As large language models (LLMs) have demonstrated their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc., many data analysts may raise concerns if their jobs will be replaced by artificial intelligence (AI). This controversial topic has drawn great attention in public. However, we are still at a stage of divergent opinions without any definitive conclusion. Motivated by this, we raise the research question of "is GPT-4 a good data analyst?" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. We propose a framework to tackle the problems by carefully designing the prompts for GPT-4 to conduct experiments. We also design several task-specific evaluation metrics to systematically compare the performance between several professional human data analysts and GPT-4. Experimental results show that GPT-4 can achieve comparable performance to humans. We also provide in-depth discussions about our results to shed light on further studies before reaching the conclusion that GPT-4 can replace data analysts.

Dailymotion is the leading video discovery destination & technology that learns about your tastes over time, constantly surfacing the best, most relevant content on the web. Our mission is to provide the best video user experience for consumers on the market, connecting publishers and advertisers to engaged viewers who turn to Dailymotion for their daily fix of the most compelling music, entertainment, news, and sports content around. Through partnerships with the world's leading publishers and content creators, France Télévisions, Le Parisien, CBS, Bein Sports, CNN, GQ, Universal Music Group, VICE and more, Dailymotion commands 3 billion monthly pageviews across its mobile app, desktop and connected TV experiences. Dailymotion is owned by Vivendi, one of the largest mass-media corporations in the world. We build the best place for people to enjoy the videos that matter.

Professional development: As the gaming industry is constantly evolving and growing, we can promise you will, too! We will ensure you have a structured and smooth onboarding process with scheduled feedback times and both a mentor and a buddy by your side. To make sure you and your team are up to date with the latest trends, we allocate a team budget for education. Financial benefits: On top of a competitive salary, we offer additional financial benefits based on annual success, such as a Christmas bonus, Easter bonus, summer bonus, 13th salary, as well as referral bonuses and fully covered transportation expenses. Work-life balance: We are deeply dedicated to our work, but also understand the importance of switching off and recharging.

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time. The Junior Data Analyst must be passionate about data science and genomics. They will be responsible for the analysis, processing, and delivery of genomic data. They should have demonstrated troubleshooting skills and a strong attention to detail.

Podsights is a small, distributed organization seeking a junior data analyst to join our growing team! Here's a little about us, a little about what we believe, and what we are looking for. Podsights is an industry-leading attribution platform for podcast advertising. We likely work with some of your favorite brands and publishers and handle over 10 billion events a month. Our mission is simple, we are looking to grow the podcast industry.

I think there's a rule somewhere that says "You can't call yourself a data scientist until you've used a Naive Bayes classifier". This article is my attempt at laying the groundwork for Naive Bayes in a practical and intuitive fashion. Let's start with a problem to motivate our formulation of Naive Bayes. Suppose we own a professional networking site similar to LinkedIn. Users sign up, type some information about themselves, and then roam the network looking for jobs/connections/etc. Until recently, we only required users to enter their current job title, but now we're asking them what industry they work in.