Top 20 NumPy Library Functions You Need to Know
Keith Renfro NumPy is a popular Python library for numerical computing and data analysis. Here are some of the most important NumPy functions used in data science: numpy.array(): Creates a NumPy array…
Intro to Python Libraries – What Are They?
A Python library is a collection of pre-written code that can be used to perform various functions without having to write new code from scratch. Libraries can save time and…
Plot a Linear Regression Using Matplotlib
Here's a simple example of how to plot a linear regression using Python and Matplotlib: import numpy as np import matplotlib.pyplot as plt # Generate some random data x =…
Python Control Structures – If, Loops & Functions
Here are the most used control structures in Python, along with three examples of each: If-else statements - These are used to conditionally execute code based on the outcome of…
Data Types and Structures in Python
Here are the main data types in Python, along with three examples of each: Integer (int) - These are whole numbers, both positive and negative. a = 5 b =…
Top 5 Skills Every Junior Data Scientist Must Know
Here are the five most important things a junior data scientist must know about Python coding: Data Structures: Understanding the fundamental data structures in Python is essential for a data…
Data Exploration in Data Science Using Python Libraries
Today, we will discuss another important step in the data science process - data exploration. Data exploration is the process of getting to know your data by summarizing and visualizing…
Data Cleaning With Python in Data Science
Today we will discuss an important step in the data science process - data cleaning. Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in the data. Data cleaning…
Data Collection in Data Science
Today we are going to discuss data collection in data science. Data collection is a crucial step in the data science process, as the quality and quantity of data collected…
The Data Science Process Life Cycle
The data science process life cycle typically includes the following steps: Problem definition: This involves understanding the business problem or research question that needs to be addressed and defining the…