SAS DATA Step Programming Techniques with Art Carpenter

𝕃𝕖𝕒𝕣𝕟 𝕥𝕠 𝕊𝕠𝕝𝕧𝕖 𝕥𝕙𝕖 𝕋𝕠𝕦𝕘𝕙𝕖𝕤𝕥 𝔻𝕒𝕥𝕒 ℙ𝕣𝕠𝕓𝕝𝕖𝕞𝕤 ✔️ 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗔𝗦® 𝗗𝗔𝗧𝗔 𝗦𝘁𝗲𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 with 𝗝𝗼𝘀𝗵 𝗛𝗼𝗿𝘀𝘁𝗺𝗮𝗻 𝗪𝗲𝗱𝗻𝗲𝘀𝗱𝗮𝘆 𝗠𝗮𝘆 𝟲 | 𝟭𝟬𝑎𝑚-𝟮𝑝𝑚 𝑃𝑎𝑐𝑖𝑓𝑖𝑐 | $𝟵𝟵 To solve complex coding problems with the SAS® DATA step, one must go beyond a basic understanding of the individual statements. You need to understand how the various statements interact with each other and how their options can be leveraged to build DATA step code that provides innovative solutions to the toughest of problems. Based on Art Carpenter’s book, 𝘊𝘢𝘳𝘱𝘦𝘯𝘵𝘦𝘳’𝘴 𝘎𝘶𝘪𝘥𝘦 𝘵𝘰 𝘐𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘷𝘦 𝘚𝘈𝘚® 𝘛𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦𝘴, this class is a must for the DATA step programmer who wants to take his or her programs to the ‘next’ level. 𝕋𝕠𝕡𝕚𝕔𝕤 𝕚𝕟𝕔𝕝𝕦𝕕𝕖: ☉ Working across multiple observations using look-ahead and look-back techniques ☉ Employing the DOW loop ☉ Taking advantage of double SET statements ☉ Working with hash objects ☉ Performing table lookups ☉ Using arrays to transpose data from columns to rows and back again ☉ Evaluating complex expressions ☉ Applying data set options ☉ Adopting new DATA step functions (and old functions with new options) 𝔸𝕟𝕕 𝕞𝕠𝕣𝕖! This course is designed to be taken by a student who has a basic understanding of the DATA step and its primary statements. The material will focus on advanced topics that will give the student a deeper understanding of the operation of the DATA step. Through examples, students will be exposed to innovative techniques for solving difficult programming problems. For 𝗠𝗼𝗿𝗲 information and to 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 ... https://lnkd.in/g__Qzpwc #SAS #DATAstep #Programming

𝕋𝕙𝕖 𝔻𝔸𝕋𝔸 𝕊𝕥𝕖𝕡 ℂ𝕒𝕟 𝔻𝕠 𝕄𝕠𝕣𝕖 𝕋𝕙𝕒𝕟 𝕐𝕠𝕦 𝕋𝕙𝕚𝕟𝕜 ... 𝔻𝕚𝕤𝕔𝕠𝕧𝕖𝕣 𝕎𝕙𝕒𝕥! 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗔𝗦® 𝗗𝗔𝗧𝗔 𝗦𝘁𝗲𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 with 𝗝𝗼𝘀𝗵 𝗛𝗼𝗿𝘀𝘁𝗺𝗮𝗻 𝗪𝗲𝗱𝗻𝗲𝘀𝗱𝗮𝘆 𝗠𝗮𝘆 𝟲 | 𝟭𝟬𝑎𝑚-𝟮𝑝𝑚 𝑃𝑎𝑐𝑖𝑓𝑖𝑐 | $𝟵𝟵 To solve complex coding problems with the SAS® DATA step, one must go beyond a basic understanding of the individual statements. You need to understand how the various statements interact with each other and how their options can be leveraged to build DATA step code that provides innovative solutions to the toughest of problems. Based on Art Carpenter’s book, 𝘊𝘢𝘳𝘱𝘦𝘯𝘵𝘦𝘳’𝘴 𝘎𝘶𝘪𝘥𝘦 𝘵𝘰 𝘐𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘷𝘦 𝘚𝘈𝘚® 𝘛𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦𝘴, this class is a must for the DATA step programmer who wants to take his or her programs to the ‘next’ level. 𝕋𝕠𝕡𝕚𝕔𝕤 𝕚𝕟𝕔𝕝𝕦𝕕𝕖: ☉ Working across multiple observations using look-ahead and look-back techniques ☉ Employing the DOW loop ☉ Taking advantage of double SET statements ☉ Working with hash objects ☉ Performing table lookups ☉ Using arrays to transpose data from columns to rows and back again ☉ Evaluating complex expressions ☉ Applying data set options ☉ Adopting new DATA step functions (and old functions with new options) 𝔸𝕟𝕕 𝕞𝕠𝕣𝕖! This course is designed to be taken by a student who has a basic understanding of the DATA step and its primary statements. The material will focus on advanced topics that will give the student a deeper understanding of the operation of the DATA step. Through examples, students will be exposed to innovative techniques for solving difficult programming problems. For 𝗠𝗼𝗿𝗲 information and to 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 ... https://lnkd.in/g__Qzpwc #SAS #DATAstep #Programming

