Data Ingestion Types – Concepts & Examples
Last updated: 17th Nov, 2023 Data ingestion is the process of moving data from its original storage location to a data warehouse or other database for analysis. Data engineers are responsible for designing and managing data ingestion pipelines. Data can be ingested in different modes such as real-time, batch mode, etc. In this blog, we will learn the concepts about different types of data ingestion with the help of examples. What is Data Ingestion? Data ingestion is the foundational process of importing, transferring, loading, and processing data from various sources into a storage medium where it can be accessed, used, and analyzed by an organization. It’s akin to the first …
Two samples Z-test for Means: Formula & Examples
Last updated: 21st Nov, 2023 Statistical hypothesis testing is an essential tool in inferential statistics that enables researchers to make informed decisions about the population parameters based on sample statistics. One common hypothesis test for comparing two sample means is the Two-Sample Z-Test. In statistics, a two-sample z-test for means is used to determine if the means of two populations are equal. This test is used when the population standard deviations are known. As data scientists, it is of utmost importance to be able to understand and conduct this test accurately. In this blog, we will delve deeper into the Two-Sample Z-Test for means, exploring its formula, assumptions, and examples …
Histogram Plots using Matplotlib & Pandas: Python
Executing the above code will print the following Histogram. Plotting multiple Histograms Side-by-Side using Matplotlib & Pandas When you want to understand the distribution of data with respect to different characteristics, you could plot the side-by-side or multiple histograms on the same plot. For example, when you want to understand the distribution of housing prices with respect to different values of accessibility to radial highways, you would want to print the histograms side-by-side on the same plot. Here is the code representing the printing of histogram plots side-by-side on the same plot: Here is how the side-by-side histogram plot would look like: Creating Stacked Histogram Plots using Matplotlib & Pandas …
Confusion Matrix Concepts, Python Code Examples
The confusion matrix is an essential tool in the field of machine learning and statistics for evaluating the performance of a classification model. It’s particularly useful when dealing with binary or multi-class classification problems. In this post, you will learn about the confusion matrix with examples and how it could be used as performance metrics for classification models in machine learning. What is Confusion Matrix? A confusion matrix is a table used to describe the performance of a classification model on a set of test data for which the true values are known. It’s most useful when you need to know more about the accuracy of the model than just …
Maximum Likelihood Estimation: Concepts, Examples
Maximum Likelihood Estimation (MLE) is a fundamental statistical method for estimating the parameters of a statistical model that make the observed data most probable. MLE is grounded in probability theory, providing a strong theoretical basis for parameter estimation. This is becoming more so important to learn fundamentals of MLE concepts as it is at the core of generative modeling (generative AI). Many models used in machine learning and statistics are based on MLE, including logistic regression, survival models, and various types of machine learning algorithms. MLE is particularly important for data scientists because it underpins many of the probabilistic machine learning models that are used today. These models, which are …
Wilcoxon Signed Rank Test: Concepts, Examples
How can data scientists accurately analyze data when faced with non-normal distributions or small sample sizes? This is a challenge that often arises in the dynamic field of data science, where making precise inferences is crucial. Enter the Wilcoxon Signed Rank Test—a non-parametric statistical method that stands as a powerful alternative to the traditional t-test. This blog post aims to unravel the concepts and practical applications of the Wilcoxon Signed Rank Test, offering key insights for data scientists and researchers navigating complex data landscapes. The beauty of the Wilcoxon Signed Rank Test lies in its wide applicability across numerous fields. From healthcare, where it can compare the efficacy of different …
R-squared in Linear Regression Models: Concepts, Examples
In linear regression, R-squared (R2) is a measure of how close the data points are to the fitted line. It is also known as the coefficient of determination. Understanding the concept of R-squared is crucial for data scientists as it helps in evaluating the goodness of fit in linear regression models, compare the explanatory power of different models on the same dataset and communicate the performance of their models to stakeholders. In this post, you will learn about the concept of R-Squared in relation to assessing the performance of multilinear regression machine learning model with the help of some real-world examples explained in a simple manner. Before doing a deep dive, …
Hierarchical Clustering: Concepts, Python Example
Hierarchical clustering a type of unsupervised machine learning algorithm that stands out for its unique approach to grouping data points. Unlike its counterparts, such as k-means, it doesn’t require the predetermined number of clusters. This feature alone makes it an invaluable method for exploratory data analysis, where the true nature of data is often hidden and waiting to be discovered. But the capabilities of hierarchical clustering go far beyond just flexibility. It builds a tree-like structure, a dendrogram, offering insights into the data’s relationships and similarities, which is more than just clustering—it’s about understanding the story your data wants to tell. In this blog, we’ll explore the key features that …
Minimum Description Length (MDL): Formula, Examples
