Joerg Fliege’s Post

🚀 Day 4 of #15DaysStatsJourney: Today, we explore Probability Distributions, the backbone of statistical inference and data modeling. Ready to uncover the patterns in your data? 📊🔍 #DataScience #ProbabilityAndStatistics 📌 Question: What are Probability Distributions, and why are they essential in data science? 📝 Answer: A Probability Distribution describes how the values of a random variable are distributed. It provides a mathematical function that represents the probabilities of all possible outcomes in a sample space. Understanding probability distributions is crucial for modeling data, making predictions, and conducting statistical analyses. 🌱 Learning: By mastering probability distributions, data scientists can better understand the underlying structure of data, select appropriate models, and make informed decisions. Distributions help in estimating probabilities, finding expected values, and assessing variability. 🔑 Key Point: Probability distributions can be discrete or continuous. Some common distributions include the Normal, Binomial, and Poisson distributions, each serving different purposes and having unique characteristics. 📊 Data Insights: For instance, in quality control, the normal distribution can model measurement errors. In customer service, the Poisson distribution can model the number of calls received per hour. 🚀 Example: In finance, the normal distribution is often used to model the returns of a stock portfolio, helping analysts understand risk and return characteristics. 1. Discrete vs. Continuous Distributions:    - Discrete Distribution: Deals with variables that have specific and countable outcomes. Examples include the Binomial and Poisson distributions.    - Continuous Distribution: Deals with variables that can take any value within a given range. Examples include the Normal and Exponential distributions. 2. Common Probability Distributions:    - Normal Distribution:      - Definition: A continuous distribution characterized by its bell-shaped curve, symmetric around the mean.      - Properties: Defined by its mean (μ) and standard deviation (σ).    - Binomial Distribution:      - Definition: A discrete distribution representing the number of successes in a fixed number of independent Bernoulli trials.      - Properties: Defined by the number of trials (n) and the probability of success (p).    - Poisson Distribution:      - Definition: A discrete distribution representing the number of events occurring within a fixed interval of time or space.      - Properties: Defined by the average rate (λ) of occurrence.    - Exponential Distribution:      - Definition: A continuous distribution representing the time between successive events in a Poisson process.      - Properties: Defined by the rate parameter (λ).

The main objective of any data scientist is not to achieve accuracy thresholds artificially created by stakeholders but to quantify the uncertainty inherent in real world processes to guide decision making. You cannot create signal from noise (impossible) and by fixating on predetermined accuracy benchmarks the likelihood of model overfitting becomes almost certain. Don’t stand on your head and expect to walk forwards. A data scientist has done their job when a model reflects the true data generating process. When this happens another researcher will not be able to produce significantly more accurate predictions on unseen data. What this means: data scientists are business partners not your server 😁

Vision : Develop a personal data scientist application, that will work daily to provide insights to meet your business goals. How does it work? Step 1 : The artificial data scientist (ADS) asks, a series of questions about the business and goals to the business owner. Step 2 : Post getting access, the ADS scans through the database and builds relationships based on its proprietary knowledge about data, metrics, data fields and the additional database metadata shared by the business. Step 3 : The ADS creates a report with 10 KPIs and 10 dimension, which is to be thoroughly validated by the business. Step 4 : The ADS generates 15 roadmap actions to verify if its aligns with the business' future roadmap, to which feedback is required with detailed sentences. Step 5 : Your ADS is ready to work for on your goals. It will provide an on-demand or daily summary of observations and data driven insights, that is aligned to the business goals.

The Essence of Data Engineering: Problem-Solving Over Technology Being a data engineer isn’t about chasing the latest technologies or building overly complex, scalable solutions. At its core, it’s about understanding problems and solving them in the most practical and efficient way. Sometimes, a simple Excel sheet is all you need to solve your problem. The real skill lies in choosing the right tool and approach based on the context, the people involved, and the problem at hand. Here are three fundamentals to guide your decisions: What problem are we solving? Define it clearly and prioritize the impact. Who will solve the problem? Consider the team’s expertise and capacity. How quickly do we need results? Balance urgency with long-term goals. Think of it like running a food business on the street. To succeed, you need a focused menu with a few cost-effective recipes tailored to your customers. Offering too many high-cost varieties might not attract the audience you're serving, and it could lead to failure. The same applies to data engineering. Over-engineering or misaligning solutions can waste time and resources. Focus on simplicity, efficiency, and impact. Let the problem dictate the solution not the technology.

