Georgia Tech Research Institute’s Post

Unlock the Power of Efficient Experimentation! 🌟Experiments should be informative, efficient, and directional, but most technical professionals have no idea how to make that happen.  Ever wondered how to insightfully test a system with many possible variables? When performing a test, how do you know which variables actually matter? How much testing is required? 🤔 If you’re unsure how to answer those questions, then discover the game-changing world of Design of Experiments (DOE) I! Perfect for engineers, technicians, data analysts, and any tech professional eager to upgrade their experimentation skills. 🔍 What You'll Gain: -Master the art of factorial experimentation. -Learn to confidently answer the critical question, “which inputs ACTUALLY matter, and by how much?” -Determine the optimal amount of testing. -Justify the cost with efficient testing strategies. -Learn to brainstorm effectively for impactful testing. -Predict outcomes with statistical confidence. -Navigate modern software tools for experiment design. 🚀 Transform your approach to experimentation and drive innovation with Design of Experiments I! Don't miss out on our upcoming session in Atlanta, GA, from July 16-18, 2024. 📌 Secure your spot now! https://lnkd.in/eS6pqx3A #GTRI #GTPE #Innovation #Efficiency #TechnicalExcellence #ExperimentDesign

Unlock the Power of Efficient Experimentation with this Short Course from Georgia Tech! 🌟 Experiments should be informative, efficient, and directional, but most technical professionals have no idea how to make that happen.  Ever wondered how to insightfully test a system with many possible variables? When performing a test, how do you know which variables actually matter? How much testing is required? 🤔 If you’re unsure how to answer those questions, then discover the game-changing world of Design of Experiments (DOE) I! Perfect for engineers, technicians, data analysts, and any tech professional eager to upgrade their experimentation skills. 🔍 What You'll Gain: -Master the art of factorial experimentation. -Learn to confidently answer the critical question, “which inputs ACTUALLY matter, and by how much?” -Determine the optimal amount of testing. -Justify the cost with efficient testing strategies. -Learn to brainstorm effectively for impactful testing. -Predict outcomes with statistical confidence. -Navigate modern software tools for experiment design. 🚀 Transform your approach to experimentation and drive innovation with Design of Experiments I! Don't miss out on our upcoming session in Atlanta, GA, from May 7-May 9, 2024. 📌 Secure your spot now! https://lnkd.in/ejYEpE7m #GTRI #GTPE #Innovation #Efficiency #TechnicalExcellence #ExperimentDesign

𝗛𝗲𝗹𝗹𝗼, 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆!  Today, I delved into the fundamental concepts of sorting techniques and analyzed algorithms through a structured approach. Here's a summary of what I learned: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗦𝗼𝗿𝘁𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: Explored the significance of sorting algorithms in organizing data for efficient processing and retrieval. 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗼𝗳 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀: 𝟭. 𝗡𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻𝘀: Examined the count of comparisons made during the sorting process, indicating the efficiency of the algorithm in evaluating elements. 𝟮. 𝗡𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝗦𝘄𝗮𝗽𝘀: Analyzed the frequency of element swaps, providing insights into the algorithm's efficiency in rearranging elements. 𝟯. 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲: Explored whether the algorithm's performance adapts based on the initial order of elements, optimizing its efficiency. 𝟰. 𝗦𝘁𝗮𝗯𝗹𝗲: Investigated if the algorithm preserves the relative order of elements with equal keys, ensuring consistency in sorting results. 𝟱. 𝗘𝘅𝘁𝗿𝗮 𝗠𝗲𝗺𝗼𝗿𝘆: Assessed the additional memory space required by the algorithm, determining its impact on resource utilization. 𝗕𝘂𝗯𝗯𝗹𝗲 𝗦𝗼𝗿𝘁: * Traced the Bubble Sort algorithm manually on pen and paper, gaining a deeper understanding of its mechanics and complexities. * Analyzed the time and space complexities of Bubble Sort, providing insights into its efficiency and scalability. * Explored techniques to enhance Bubble Sort's adaptability and stability, optimizing its performance for various datasets. Through this exploration, I gained valuable insights into sorting algorithms and their analysis, laying the groundwork for further study and experimentation in the realm of algorithms and data structures. Excited to continue this journey and explore more sorting techniques and optimization strategies! Stay tuned for more updates as I uncover more insights in the world of DSA!  #DSA #SortingTechniques #AlgorithmAnalysis #BubbleSort #LearningJourney #ProblemSolving #ContinuousImprovement #TechSkills #LinkedInLearning #TechCommunity

Data science and Artificial intelligence, two giants of the modern world! They're intricately linked, yet distinct in their roles. We can explore specific applications, career paths, or the ethical considerations surrounding these exciting fields! Interested candidates can feel free to contact HR Nikitha- 78456 84037 #clinicalresearch #clinicaldatamanagement #regulatoryaffairs #medidate #SAS #Biostatistics #biostatistician #statistics #statisticalprogramming #training #hr #hiring #jobs #course #courses #freshers #fresher #mode #recruiting #recruitment #resume #pharmacy #pharmaceutical #pharmajob #pharmacist #pharmacovigilance #dubai #uae #clinicalresearch #pharmacovigilance #cdm #arabguidelines

