTA, or evidence strategy, this paper is genuinely worth your time. Sometimes the most novel idea isn’t a new algorithm — it’s a better way to think. 🏷️ Keywords: #RealWorldEvidence #RWD #HealthData #CausalInference #HTA #EvidenceBasedMedicine #HeliosAcademy #Metascience ⚠️ Disclaimer: Views expressed here are my own. Helios Academy Ltd — “Where Science Meets Compassion” — is an independent educational initiative. This post does not represent the views of Astellas Pharma, my employer, and contains no confidential or company-related information.