Category Archives: Data Analysis
Approximate Sparsity Explained: Why should we use Lasso with high dimensional data?
Approximate sparsity refers to the situation in a high-dimensional regression model where only a small number of predictors (regressors) have significant (large) coefficients, while the majority of predictors have coefficients that are either zero or very close to zero. This concept is crucial in high-dimensional settings, where the number of predictors pp is large, often…
Singular Value Decomposition (SVD): Definitions and Applications In Python?
Introduction Singular Value Decomposition (SVD) is a fundamental technique in linear algebra with numerous applications in data science, machine learning, and various scientific fields. This comprehensive guide delves into the mathematical foundations of SVD, its importance, and its practical applications, providing intuitive examples to help you understand this powerful tool. 1. What is SVD? Mathematical…
Understanding OLS in High-Dimensional Settings: Insights and Practical Implications
In the world of data science and machine learning, linear regression stands as a foundational tool for predictive modeling. Despite its simplicity, its proper implementation, especially in high-dimensional settings, demands a nuanced understanding. This blog post dives into the intricacies of linear regression, focusing on how dimensionality impacts wage gap estimates and the challenges associated…
Detailed Explanation of Partialling-Out and the Frisch-Waugh-Lovell (FWL) Theorem
Partialling-Out Partialling-out is a technique used in regression analysis to isolate the effect of a specific variable (regressor) on the outcome by removing the influence of other variables (control variables). This helps us understand the true relationship between the target regressor and the outcome. Summary
Python for Data Analysis: A Brief Book Review From a Personal Perspective
“Python for Data Analysis” by Wes McKinney serves as an introductory guide for those venturing into the world of data analysis using Python. It aims to furnish readers with a solid foundation in Python’s data analysis libraries, such as Numpy, Pandas, Matplotlib, and Seaborn. These tools are the bedrock of data manipulation, visualization, and analysis…
Basics of Generating Date Ranges and Resampling in Python
The world is full of data that changes over time, from stock prices to weather patterns. This kind of data is called time series data, and analyzing it requires special techniques. This blog post takes a look at the chapter on time series data in the book “Python for Data Analysis” by Wes McKinney. We’ll…
Mastering Data Analysis with Pandas GroupBy Function
Pandas, the popular Python library for data manipulation, offers a powerful tool for data analysis: the groupby function. This function allows you to group data based on specific columns and perform various operations on each group. Let’s explore different ways to leverage groupby for effective data analysis. 1. Aggregating by a Custom Function: Imagine you…
Mastering Complex Data with Pandas: Advanced read_csv Arguments
Welcome data enthusiasts! Today, we delve into the advanced functionalities of Pandas’ read_csv function, equipping you to handle even the most challenging datasets. Often, real-world data throws curveballs, but fret not! With the following arguments, you’ll be reading complex CSV files like a pro. 1. Handling Datasets Without Column Names: By default, read_csv assumes the…