Data Visualization with Seaborn: 7 Steps Guide to Create Scatter Plot
In today’s world, data is more than just numbers—it’s a story waiting to be told. With tools like Python and Seaborn, you can transform raw data into visually appealing and insightful plots that help you make data-driven decisions. This blog walks you through a hands-on example of creating a professional scatter plot using Pandas, Seaborn, […]
Files Oragnizer with Python: A Step-by-Step Guide to Automate File Management(10 steps)
Introduction Python offers an efficient solution to organize files as in today’s digital world, the files quickly pile up in our computers, ranging from images to documents, audio, and video files. Manually organizing these files into folders is time-consuming and repetitive, especially when we can leverage the power of the OS module of Python to […]
Data Visualization with matplotlib: Build Scatter Plot in 7 easy steps
In this blog post, we’ll walk through a simple data visualization example using Matplotlib in Python. We aim to create a scatter plot showing the relationship between the total bill and tip amounts from a dataset. This example will help you understand how to create meaningful visualizations step-by-step, adjust aesthetics like size and colors, and […]
Data Visualization with matplotlib: Build Bar Chart in 6 easy steps
Data visualization is the graphical representation of information and data. It helps in identifying trends, patterns, and outliers that might go unnoticed in a plain data table. Bar charts, line graphs, histograms, and pie charts are some popular ways to visualize data. In Python, we use powerful libraries like Pandas and Matplotlib to: In the […]
Data Visualization with matplotlib: Build Line Chart for Retail Sales Analysis in 7 easy steps
In this blog on data visualization, we will walk you through the process of reading and visualizing data using Python’s library Matplotlib for visualization. By the end of this tutorial, you’ll have a clear understanding of how to load data, create a comparison plot, and annotate the graph with important information. Step 1: Importing Required Libraries […]
7 Essential Data Science Applications in E-commerce “
Data Science In today’s digital-first world, e-commerce is no longer a luxury—it’s a necessity. The pandemic has accelerated the growth of online shopping, and with that growth comes an immense amount of data. It is estimated, that global e-commerce sales are projected to reach $6.3 trillion, and data science is playing a pivotal role in […]
For Every Future Data Analyst: 7 Essential Tips
Data Analyst is one of the most exciting and rapidly growing profiles today. With massive data being generated every second, companies are on the hunt for skilled data analysts who can turn raw data into actionable insights. However, when people think about data science, they usually picture code, algorithms, and complex models. While technical skills […]
30 Essential Data Science Interview Questions from Python & SQL One Must Know
Are you preparing for a data science interview? Mastering Python, Pandas, and SQL is critical if you want to stand out. These tools are essential for data analysis, data manipulation, and querying databases, making them a must-have skill set for any data scientist. In this blog, we’ll cover 35 of the most important interview questions […]
Build Daily Price Tracker on Decathlon in Python: Web Scraping, Email Automation & Scheduling in 4 steps
Python Automation can play an important role if you know how to leverage it. In today’s e-commerce-driven world, online shopping has become a routine part of life. However, finding the best deals or price drops on your favorite products can be a bit of a hassle. Wouldn’t it be great if you could get an […]
How Data Science takes advantage of 1 Levenshtein Algorithm for Auto-Correct Product Search
In the world of data science, where accuracy and efficiency are key, ensuring data consistency is critical. One of the challenges data scientists often face is handling inconsistencies in textual data. Whether it’s misspelled user inputs or slight variations in product names, these small differences can lead to bigger issues, affecting data quality and outcomes. […]