Data Science Trends & Salaries in 2023

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? 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: <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> Let’s start out by loading the dataset. <h1>Loading the Dataset</h1> 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. <img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*dC2n4hmuQgWnsEUDkzTWEg.png" style="height:412px; width:700px" /> <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> What we’ll do first is load this dataset with Pandas. This library is an awesome library for performing data manipulation. <pre> import pandas as pd df=pd.read_csv('ds_salaries.csv')</pre> Ok, we have loaded the dataset. Let’s go ahead and try to understand the dataset using several methods in Pandas. <blockquote> You can find the link to the notebook <a href="https://www.kaggle.com/code/tirendazacademy/eda-visualization-on-data-science-salaries" rel="noopener ugc nofollow" target="_blank">here</a>. </blockquote> <h1>Understanding the Dataset</h1> Before making inferences on the dataset, let’s understand this dataset a little. First, let’s take a look at the first five rows of the dataset with the <code>head</code> method. <a href="https://levelup.gitconnected.com/data-science-trends-salaries-in-2023-57b75df39d32">Click Here</a>