Good day everyone, I recently joined Quantum Analytics NG as a data analyst intern, and I am excited to make my first publication here of an analysis dashboard that I prepared myself. It’s my 2nd dashboard assignment in Quantum Analytics Ng. Dashboard Title: Unicorn Companies Analysis Dashboard. Data Source: Quantum Analytics NG. Tool Used: Power BI. Some of the generated Insights: ·      Total of the Unicorn Companies. ·      Total Number of Unicorn Companies. ·      Number of industries the Unicorn Companies fall into. ·      Countries with most Unicorn companies. ·      Top 10 most funded Unicorn Companies. ·      Top 10 most valued Industries that these companies belong to, Etc. A very special thanks to: Quantum Analytics NG, Jonathan Osagie, Christian Okwara and the entire Quantum Analytics team. #dataanalysis #quantumanalyticsng

Today's lc problem is very interesting! I had so much fun solving it! Problem statement: Given an array nums of size n, return the majority element. The majority element is the element that appears more than ⌊n / 2⌋ times. You may assume that the majority element always exists in the array. There's many ways to solve this, let's do naive approaches first and optimize it as we gain a better understanding of the nuances! Approach 1: sort the array and compare the first, middle and last element. if any one of them are equal to the other then we have found our candidate. Time Complexity: O(nlogn) Space Complexity: O(1) Approach 2: Use a hasmap and map each element with its frequency of occurrence. Then traverse again to pick whichever one exceeds floor(n / 2) tiems Time complexity: O(n) Space complexity: O(n) Approach 3: If we want to optimize further in terms of space we have to use moore's voting algorithm. At all times we need to maintain the current count and candidate as we parse we need to continuisly update this such that if there is a new candidate => decrement count if count reaches zero that means the current element has been surpassed by someother element => if this happens we reset the candidate to the current element which has surpassed the existing candidate if we encounter the same candidate again => we simply increment the counter by one. By following this approach we can make sure that we are getting ourselves towards the element that if exists will be the majority element. But we cannot affirm this for sure=> why we cannot affirm this for sure ?? The answer lies that there might be an occurence of same element enough times that it is enough to update itself as candidate but this occurence need not necessarily be more tha floor(n / 2). hence we need to do a linear traversal to confirm if the candidate is indeed present more than floor(n / 2) times!! Time Complexity: O(n) Space Complexity: O(1) #leetcode #datastructuresandalgorithms #mooresvotingalgorithm

Big shout out to our intern Helen N. for finding this awesome paper about "valid straightlining" in surveys, which identifies that "in many common situations, straightlining is [actually] a consequence of increasing data quality." (Kevin Reuning and Eric Plutzer; https://lnkd.in/gwJgWJvW ). It's really thought-provoking stuff. I can't remember the last time I read an academic paper this closely. I looked around Google Scholar to see what others are doing with these findings. Many are thinking harder about whether removing systematically biased responses is necessarily a good thing in context, and taking more care to try and distinguish between "valid" and "invalid" straightlining before removing responses. Some are ... not 🤷♂️

The tech industry has been moving to no-code or low-code solutions for decades. For example, it’s much easier for a business to setup an e-commerce website using tools provided by the likes of GoDaddy and Shopify. AI will simplify the process further and allow for much more bespoke solutions. However, this will not replace developers. From a test engineers’s perspective, it adds opportunity to to use AI tools and focus on the actual act of testing software rather than spending lots of time on developing test tools and scripts.

🚀 Sample Regression Model Validation Techniques 🚀 In my recent lab, I delved into the intricacies of regression model validation using the Ames Housing dataset. Here are some key techniques I utilized: 🔍 Data Preparation: I employed a meticulous train-test split to ensure robust model evaluation. By setting a random state, I guaranteed consistent results across multiple iterations. 🔧 Feature Engineering:   - Log Transformation: Applied to continuous variables like 'LotArea' and 'GrLivArea' to normalize the data and improve model performance.   - One-Hot Encoding: Used for categorical features to handle non-numeric data, ensuring the model accurately interprets different categories without multicollinearity. 📈 Model Training:   - Implemented a linear regression model to predict housing prices, leveraging the transformed data to enhance prediction accuracy. 🧮 Model Evaluation:   - Mean Squared Error (MSE): Calculated for both training and test datasets to assess model performance.   - Conducted analysis to identify overfitting/underfitting by comparing errors across different train-test split sizes. 🔄 Iterative Validation: Tested multiple train-test splits and averaged results to ensure the model's robustness, accounting for variability in data sampling. By focusing on these methodologies, I ensured the model's reliability and generalizability, providing actionable insights into the housing market. #DataScience #MachineLearning #RegressionModeling #ModelValidation #DataAnalysis #FeatureEngineering #LinearRegression #ADHDAwareness #DataDriven

Explore practical insights on career navigation and tips for securing your first role in the data industry. Read about key strategies and experiences shared in my latest article