# Category Archives: Data Science

## Generate Random Numbers & Normal Distribution Plots

In this blog post, we’ll be discussing how to generate random numbers samples from normal distribution and create normal distribution plots in Python. We’ll go over the different techniques for random number generation from normal distribution available in the Python standard library such as SciPy, Numpy and Matplotlib. We’ll also create normal distribution plots from these numbers generated. Generate random numbers using Numpy random.randn Numpy is a Python library that contains built-in functions for generating random numbers. The numpy.random.randn function generates random numbers from a normal distribution. This function takes size N as in number of numbers to be generated as an input and returns an array of N random …

## Pandas: Creating Multiindex Dataframe from Product or Tuples

MultiIndex is a powerful tool that enables us to work with higher dimensional data, but it can be tricky to create MultiIndex Dataframes using the from_tuples and from_product function in Pandas. In this blog post, we will be discussing how to create a MultiIndex dataframe using MultiIndex from_tuples and from_product function in Pandas. What is a MultiIndex? MultiIndex is an advanced Pandas function that allows users to create MultiIndexed DataFrames – i.e., dataframes with multiple levels of indexing. MultiIndex can be useful when you have data that can be naturally grouped by more than one category. For example, you might have data on individual employees that can be grouped by …

## Top Python Statistical Analysis Packages

As a data scientist, you know that one of the most important aspects of your job is statistical analysis. After all, without accurate data, it would be impossible to make sound decisions about your company’s direction. Thankfully, there are a number of excellent Python statistical analysis packages available that can make your job much easier. In this blog post, we’ll take a look at some of the most popular ones. SciPy SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering. SciPy contains modules for statistics, optimization, linear algebra, integration, interpolation, special functions, Fourier transforms (FFT), signal and image processing, and other tasks common in science and …

## Covariance vs. Correlation vs. Variance: Python Examples

In the field of data science, it’s important to have a strong understanding of statistics and know the difference between related concepts. This is especially true when it comes to the concepts of covariance, correlation, and variance. Whether you’re a data scientist, statistician, or simply someone who wants to better understand the relationships between different variables, it’s important to know the difference between covariance, correlation, and variance. While these concepts may seem similar at first glance, they each have unique applications and serve different purposes. In this blog post, we’ll explore each of these concepts in more detail and provide concrete examples of how to calculate them using Python. What …

## Import or Upload Local File to Google Colab

Google Colab is a powerful tool that allows you to run Python code in the cloud. This can be useful for a variety of tasks, including data analysis and machine learning. One of the lesser known features of Google Colab is that you can also import or upload files stored on your local drive. In this article, we will show you how to read a file from your local drive in Google Colab using a quick code sample. There are a few reasons why you as a data scientist might need to learn how to read files from your local drive in Google Colab. One reason is that you may …

## Ridge Classification Concepts & Python Examples

In machine learning, ridge classification is a technique used to analyze linear discriminant models. It is a form of regularization that penalizes model coefficients to prevent overfitting. Overfitting is a common issue in machine learning that occurs when a model is too complex and captures noise in the data instead of the underlying signal. This can lead to poor generalization performance on new data. Ridge classification addresses this problem by adding a penalty term to the cost function that discourage complexity. This results in a model that is better able to generalize to new data. In this post, you will learn about Ridge classifier in detail with the help of …

## PCA vs LDA Differences, Plots, Examples

Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two of the most popular dimensionality reduction techniques. Both methods are used to reduce the number of features in a dataset while retaining as much information as possible. But how do they differ, and when should you use one method over the other? As data scientists, it is important to get a good understanding around this concept as it is used in building machine learning models. Keep reading to find out with the help of Python code & examples. How does PCA work? Principal Component Analysis (PCA) works by identifying the directions (components) that maximize the variance in a dataset. …

## Pandas Dataframe loc, iloc & brackets examples

Pandas is a powerful data analysis tool in Python that can be used for tasks such as data cleaning, exploratory data analysis, feature engineering, and predictive modeling. In this article, we will focus on how to use Pandas’ loc and iloc functions on Dataframe, as well as brackets with Dataframe, with examples. As a data scientist or data analyst, it is very important to understand how these functions work and when to use them. In this post, we will work with the following Pandas data frame. Use loc and iloc functions to get Rows of Dataframe The loc function is used to get a particular row in a Dataframe by …

