Data Science Trends & Salaries in 2023
<p>Data science is one of the coolest fields in recent years. Many people from different backgrounds have transitioned into this field. But, is this trend still ongoing?</p>
<p>Today, we’ll handle the data science salaries 2023 dataset and explore trends in data science with data visualization techniques. Here are topics we’ll cover in this blog:</p>
<ul>
<li>Top 10 job titles in 2023</li>
<li>Experience levels in data science</li>
<li>Employment types</li>
<li>Salaries by job titles</li>
<li>Salaries by employment types</li>
<li>Salaries by work years</li>
</ul>
<p>Let’s start out by loading the dataset.</p>
<h1>Loading the Dataset</h1>
<p>The dataset we’ll be using is the <a href="https://www.kaggle.com/datasets/arnabchaki/data-science-salaries-2023" rel="noopener ugc nofollow" target="_blank">data science salaries 2023</a> dataset on Kaggle.</p>
<p><img alt="" src="https://miro.medium.com/v2/resize:fit:630/1*dC2n4hmuQgWnsEUDkzTWEg.png" style="height:412px; width:700px" /></p>
<p><a href="https://www.kaggle.com/datasets/arnabchaki/data-science-salaries-2023" rel="noopener ugc nofollow" target="_blank">The data science salaries 2023 dataset</a></p>
<p>What we’ll do first is load this dataset with Pandas. This library is an awesome library for performing data manipulation.</p>
<pre>
import pandas as pd
df=pd.read_csv('ds_salaries.csv')</pre>
<p>Ok, we have loaded the dataset. Let’s go ahead and try to understand the dataset using several methods in Pandas.</p>
<p><a href="https://levelup.gitconnected.com/data-science-trends-salaries-in-2023-57b75df39d32">Read More</a></p>