W𝐢l𝐥 𝐊i𝐫k P𝐮l𝐥 𝐚 𝐑a𝐛b𝐢t O𝐮t o𝐟 𝐚 𝐇a𝐭? ... S𝐚w H𝐢s A𝐬s𝐢s𝐭a𝐧t i𝐧 𝐇a𝐥f? ... 𝐌a𝐤e a S𝐭u𝐝e𝐧t D𝐢s𝐚p𝐩e𝐚r? E𝐱p𝐞r𝐢e𝐧c𝐞 𝐭h𝐞 𝐌a𝐜r𝐨 𝐌a𝐠i𝐜 ==> 𝗦𝗔𝗦® 𝗠𝗮𝗰𝗿𝗼 𝗠𝗮𝗴𝗶𝗰 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 With Kirk Paul Lafler 𝐴𝑝𝑟𝑖𝑙 9, 2026 | 10𝑎𝑚-2𝑝𝑚 𝑃𝑎𝑐𝑖𝑓𝑖𝑐 | $99 𝗟𝗲𝗮𝗿𝗻 𝘁𝗼 𝗪𝗿𝗶𝘁𝗲 𝗦𝗔𝗦 𝗖𝗼𝗱𝗲 𝗧𝗵𝗮𝘁 𝗪𝗼𝗿𝗸𝘀 𝗦𝗺𝗮𝗿𝘁𝗲𝗿, 𝗡𝗼𝘁 𝗛𝗮𝗿𝗱𝗲𝗿 ... This course introduces macro programming as a productivity tool, not a mystery language by stripping away the confusion and focusing on practical patterns beginners can use immediately. With guided examples and incremental exercises, you’ll learn how to construct macros that automate common tasks such as dataset creation, reporting, and repetitive analyses – perfect for business and analytics environments, and how macros can help you write code once and run it many times – saving effort, reducing errors, and improving consistency across projects. 𝕂𝕖𝕪 𝕋𝕠𝕡𝕚𝕔𝕤 Macro basics explained in plain language The role of macros in scalable SAS programming Creating reusable macro templates Conditional macro logic for workflow control Using macro parameters effectively Macro loops for repetitive tasks Debugging and testing macros 𝕃𝕖𝕒𝕣𝕟𝕚𝕟𝕘 𝕆𝕦𝕥𝕔𝕠𝕞𝕖𝕤 Confidently read and write basic SAS macros Design reusable SAS programs using macros Apply looping and parameterization techniques Improve code efficiency and consistency Understand when macros add value – and when they don’t. For 𝗠𝗼𝗿𝗲 information and to 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 ... https://lnkd.in/g__Qzpwc #Macros #SAS #KirkPaulLafler #Beginner

 Data doesn’t always come in pretty Excel sheets… and that’s where the real learning begins.  Day 7 of my 30 Day Data Analysis Challenge Today I tackled something that every beginner data analyst eventually faces: Different data file types. When I first started, I thought everything came as a neat spreadsheet. But this challenge reminded me that data can show up in all sorts of shapes and formats and knowing how to handle them is half the job! Here’s what I learned today  CSV (.csv) ; The classic! Clean and simple, perfect for starting out. But sometimes your “commas” change to semicolons and suddenly nothing works  Excel (.xlsx) ;A familiar friend for most beginners. Great for small datasets, but hidden cells or formulas can cause surprises when you try to import them.  JSON (.json) ; Mostly found in web apps or APIs. Looks confusing at first with all the brackets, but once you learn the structure, it’s super powerful.  ZIP (.zip) ; Big datasets often come zipped. It’s like downloading your data in a bundle unzip it, and a whole new world of files appears.  TXT (.txt) ; Raw, unstructured data. It’s messy, but learning how to work with logs and plain text teaches you real problem-solving.  My biggest takeaway: understanding file formats helps you connect with your data better. Once you know where it comes from and how it’s stored, you can start analyzing more confidently. Tomorrow’s goal: data cleaning techniques turning messy data into something useful #30DayChallenge #BeginnerDataAnalyst #DataAnalysis #LearningInPublic #Python #Excel #DataJourney