Understanding probability distributions is crucial for interpreting data accurately and making informed decisions. Here’s a quick guide on why they are essential in statistical analysis: 🔹 Identify Patterns: Probability distributions help us understand how data is spread across different values, which is key for predicting future events. 🔹 Risk Assessment: They are used to quantify the risk in various scenarios, enabling businesses and investors to make better risk management decisions. 🔹 Quality Control: In manufacturing, probability distributions are used to assess product quality and maintain control over the manufacturing process. 🔹 Hypothesis Testing: They are fundamental in determining the significance of data-driven insights, helping to confirm or reject initial hypotheses. Understanding these distributions can greatly enhance your analytical capabilities, whether you're a data scientist, market researcher, or student of statistics. I've created an introduction to different types of probability distributions using R programming. More information: https://lnkd.in/e98h3xB #dataanalytics #rstats #datastructure #bigdata #advancedanalytics

If you want to have an impact in your organisation as a data scientist you need to get things done. However, the danger is that the data, the tech, or the organisation’s requirements can all change rapidly making weeks to months of work worthless. Read my blog post for some ideas and tips.

Having strong statistical and analytical capacity within your team can significantly enhance the precision, accuracy, reliability, and relevance of your data. This article delves into the essential skillsets required to ensure responsible data handling throughout the data lifecycle from collection to dissemination and use. Read more about it here: https://lnkd.in/dFtmpcKS #Analysis #Statistics #DataResponsibility

💻 Data teams are often undervalued and not rewarded for the hard work that goes into maintaining, cleaning, and optimizing important data models. As a result, data untrustworthiness (data debt) can accumulate at a business. 📊 Data scientists, analysts, and business intelligence experts are expected to move quickly and answer questions iteratively, instead of building scalable data infrastructure that can be leveraged by the entire organization. 📈 Data platforms are often treated as second-class citizens, despite the fact that many business-critical operations like machine learning, usage-based pricing reports, and more are run off the data warehouse. 👊 In order for data quality goals to be achieved, this needs to change. Recognising data as a product, treating the data warehouse as production, and resolving data debt can be just as valuable as delivering features, because it allows others to deliver results faster. 💪 #understandingdata #dataengineering #data

🌟 Understanding Outliers and Their Impact on Data Analysis 🌟 In the world of data, outliers can make or break the accuracy of your results. But what exactly are outliers, and why is their detection so important? 🔎 What is an Outlier? An outlier is a data point that significantly deviates from the rest of the dataset. It might be an error or a rare, meaningful occurrence. Either way, outliers can skew averages and impact statistical models. 💡 Why Should You Care? Data Quality: Outliers might highlight data entry or measurement errors that need correction. Accuracy: Statistical measures like the mean and standard deviation can be disproportionately affected by outliers. Model Performance: In machine learning, outliers can lead to overfitting or underfitting. Handling them improves model robustness. Insights: Sometimes, outliers aren’t errors, but valuable insights—like fraud detection in finance or rare events in environmental data. 🔧 How to Handle Outliers: Remove them (if they are errors) Analyze separately (if they hold significance) Use robust statistical methods that minimize their impact 📊 Example R Code: I recently worked on a project where I wrote R code to detect outliers based on z-scores. This code identifies outliers by comparing them to predefined thresholds and helps to count outliers in numeric datasets. Detecting outliers is a crucial first step in data preprocessing, ensuring the analysis remains accurate and meaningful. #DataScience #DataQuality #Outliers #MachineLearning #RProgramming #Analytics

Unfortunately, every machine learning book starts with a static dataset. But there aren't static datasets in the real world. When building production systems, we deal with *data*. Messy, unstructured, and always-changing data. Part of your job is to define, collect, and curate the data you need to solve a problem. This is usually a shock to people who come to the industry with no previous experience. They think their job starts after downloading dataset.zip.