Joachim Schork’s Post

➡️ What is a Data Structure? It's a way to organize, manage, and store data in a computer so it can be used and processed efficiently. 📚 The Main Types (A Quick Look): 1. Linear Structures: Data elements are arranged sequentially. - Arrays: A list of items in contiguous memory locations. - Linked Lists: Items linked via pointers (like a chain). - Stacks: Last-In, First-Out (LIFO) order (like a pile of plates). - Queues: First-In, First-Out (FIFO) order (like a waiting line). 2. Non-Linear Structures: Data elements are not in a sequence. - Trees: Hierarchical structure (like a family tree). - Graphs: A network of connected nodes (like social networks). - Hash Tables: Store data using a key/value pair for quick lookups. #DataStructures #CodingForBeginners #ComputerScience #TechSkills #Programming

Learn how to calculate z scores in R with this step-by-step tutorial. Use R's powerful functions to standardize your data and analyze its distribution. #rfaqs #rlanguage #learnRLanguage #RProgramming #Rquiz #Rmcqs #rlanguagemcqs #Rtutorial #Rdataanalysis #rLanguageprogramming #Rquizzes

"𝐈𝐟 𝐒𝐐𝐋 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐨𝐫𝐬… 𝐭𝐡𝐞𝐧 𝐒𝐭𝐨𝐫𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐞𝐬 𝐚𝐫𝐞 𝐑𝐨𝐛𝐨𝐭𝐬." Why? Because… A 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 only calculates and gives back 𝐨𝐧𝐞 𝐫𝐞𝐬𝐮𝐥𝐭 — just like a calculator. A 𝐒𝐭𝐨𝐫𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐞 can 𝐩𝐞𝐫𝐟𝐨𝐫𝐦 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐚𝐜𝐭𝐢𝐨𝐧𝐬 — like updating data, logging history, sending outputs — just like a robot that completes a full task workflow. 𝐑𝐞𝐚𝐥 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 (𝐁𝐚𝐧𝐤𝐢𝐧𝐠) Deposit money + log transaction --> 𝐒𝐭𝐨𝐫𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐞 (𝐌𝐮𝐥𝐭𝐢 𝐒𝐭𝐞𝐩 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰) sql 𝐄𝐗𝐄𝐂 𝐃𝐞𝐩𝐨𝐬𝐢𝐭𝐀𝐦𝐨𝐮𝐧𝐭 𝟏𝟐𝟑𝟒𝟓, 𝟓𝟎𝟎.𝟎𝟎; #Updates Balance and Log History Calculate interest --> 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 (𝐒𝐢𝐧𝐠𝐥𝐞 𝐑𝐞𝐭𝐮𝐫𝐧 𝐕𝐚𝐥𝐮𝐞) sql 𝐒𝐄𝐋𝐄𝐂𝐓 𝐜𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐞_𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭(𝟏𝟎𝟎𝟎𝟎, 𝟓, 𝟐); #Returns 1 output. 𝐄𝐚𝐬𝐢𝐞𝐬𝐭 𝐖𝐚𝐲 𝐭𝐨 𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫 Function = Calculator Stored Procedure = Performer / Robot Tagging for reach : my mentors: Dhaval Patel, Hemanand Vadivel, Codebasics #sql #database #programming #techlearning #interviewprep #backenddevelopment

Presenting your statistical models in a clear and professional manner is as crucial as the analysis itself. Here's a simple method to make this process very easy! LaTeX, a high-quality typesetting system, is often the go-to for academic and scientific documentation due to its ability to handle complex tables and mathematical expressions with elegance. When it comes to sharing the results of a regression model, seamlessly integrating these results into a LaTeX document can elevate your work's clarity and impact. This is where R and the xtable package come into play, providing a straightforward pathway to bridge your analysis with LaTeX's typesetting capabilities. Here’s how: # Load xtable library(xtable) # Load example data data(mtcars) # Linear regression my_mod <- lm(mpg ~ wt + qsec, mtcars) # Summary statistics summary(my_mod) # Create LaTeX table print(xtable(summary(my_mod)), type = "latex") This code snippet accomplishes a few key tasks: 📊 Data Analysis: Utilizes the mtcars data set to perform a linear regression, examining how the variables wt (weight) and qsec (quarter-mile time) predict mpg (miles per gallon). 🔍 Statistical Summary: Provides a comprehensive summary of the regression model, offering insights into the relationships between variables. 📝 LaTeX Integration: Converts the model summary into a LaTeX-friendly table using xtable, making it ready for inclusion in any document or publication. This step is crucial for researchers and analysts who need to present their findings in a standardized and visually appealing format. By leveraging xtable with R's analytical power, you can efficiently prepare your regression analysis results for high-quality documentation, enhancing readability and professionalism in your presentations or publications. Interested in deepening your understanding of topics like this? Subscribe to my free newsletter for regular insights on statistics, data science, and programming. See this link for additional information: http://eepurl.com/gH6myT #analytics #package #datasciencecourse #data

Knowing which tool to use is half the battle in data analytics. 👉 This visual lists common options for tabular data, programming, and data viz. Are there any essentials missing? #dataanalyst #dataalchemist #programming #tools #skills