## Pandas: How to Create a Dataframe – Examples

One of the most popular modules for working with data in Python is the Pandas library. Pandas provides data structures and operations for working with structured data. A key concept in Pandas is the Dataframe. Learning how to create and use dataframes is an important skill for anyone including data analysts and data scientists working with data in Python. In this post, you will learn about how to create a Pandas dataframe with some sample data. What is Pandas Dataframe? A Pandas dataframe is a two-dimensional data structure, like a table in a spreadsheet, with columns of data and rows of data. Dataframe is analogous to a table in SQL …

## Central Limit Theorem: Concepts & Examples

The central limit theorem is one of the most important concepts in statistics. This theorem states that, given a large enough sample size, the distribution of sample averages will be approximately normal. This is a huge deal because it means that we can use the normal distribution to make predictions about populations based on samples. In this article, we’ll explore the central limit theorem in more detail and look at some examples of how it works. As data scientists, it is important to understand the central limit theorem so that we can apply it to real-world situations. What is the central limit theorem and why is it important? The central …

## Probability concepts, formulas & real-world examples

Probability is a branch of mathematics that deals with the likelihood of an event occurring. It is important to understand probability concepts if you want to get good at data science and machine learning. In this blog post, we will discuss the basic concepts of probability and provide examples to help you understand it better. We will also introduce some common formulas associated with probability. So, let’s get started! What is probability and what are the different types? Probability is a concept in mathematics that measures the likelihood of an event occurring. It is typically expressed as a number between 0 and 1, with 0 indicating that an event is …

## Statistics – Random Variables, Types & Python Examples

Random variables are one of the most important concepts in statistics. In this blog post, we will discuss what they are, their different types, and how they are related to the probability distribution. We will also provide examples so that you can better understand this concept. As a data scientist, it is of utmost importance that you have a strong understanding of random variables and how to work with them. What is a random variable and what are some examples? A random variable is a variable that can take on random values. The key difference between a variable and a random variable is that the value of the random variable …

## Histogram Plots using Matplotlib & Pandas: Python

Histograms are a graphical representation of the distribution of data. In Python, there are several ways to create histograms. One popular method is to use the Matplotlib library. In this tutorial, we will show you how to create different types histogram plots in Python using Matplotlib. As data scientists, it is important to learn how to create visualizations to communicate our findings. Histograms are one way to do this effectively. What are Histogram plot? Histogram plots are a way of representing the distribution of data. A histogram is made up of bars, with each bar representing a certain range of data values. The height of the bar indicates how many …

## How to Create Pandas Dataframe from Numpy Array

Pandas is a library for data analysis in Python. It offers a wide range of features, including working with missing data, handling time series data, and reading and writing data in different formats. Pandas also provides an efficient way to manipulate and calculate data. One of its key features is the Pandas DataFrame, which is a two-dimensional array with labeled rows and columns. A DataFrame is a table-like structure that contains columns and rows of data. Creating a Pandas DataFrame from a NumPy array is simple. In this post, you will get a code sample for creating a Pandas Dataframe using a Numpy array with Python programming. Step 1: Load …

## Ensemble Methods in Machine Learning: Examples

Machine learning models are often trained with a variety of different methods in order to create a more accurate prediction. Ensemble methods are one way to do this, and involve combining the predictions of several different models in order to get a more accurate result. When different models make predictions together, it can help create a more accurate result. Data scientists should care about this because it can help them create models that are more accurate. In this article, we will look at some of the common ensemble methods used in machine learning. Data scientists should care about this because it can help them create models that are more accurate. …

## Learning Curves Python Sklearn Example

In this post, you will learn about how to use learning curves using Python code (Sklearn) example to determine machine learning model bias-variance. Knowing how to use learning curves will help you assess/diagnose whether the model is suffering from high bias (underfitting) or high variance (overfitting) and whether increasing training data samples could help solve the bias or variance problem. You may want to check some of the following posts in order to get a better understanding of bias-variance and underfitting-overfitting. Bias-variance concepts and interview questions Overfitting/Underfitting concepts and interview questions What are learning curves & why they are important? Learning curve in machine learning is used to assess how models will …