Understanding the z-score can significantly enhance your data analysis skills. Here’s a quick guide to what z-scores are and why they matter: 🔍 What is a z-score? A z-score, or standard score, indicates how many standard deviations an element is from the mean. A z-score of 0 means the value is exactly average, while a z-score of +1.5 indicates a value 1.5 standard deviations above the average. 📊 Why use z-scores? - Comparability: It allows comparison between different data sets with various means and standard deviations. - Outlier Detection: High or low z-scores may help identify potential outliers in a data set. - Standardization: Z-scores help standardize data, preparing it for techniques that assume normal distribution. 🚧 Limitations of z-scores: - Assumption of Normality: Z-scores are most effective when data follows a normal distribution. Their reliability decreases with heavily skewed data or extreme outliers. For example, the data in this post's graph does not appear normally distributed. While z-scores can still be calculated, they must be applied cautiously. - Context Dependent: The interpretation of a z-score can vary by context; a z-score considered high in one field might be average in another. - Oversimplification: Relying solely on z-scores might oversimplify the analysis, potentially overlooking important nuances in the data. 💡 Z-scores transform your data, facilitating complex analyses and potentially leading to more consistent conclusions. Whether you're examining student test results or assessing stock market fluctuations, z-scores can offer a clear picture of how each data point relates to the whole. I've created a tutorial on how to compute z-scores in the R programming language: https://lnkd.in/eN4SksN Interested in mastering statistics and R? Check out my online course on Statistical Methods in R. Take a look here for more details: https://lnkd.in/ed7XyXQm #rstudioglobal #RStats #database #Data #Python #coding #DataViz #statisticians #VisualAnalytics

Learning SQL can feel overwhelming at first, but breaking it down into smaller pieces makes it much more manageable. At its core, SQL is about understanding how data is stored, accessed, and manipulated. Once you grasp the fundamentals like SELECT, WHERE, and JOIN, everything else starts to build naturally on top of that foundation. What helped me the most was focusing on three things: First, understanding how databases and tables are structured. Second, practicing queries daily instead of just reading theory. Third, learning how to think in terms of data problems rather than syntax. SQL is not just a skill, it is a mindset. The more you practice, the more intuitive it becomes. If you are starting your SQL journey, don’t rush. Focus on consistency, and you will see progress sooner than you expect. #SQL #DataAnalytics #DataScience #LearningJourney #TechSkills #CareerGrowth #Analytics #Programming #DataCommunity

📌 SQL Cheat Sheet (All Categories in One Place) SQL is one of the most important skills for anyone starting their journey in Data Engineering, Databases, Backend Development, or Data Analytics. I created this SQL Cheat Sheet to quickly understand and revise the core concepts of SQL, including: ✅ DDL ✅ DML ✅ SELECT Queries ✅ Joins ✅ GROUP BY & HAVING ✅ Analytical Queries ✅ Normalization This cheat sheet is helpful for beginners who want to build a strong foundation in SQL and understand how data is created, managed, queried, analyzed, and structured in databases. Learning step by step and improving every day. 🚀 #SQL #Database #DataEngineering #DataAnalytics #BackendDevelopment #CloudDataEngineering #SQLQueries #Joins #Normalization #LearningJourney #TechLearning #Programming #DataSkills #SMIT #SaylaniMassITTraining

𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗦𝗤𝗟 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝗲𝗮𝗱 𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝘆? Where does it start? This is basic, but if you understand this, writing queries becomes much easier. Let’s take a simple example with two tables: Table 1: names → student_id, student_name Table 2: subjects → student_id, subject_name Query: SELECT n.student_name, s.subject_name FROM names n INNER JOIN subjects s ON n.student_id = s.student_id Now here’s the confusing part: In SELECT, we are already using aliases n and s But those aliases are defined later in FROM and JOIN. So how does SQL understand this? Because SQL does NOT execute top to bottom like C++. It logically works like this: FROM → loads the table JOIN → combines tables WHERE → filters rows GROUP BY → groups data HAVING → filters groups SELECT → picks columns ORDER BY → sorts LIMIT → restricts rows So even though SELECT is written first, it actually runs later. That’s why aliases defined in FROM are already available. Why this matters: If you don’t understand how SQL thinks, you’ll keep guessing queries. In C++ we do dry run step by step. In SQL, it’s about understanding data flow. Once this clicks, SQL becomes much easier. #SQL #Databases #DataAnalytics #DataScience #Programming #SoftwareEngineering #DSA #LearningInPublic #Developers #Tech

