How to Become a Data Analyst in 2023: Insights from a former data professional
Douglas Robertson
Recovering data analyst, helping businesses hire top data and analytics talent | Founder @ DR Analytics Recruitment | Head of Operations @ Data Engineer Camp | Perth ??
In today's data-driven world, the role of a data analyst is more critical than ever and becoming a more popular career pathway. To begin the data journey, it is essential to understand the responsibilities of the role and how to develop the necessary technical skills. This article explores the fundamental aspects of being a data analyst, the technical skills employers seek, and where aspiring analysts can acquire these skills. Additionally, we'll delve into the importance of building a project portfolio and data network to enhance your chances of securing your first data analyst job.
I'm a former data analyst having worked for an ASX-listed financial services firm and now own a data & analytics recruitment agency, DR Analytics Recruitment, which gives me insight into both the employer and employee side of the equation!
First, we must understand what being a data analyst entails
A data analyst is a professional who collects, cleans, analyses, and interprets complex data sets to help organisations make informed decisions. They work closely with stakeholders to identify business needs and translate them into actionable insights. Data analysts use various tools, techniques, and statistical methods to uncover patterns, trends, and correlations within the data. Key responsibilities include:
What technical hard skills are employers looking for?
SQL: Proficiency in SQL is crucial for data analysts to retrieve, manipulate, and analyze data from relational databases. It enables them to write complex queries, join tables, and gain insights into patterns and trends within the data.
Business Intelligence Tools: Data analysts should be adept at using popular Business Intelligence tools like Tableau, Power BI, or QlikView. These tools enable them to create visually appealing dashboards and reports, effectively communicate insights, and present data-driven recommendations to stakeholders.
Excel: Excel is a versatile tool that data analysts rely on for tasks such as data cleaning, manipulation, and basic statistical analysis. Proficiency in Excel's functions, pivot tables, and charting capabilities allows analysts to explore and analyze data efficiently.
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Coding Languages (Python, R, etc.): Knowledge of coding languages like Python or R is highly valuable for data analysts. These languages offer extensive libraries and packages for data analysis, enabling automation of tasks, building statistical models, and performing advanced analytics.
Where to learn this skills?
Build a Project Portfolio and Data Network
Building a project portfolio and data network is crucial for getting your first data analyst job. It allows you to demonstrate practical skills by showcasing your ability to apply data analytics techniques in real-world scenarios. A strong project portfolio sets you apart from other candidates, highlighting your initiative, dedication, and passion for data analytics. It provides hiring managers with tangible evidence of your skills, problem-solving capabilities, and the value you can bring to their organisation. Additionally, actively building a data network through collaborations, open-source contributions, and networking events expands your connections in the industry, opening doors to job opportunities, mentorship, and valuable insights.
Get on GitHub and get to networking events!
The Data Analyst Career Pathway
The career path in data analysis typically starts with entry-level positions, such as Junior Data Analyst. With experience and continuous learning, professionals can advance to roles like Senior Data Analyst, Data Scientist, or Data Analytics Manager. Continuous upskilling in areas like machine learning, big data technologies, and advanced statistical analysis can open doors to exciting opportunities in the evolving field of data and analytics.
Data analysts are invaluable assets to organisations seeking to harness the power of data. With their expertise in collecting, analysing, and interpreting data, they enable data-driven decision-making and provide valuable insights. There is only more data being created and there'll be a need for more individuals to analyse it!
Senior Data Analyst | Engineer| Python | SQL | Excel | PowerBI
1 年Great Content, Very well articulated Douglas Robertson.Seems like I am on the train too:). Investigating a problem in Dataset is really important especially posing questions that can be answered with datasets. Both programmatic and visual assessment of data is also a must. Apart from Portfolio in Data ,attending Data networking events is a great advice by you. keep posting such useful stuff.
Data Engineer at INX K2FLY
1 年Can’t believe how little bootcamps and universities emphasise teaching SQL.