Data Analysis Techniques That Drive Insights

Want to up your data analysis/science game? I will share one of my most powerful techniques in this post. Here’s the cool part. This technique is universal. I’ve used it to feed exploratory data analysis (EDA), market basket analysis, and machine learning algorithms. I’ve used it with small data, big data, and everything in between. Can you guess what it is? It’s a specific type of SQL query: SELECT <unique_id> -- Has something happened? ,MAX(CASE WHEN <some_logical_condition> THEN 1 ELSE 0 END) AS <indicator> -- Count how many times something happened ,SUM(CASE WHEN <some_logical_condition> THEN 1 ELSE 0 END) AS <count> -- Count how many times something happened within X number of days ,SUM(CASE WHEN <some_logical_condition>  AND DATEDIFF(DAY, <start_date>, <end_date>) <= <value> THEN 1 ELSE 0 END) AS <date_count> FROM <some_table>     <any_joins> WHERE <filter> GROUP BY <unique_id> I can’t tell you how often I’ve used some version of the above SQL to craft data that produced new business insights. Some real-world examples: 1 - Pull data into Microsoft Excel (e.g., via Power Query) to conduct EDA. 2 - Crafting binary indicators to use in market basket analysis. 3 - Building powerful features for machine learning models. Over the years, I’ve found SQL to be the most versatile and useful of all my data skills: A – Querying relational databases for “small” data. Make no mistake, “small” relational data is still king in many organizations. B – Querying “big data” stores like Spark and Hive. That said, the idea behind the SQL query is the real magic. Grab your tool of choice and start exploring your data: You can reproduce the SQL using dplyr or pandas? Awesome! You can reproduce the SQL using a drag-and-drop visual tool? Sweet! You can reproduce the SQL using M/DAX/VBA in Excel? Righteous! I’m betting you won’t be disappointed: BTW – I’ve consistently found that <date_count> features are the most useful for uncovering new business insights, especially with machine learning models. Stay healthy and happy data sleuthing! #datascience #machinelearning #analytics #businessanalytics #dataanalytics

A/B testing is a staple in the industry, often highlighted as the gold standard for experimentation. But how often do we talk about causal analysis, the broader and equally important field that underpins it? While it may be less commonly referenced, causal analysis is fundamental to answering deeper questions about cause-and-effect relationships in data. This introductory blog by a Microsoft data scientist provides a clear and approachable overview of causal analysis, breaking down its major components and their applications. Broadly, causal analysis can be categorized into two key areas:   -- Causal Discovery: This focuses on identifying the underlying causal structure from data. It answers questions like, "What factors influence an outcome, and how are they connected?" Algorithms like the Peter-Clark algorithm and Greedy Equivalence Search help uncover these relationships, often represented as causal graphs.   -- Causal Inference: This focuses on quantifying the effect of one variable on another. It answers questions like, "How much does X cause Y?" Techniques range from experimental approaches like A/B testing to observational methods like propensity score matching, instrumental variables, and difference-in-differences. Our commonly known A/B testing is a subset of causal inference and relies on controlled experiments to estimate effects. However, non-experimental approaches offer powerful alternatives, especially when experiments aren’t feasible. If you’re curious about expanding your understanding of causality and its practical applications, this blog is a great starting point to explore how causal analysis can elevate data-driven decision-making. #datascience #analytics #causal #discovery #inference #abtest  – – –  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/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gfxTjapV