😅 Debugging errors with coffee… or maybe debugging because of coffee. 🚀 Week 1 Update: Building a complete data system (step by step) This week wasn’t smooth. More like… fix one thing, break another. Classic. But progress is happening 👇 🔹 Data Downloader (Python) 🔹 Database Creation (Python) 🔹 DB → CSV extraction (SQL) 🔹 Matrix creation from raw data (Query) 👉 Right now, I’m in the raw data → matrix creation phase And honestly… debugging all of this? ⚠️ Sometimes exciting ⚠️ Sometimes confusing ⚠️ Sometimes pure anxiety 😅 But that’s where the real learning is. 🎯 Next targets: • Finalize matrix structure • Build a Snowflake schema • Create a main pivot system • Generate multiple reports from one source • Add VBA automation (refresh → update → auto-save reports) This time it’s different… 👉 Not just solving problems, but building a complete automated reporting system. Still figuring things out… but step by step, it’s coming together. 💪 If you’ve been through this phase… what stresses you more: debugging, data modeling, or automation? 👀 #DataAnalytics #Automation #Python #SQL #Excel #VBA #Debugging #DataModeling #SnowflakeSchema #DataPipeline #LearningJourney

𝕋𝕙𝕖𝕣𝕖'𝕤 𝕊𝕥𝕚𝕝𝕝 𝕋𝕚𝕞𝕖 (𝟚 𝕕𝕒𝕪𝕤 ⏳) ... ℂ𝕠𝕟𝕢𝕦𝕖𝕣 𝕊𝔸𝕊 𝕄𝕒𝕔𝕣𝕠𝕤 𝕨𝕚𝕥𝕙 𝕒 𝕄𝕒𝕤𝕥𝕖𝕣 🎓 𝗦𝗔𝗦® 𝗠𝗮𝗰𝗿𝗼 𝗠𝗮𝗴𝗶𝗰 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 𝑳𝒆𝒂𝒓𝒏 𝒕𝒐 𝑾𝒓𝒊𝒕𝒆 𝑺𝑨𝑺 𝑪𝒐𝒅𝒆 𝑻𝒉𝒂𝒕 𝑾𝒐𝒓𝒌𝒔 𝑺𝒎𝒂𝒓𝒕𝒆𝒓, 𝑵𝒐𝒕 𝑯𝒂𝒓𝒅𝒆𝒓 with 𝗞𝗶𝗿𝗸 𝗣𝗮𝘂𝗹 𝗟𝗮𝗳𝗹𝗲𝗿 𝐴𝑝𝑟𝑖𝑙 9, 2026 | 10𝑎𝑚-2𝑝𝑚 𝑃𝑎𝑐𝑖𝑓𝑖𝑐 | $99 This course introduces macro programming as a productivity tool, not a mystery language by stripping away the confusion and focusing on practical patterns beginners can use immediately. With guided examples and incremental exercises, you’ll learn how to construct macros that automate common tasks such as dataset creation, reporting, and repetitive analyses – perfect for business and analytics environments, and how macros can help you write code once and run it many times – 𝘀𝗮𝘃𝗶𝗻𝗴 𝗲𝗳𝗳𝗼𝗿𝘁, 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗲𝗿𝗿𝗼𝗿𝘀, 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 across projects. 𝕂𝕖𝕪 𝕋𝕠𝕡𝕚𝕔𝕤 Macro basics explained in plain language The role of macros in scalable SAS programming Creating reusable macro templates Conditional macro logic for workflow control Using macro parameters effectively Macro loops for repetitive tasks Debugging and testing macros 𝕃𝕖𝕒𝕣𝕟𝕚𝕟𝕘 𝕆𝕦𝕥𝕔𝕠𝕞𝕖𝕤 Confidently read and write basic SAS macros Design reusable SAS programs using macros Apply looping and parameterization techniques Improve code efficiency and consistency Understand when macros add value – and when they don’t. For 𝗠𝗼𝗿𝗲 information and to 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 ... https://lnkd.in/g__Qzpwc #SAS #Macros #KirkPaulLafler