Learning the concepts of Minimum Description Length (MDL) is valuable for several reasons, especially for those involved in statistics, machine learning, data science, and related fields. One of the fundamental problems in statistics and data analysis is choosing the best model from a set of potential models. The challenge is to find a model that captures the essential features of the data without overfitting. This is where methods such as MDL, AIC, BIC, etc. comes to rescue. MDL offers a principled way to balance model complexity against the goodness of fit. This is crucial in many areas, such as machine learning and statistical modeling, where overfitting is a common problem. …
AIC & BIC for Selecting Regression Models: Formula, Examples
Are you grappling with the complexities of choosing the right regression model for your data? You are not alone. When working with regression models, selecting the most appropriate machine learning model is a critical step toward understanding the relationships between variables and making accurate predictions. With numerous regression models available, it becomes essential to employ robust criteria for model selection. This is where the two most widely used criteria come to the rescue. They are the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). In this blog, we will learn about the concepts of AIC, BIC and how they can be used to select the most appropriate machine …
Linear Regression Datasets: CSV, Excel
Linear regression is a fundamental machine learning algorithm that helps in understanding the relationship between independent and dependent variables. It is widely used in various fields for predicting numerical outcomes based on one or more input features. To practice and learn about linear regression, it is essential to have access to good quality datasets. In this blog, we have compiled a list of 17 datasets suitable for training linear regression models, available in CSV or easily convertible to CSV (Excel) format. I have also provided a sample Python code you can use to train using these datasets. List of Dataset for Training Linear Regression Models The following is a list …
Pearson vs Spearman: Choosing the Right Correlation Coefficient
Are you as a data scientist trying to decipher relationship between two or more variables within vast datasets to solve real-world problems? Whether it’s understanding the connection between physical exercise and heart health, or the link between study habits and exam scores, uncovering these relationships is crucial. But with different methods at our disposal, how do we choose the most suitable one? This is where the concept of correlation comes into play, and particularly, the choice between Pearson and Spearman correlation coefficients becomes pivotal. The Pearson correlation coefficient is the go-to metric when both variables under consideration follow a normal distribution, assuming there’s a linear relationship between them. Conversely, the …
Pearson Correlation Coefficient: Formula, Examples
In the world of data science, understanding the relationship between variables is crucial for making informed decisions or building accurate machine learning models. Correlation is a fundamental statistical concept that measures the strength and direction of the relationship between two variables. However, without the right tools and knowledge, calculating correlation coefficients and p-values can be a daunting task for data scientists. This can lead to suboptimal decision-making, inaccurate predictions, and wasted time and resources. In this post, we will discuss what Pearson’s r represents, how it works mathematically (formula), its interpretation, statistical significance, and importance for making decisions in real-world applications such as business forecasting or medical diagnosis. We will …
t-distribution vs Normal distribution: Differences, Examples
Understanding the differences between the t-distribution and the normal distribution is crucial for anyone delving into the world of statistics, whether they’re students, professionals in research, or data enthusiasts trying to make sense of the world through numbers. But why should one care about the distinction between these two statistical distributions? The answer lies in the heart of hypothesis testing, confidence interval estimation, and predictive modeling. When faced with a set of data, choosing the correct distribution to describe it can greatly influence the accuracy of your conclusions. The normal distribution is often the default assumption due to its simplicity and the central limit theorem, which states that the means …
First Principles Thinking using ChatGPT
Have you ever wondered why an object such as a chair is shaped the way it is, or why it’s even needed in the first place? What mystery unravels when we dig into the very essence of everyday objects and concepts around us? Navigating through a universe having well-established beliefs and customary wisdom, the hunt for innovative answers and deciphering the secrets hidden behind the everyday becomes not just a curiosity, but a necessity. This is where first principles thinking comes to the rescue. I have posted a detailed blog on First principles thinking – First principles thinking: Concepts & Examples. In this blog, let’s explore how we can utilize …
Generative AI Framework for Product Managers: Examples
Ever wondered how you as a product manager can stay ahead in the competitive era fuelled by technological advancements such as generative AI? Are you constantly grappling with the pressure to deliver groundbreaking solutions in line with your business goals? As a product manager, wouldn’t it be revolutionary to have some kind of a playbook that simplifies these challenges? What exactly can generative AI do for modern product managers? Which areas of your daily struggles can it alleviate, and what AI frameworks are best suited for your unique challenges? In this blog, we will dive into the potential of Generative AI vis-a-vis real-life use cases for product managers. Use Cases: …
I found it very helpful. However the differences are not too understandable for me