75: Want to be a Data Analyst? Learn These Skills w/ Luke Barousse
Sep 13, 2023
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Luke Barousse, data legend, discusses the important skills for aspiring data analysts. Topics include analyzing 1.2M data jobs, essential data skills, more senior skills, and data job titles.
Focusing on Excel, Python, and Power BI can increase the chances of landing a data job.
SQL and Python are a powerful skillset for data engineers and data scientists.
Deep dives
The importance of simplifying the skills needed to become a data analyst
In this podcast episode, Luke Barus discusses the overwhelming amount of skills that aspiring data analysts are told they need to know. Luke emphasizes the need to simplify the skills required to succeed in the field. By focusing on the top three skills, such as Excel, Python, and Power BI, individuals can increase their chances of landing a data job. Luke also mentions the SPN method, a webinar that provides valuable insights into the skills needed for a data analyst position.
Introducing Data Nerd.Tech
Luke Barus introduces Data Nerd.Tech, a web tool that aggregates job postings to determine the top skills required for data professionals. The tool aims to solve the problem of inconsistent skill recommendations by providing data-driven insights into the most sought-after skills for data scientists, data engineers, data analysts, and business analysts. By accessing Data Nerd.Tech, individuals can explore the top skills required for their desired role and location.
The power of SQL and Python in data roles
Luke Barus highlights the importance of SQL and Python in data roles. Based on his analysis of job postings, Luke reveals that a combination of SQL and Python is a powerful skillset, particularly for data engineers and data scientists. He explains that these two skills open up possibilities for careers in data engineering and data science, providing individuals with the ability to work with large datasets and build data pipelines.
Understanding job titles and the need for careful reading
Luke Barus discusses the confusion surrounding job titles in the data field. He emphasizes the importance of reading job descriptions carefully to understand the actual responsibilities and requirements of a role, as job titles can often be misleading or ambiguous. Luke also advises applicants to apply for jobs where they have approximately 60-70% of the required skills, as job requirements are often wish lists and not strict prerequisites.
Join The January Cohort of The Data Analytics Accelerator
Have a goal to become a data analyst in 2025? Let me help. We are launching a new cohort of my 10-week bootcamp on January 13th. We'll teach you the skills, the projects, and the job hunting skill necessary to become a data analyst.