Longhow Lam’s Post

For my second blog article, I wanted to go beyond technical topics. Therefore, I decided to focus on soft skills that I personally value when working (and interviewing) with other Data Scientists. I hope you’ll enjoy it!

Data Scientists can learn from Engineers. My latest study reveals a big problem—and the solution: Some people take averages and ignore the spread in data, while experienced engineers guide their analysis based on consequences. Read more at JSDSE.

📊 Embracing the Power of Statistics in Data Science: Exploring the Central Limit Theorem 📈 The Central Limit Theorem (CLT) holds a special place in the toolkit of every data scientist, serving as a guiding principle when dealing with sample distributions. "At its core, the CLT states that regardless of the underlying distribution of a population, the sampling distribution of the sample mean will tend to be normally distributed for a sufficiently large sample size." Here's why this theorem is so profound: 🔍 Universal Applicability: Whether we're analyzing the heights of individuals in a population or the response times of website users, the CLT provides a reliable framework for making inferences about the population mean. 💪 Robustness: Even if the original population distribution is skewed or non-normal, as long as the sample size is large enough, the sampling distribution of the sample mean will approximate a normal distribution. This robustness allows us to apply inferential statistics with confidence. 🎯 Foundation of Confidence Intervals and Hypothesis Testing: Understanding the CLT is essential for constructing confidence intervals and conducting hypothesis tests, which are fundamental techniques in data analysis and decision-making. 🌟 Practical Implications: In real-world data science projects, we often encounter scenarios where assumptions of normality may not hold. Thanks to the CLT, we can still proceed with our analyses, knowing that as our sample size increases, our estimates become more reliable. #Statistics #DataScience #CentralLimitTheorem #ContinuousLearning

For Baa, the best insights often emerge from a marriage of data science and the data management and analysis facilitated by GIS technology: https://lnkd.in/eHjijTtY

5 Excellent Data Science Courses to Consider in 2024 As data generation continues to explode, businesses are increasingly looking to leverage this information, creating a significant demand for skilled data scientists. The U.S. Bureau of Labor Statistics predicts that… https://lnkd.in/d_Jy7guv

In the realm of data science, Bayesian methods are reshaping our approach to predictive analytics and decision-making. 🤔 Bayesian methods provide a robust framework for making inferences in data science, allowing for the integration of prior knowledge and evidence through the use of probabilities. Unlike frequentist statistics, Bayesian probability considers all uncertainties within model parameters, making it incredibly powerful, especially in fields requiring robust risk assessment and decision-making under uncertainty. One noticeable advantage of Bayesian statistics is its flexibility in updating the probability of a hypothesis as more evidence becomes available. This feature is particularly useful in dynamic environments where data influx is continuous and decision frameworks must be adaptable. However, these methods can be computationally intensive and require a good understanding of their theoretical foundations to implement effectively. Despite these challenges, the depth of insight offered by Bayesian inference makes it a valuable tool in the arsenal of modern data science. Let’s dive deeper into this exciting topic! Follow for more insights on adapting Bayesian methods in practical scenarios. Feel invited to connect and discuss how Bayesian inference can revolutionize your analytical strategies! 🚀 #BayesianMethods #DataScience #BayesianDataScience #ProbabilisticProgramming #MachineLearning #DeepLearning

🌟Data Challenges in the Real World: Transitioning from Academia to Industry🌟As I venture from academic studies into the data engineering field, the shift in data manipulation techniques has been eye-opening! 📊 From structured environments in academia to dynamic, real-world scenarios, the change is palpaticle. Here’s what I’ve observed: Academic projects often provide predetermined datasets; industry projects require data extraction from diverse sources. Real-world data is messier and demands rigorous cleaning and validation. The pace is fast, driven by immediate business needs which means quicker turnarounds on projects. How did you adapt to these shifts if you moved from academia to industry? Any tips for newcomers like me?#DataEngineering #CareerTransition #RealWorldData

Dive into the world of #DataScience with Probability Theory! 🎲 Ever wondered how data scientists predict future trends with such accuracy? It's all about mastering the art of uncertainty through probability theory. Explore more in our latest article 🔗https://bit.ly/3TRNEuY

Just completed the High-Dimensional Data Analysis course from Harvard University on edX! 🎓 This course has equipped me with invaluable insights and skills to navigate complex data landscapes with confidence. From advanced techniques in data preprocessing and cleaning to dimensionality reduction and feature extraction, every module has been a revelation. Statistical inference in high-dimensional spaces and machine learning tailored for such data have opened new vistas of understanding. Moreover, mastering data visualization techniques tailored for complex datasets has empowered me to communicate insights effectively. Excited to apply these newfound skills in real-world scenarios and contribute meaningfully to the ever-evolving field of data science! 💡💻 #DataScience #OnlineLearning #edX #HarvardUniversity #DataAnalysis #HighDimensionalData #SkillsDevelopment #LifeLongLearning

📊 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧: 𝐏𝐢𝐨𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬! Looking to advance your career in Data Science?  #Datascience is the study of data to extract meaningful insights for business. To analyse massive volumes of data, it is a multidisciplinary technique that blends ideas and methods from computer engineering, #artificial #intelligence, statistics, and mathematics. Data Science Market Size is projected to reach USD 𝟵𝟰𝟭.𝟴 𝗕𝗶𝗹𝗹𝗶𝗼𝗻 by 2034 at a CAGR of 𝟯𝟭.𝟬% 𝘾𝙝𝙚𝙘𝙠 𝙤𝙪𝙩 𝙤𝙪𝙧 𝙡𝙖𝙩𝙚𝙨𝙩 𝙧𝙚𝙥𝙤𝙧𝙩 𝙤𝙣 𝙟𝙤𝙗 𝙩𝙧𝙚𝙣𝙙𝙨 𝙖𝙣𝙙 𝙚𝙨𝙨𝙚𝙣𝙩𝙞𝙖𝙡 𝙨𝙠𝙞𝙡𝙡𝙨 𝙞𝙣 𝙩𝙝𝙚 𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝙢𝙖𝙧𝙠𝙚𝙩: https://bit.ly/3W5j0Ai #DataScienceMarket #BigData #ArtificialIntelligence #MarketAnalysis #MachineLearning #DataAnalytics #TechIndustry #InnovationsInTech #BusinessIntelligence #MarketTrends