10 Practices I Left Behind to Master the Art of Data Science

<p>Hey there, fellow data enthusiasts! I&rsquo;m Gabe A, and today, I want to take you on a journey through my data science career, highlighting the ten practices I&rsquo;ve shed along the way to become the Python and data visualization expert I am today. Over the past decade, I&rsquo;ve been fortunate enough to dive deep into the world of data, and I&rsquo;m excited to share with you the lessons I&rsquo;ve learned.</p> <h1>1. Manual Data Entry</h1> <p>In the early days of my data science journey, I used to spend hours manually entering data into spreadsheets. It was mind-numbing work that left little room for actual analysis. Today, I embrace automation with Python scripts to fetch, clean, and preprocess data effortlessly. Here&rsquo;s a snippet of a simple data fetching script:</p> <pre> import pandas as pd # Fetch data from a URL url = &#39;https://example.com/data.csv&#39; data = pd.read_csv(url) # Now you have your data in a DataFrame</pre> <h1>2. Ignoring Version Control</h1> <p>Once upon a time, I didn&rsquo;t think version control was necessary for a data scientist. But I soon realized that keeping track of code changes is crucial. Git and platforms like GitHub have become my best friends. Here&rsquo;s a basic Git workflow:</p> <pre> # Initialize a Git reposito</pre> <p><a href="https://medium.com/@araujogabe1/10-practices-i-left-behind-to-master-the-art-of-data-science-e598b2428852"><strong>Learn More</strong></a></p>