Most businesses today are running on Simple Data Analytics (SDA). -Summing -Averaging -Multiplying -Basic reports It’s enough to track what’s happening. But is it enough to stay competitive? Maybe not. Because while SDA gives you a snapshot of the past, it doesn’t prepare you for the future. Enter Intelligent Data Analytics (IDA). IDA goes beyond basic number crunching. It transforms, standardizes, and enriches data with AI before analysis. That means: ✔ Extracting meaning from unstructured sources (like social media, emails, or customer reviews). ✔ Identifying hidden patterns using natural language processing and machine learning. ✔ Automating complex data processing to surface real insights. Why does this matter? Let’s say your company sees a 10% drop in customer retention. SDA tells you the retention rate is down. But why? With IDA, you can analyze customer call center transcripts, recent product reviews, customer satisfaction surveys, and buying behavior to tell you: → Are customers leaving due to price sensitivity? → Is a competitor offering better service? → Are product reviews highlighting recurring issues? SDA can tell you what happened, but IDA can tell you what actually transpired and provide insights into what to do next. Businesses that stop at simple data analytics are leaving valuable insights on the table. In our AI-driven world, data isn’t just about reporting—it’s the key to smarter, more strategic decision-making. Are you still relying on basic reports, or have you made the shift to intelligent data analytics?

New Video: How to Apply Key Inferential Statistics Methods In the final part of my Inferential Statistics series, I break down four essential methods every analyst should master: - Chi-Squared Tests – for analyzing categorical data relationships - T-Tests – for comparing means between two groups - ANOVA – for comparing multiple groups - Tukey Tests – for post-hoc comparisons after ANOVA Whether you’re working with marketing data, research studies, or product performance metrics, these methods are foundational for uncovering meaningful insights and making data-driven decisions. What You’ll Learn: • When and how to use each test • Step-by-step demos in Excel and Google Sheets • How to turn data into actionable insights You'll find the full video here: https://bit.ly/3DQsBVe Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Exploring the Foundations of Machine Learning: Key Algorithms for Data-Driven Decision Making As we navigate the complex landscape of data science and artificial intelligence, it's crucial to understand the core algorithms that power modern machine learning applications. Let's examine ten fundamental techniques that form the backbone of data-driven insights: ◈ Random Forest: An ensemble method that leverages the wisdom of multiple decision trees, offering robust performance in both classification and regression tasks. Its strength lies in mitigating overfitting through collective decision-making. ◈ Naive Bayes: Rooted in probabilistic theory, this algorithm excels in text classification and spam filtering. Its efficiency stems from the assumption of feature independence, allowing for rapid training and deployment. ◈ Decision Trees: These intuitive models provide transparent decision-making processes, making them invaluable for both predictive modeling and explanatory analysis in business contexts. ◈ AdaBoost (Adaptive Boosting): A pioneering boosting algorithm that iteratively improves model performance by focusing on misclassified instances, demonstrating the power of ensemble learning in handling complex datasets. ◈ Gradient Boosting Machines (GBM): An advanced ensemble technique that sequentially builds models to correct errors, offering state-of-the-art performance in various domains, from finance to healthcare. ◈ Logistic Regression: Despite its simplicity, this algorithm remains a cornerstone of binary classification, providing interpretable results and probabilistic outputs crucial for risk assessment and decision boundary analysis. ◈ K-Means Clustering: An unsupervised learning approach essential for market segmentation, anomaly detection, and pattern discovery in high-dimensional data spaces. ◈ Support Vector Machine (SVM): Renowned for its effectiveness in high-dimensional spaces, SVM's ability to define optimal hyperplanes makes it indispensable in image classification and bioinformatics. ◈ K-Nearest Neighbors (KNN): A versatile, non-parametric method that shines in recommendation systems and pattern recognition tasks, leveraging the principle that similar data points cluster together. ◈ Regression Techniques: From linear to polynomial models, regression analysis forms the foundation of predictive modeling, offering insights into variable relationships and forecasting trends. The mastery of these algorithms empowers data scientists to extract meaningful insights, drive innovation, and solve complex business challenges. As we continue to push the boundaries of AI, a deep understanding of these foundational techniques remains paramount. What are your experiences with implementing these algorithms in real-world scenarios? How have they transformed your approach to data-driven decision-making? #MachineLearning #DataScience #ArtificialIntelligence #AdvancedAnalytics