💡 Here’s how I finally understood sorting algorithms! Visuals make everything easier. 🚀 Sorting Algorithms Made Simple Visual Way to Learn! We often hear about Bubble Sort, Selection Sort, and Insertion Sort but understanding how they really work becomes super easy when you can see it visually 👀 🔹 Bubble Sort → compares side-by-side elements and swaps them step by step. 🔹 Selection Sort → finds the smallest value in the list and places it at the front. 🔹 Insertion Sort → takes one element at a time and inserts it into its right position. 📊 This visual shows how each algorithm works with the same numbers, but in different ways making it easier to memorize and compare! 💡 If you’re learning Data Structures & Algorithms, this is your first big step towards understanding how computers “think” about sorting data. #SortingAlgorithms #DataStructures #ComputerScience #ProgrammingBasics #LearningJourney #AlgorithmVisualization #StudySmart #Cplusplus #CodeForBeginners

𝑺𝒑𝒂𝒄𝒆 𝑪𝒐𝒎𝒑𝒍𝒆𝒙𝒊𝒕𝒚 !!!... Space Complexity measures 𝙝𝙤𝙬 𝙢𝙪𝙘𝙝 𝙢𝙚𝙢𝙤𝙧𝙮 (𝙍𝘼𝙈) a program uses while running. It includes:  • Variables and constants.  • Data structures (arrays, lists, matrices).  • Function call stack (for recursion).  • Auxiliary/temporary memory used during computation. 𝑰𝒎𝒑𝒐𝒓𝒕𝒂𝒏𝒕 𝑻𝒆𝒓𝒎𝒔:  • 𝘈𝘶𝘹𝘪𝘭𝘪𝘢𝘳𝘺 𝘚𝘱𝘢𝘤𝘦: Extra memory used beyond input data.  • 𝘐𝘯-𝘱𝘭𝘢𝘤𝘦 𝘈𝘭𝘨𝘰𝘳𝘪𝘵𝘩𝘮: Uses very little extra memory → ideally O(1). 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗦𝗽𝗮𝗰𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 (𝗕𝗶𝗴 𝗢 𝗡𝗼𝘁𝗮𝘁𝗶𝗼𝗻) 1.𝑶(1) — 𝑪𝒐𝒏𝒔𝒕𝒂𝒏𝒕 𝑺𝒑𝒂𝒄𝒆 • Meaning: Memory usage 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝗴𝗿𝗼𝘄 with input size.   • Example: Using a few variables. 2.𝑶(𝒏) — 𝑳𝒊𝒏𝒆𝒂𝒓 𝑺𝒑𝒂𝒄𝒆 • Meaning: Memory usage grows linearly with input size.   • Example: Creating an extra array of size n. 3.𝑶(𝒏²) — 𝑸𝒖𝒂𝒅𝒓𝒂𝒕𝒊𝒄 𝑺𝒑𝒂𝒄𝒆  • Meaning: Memory usage grows with the square of input size.   • Example: Using a 2D array or matrix of size n × n. #SpaceComplexity #BigONotation #MemoryOptimization #CSFundamentals #TechExplained #LearnToCode #FreshersInTech #JavaProgramming #DevJourney #LinkedInLearning

🚀 Data Cleaning with R – Hands-On Practice! Today I worked on a small project in NTI to practice data cleaning in R, and it was a great reminder that clean data = better insights! 🧹📊 Here’s what I did step by step: ✅ Loaded the dataset and explored its structure. ✅ Handled missing values using replace_na() and filled missing product names with "Unknown". ✅ Removed duplicates to keep only unique records using: ✅ Checked data types and converted columns to the proper formats. ✅ Filtered data using logical conditions with AND / OR. Libraries I used: 📦 tidyverse, dplyr, caret, scales, and janitor This small exercise helped me better understand how to handle real-world messy data in R and prepare it for analysis or modeling. 💡 “Data cleaning is not boring — it’s where good analysis begins.” #RProgramming #DataCleaning #DataScience #LearningJourney #RStudio #DataAnalytics

Part 5: Programming Features of R - The Actuarial Engine for Precision R’s programming paradigm empowers actuaries to build fast, accurate, and scalable risk models. As an interpreted language, R enables real-time prototyping test GLMs, simulate reserving triangles, or validate pricing logic instantly in RStudio, slashing development time by up to 60%. Native matrix arithmetic accelerates core actuarial tasks. Perform credibility weighting, solve linear systems in capital modeling, or run PCA on mortality drivers with vectorized speed. Packages like Matrix extend this to high-performance computing. Procedural programming with functions supports modular workflows. Create reusable scripts for chain-ladder, Mack, or Bornhuetter-Ferguson methods, then automate scenario analysis using purrr ensuring auditability and compliance. Object-oriented programming (S3/S4) with generic functions adds structure. Define custom “Policy” classes with methods for premium calculation, claims simulation, and reporting ideal for evolving product frameworks. These features make R indispensable in 2025. Use dplyr for data prep, mgcv for GAMs, and Shiny for dashboards all in one auditable environment. Deploy via plumber APIs for real-time pricing or Posit Connect for reporting. Master vectorization, build S3 classes, and script production workflows. R isn’t just code it’s actuarial intelligence. #ActuarialScience #RProgramming #InsuranceAnalytics #DataScience #RiskModeling