How to Become a Data Analyst in 2023: Insights from a former data professional

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:

1. Data Collection and Cleaning: Data analysts gather and organise data from multiple sources, ensuring its accuracy and reliability.

2. Data Analysis and Interpretation: They apply analytical techniques to extract meaningful insights, identify trends, and make data-driven recommendations.

3. Data Visualisation: Data analysts create visual representations, such as charts and graphs, to communicate complex findings effectively to stakeholders.

4. Data Quality Assurance: Data analysts ensure data integrity, perform quality checks, and identify anomalies or errors in the data.

What technical hard skills are employers looking for?

1. 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.

2. 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.

3. 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.

4. 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?

1. Tertiary Institutions: such as universities and colleges, offer comprehensive degree programs in fields like data science, statistics, computer science, or business analytics. These programs provide a solid foundation in data analytics principles, statistical analysis, machine learning, and data visualisation. Pursuing a degree can provide a deep understanding of the subject and often includes hands-on projects and research opportunities.

2. Bootcamps: Data analytics bootcamps are intensive, short-term programs that focus on practical, industry-relevant skills. These bootcamps are designed to quickly equip participants with the necessary knowledge and tools to enter the job market as data analysts. They often offer hands-on training, mentorship, and project-based learning, enabling learners to build a portfolio of real-world projects. Bootcamps are a popular choice for individuals seeking a career transition or looking to upskill in a shorter timeframe compared to traditional education.

3. Organisations: Major technology companies like Microsoft, Amazon Web Services (AWS), Google, and IBM offer certifications and training programs in data analytics. These programs are specifically tailored to their respective platforms and tools, such as Microsoft Power BI, AWS Analytics Services, Google Analytics, and IBM Watson. They provide in-depth training on using the company's analytics tools, data manipulation, visualisation, and cloud-based analytics solutions. These certifications can enhance your credibility and demonstrate expertise in a particular technology ecosystem.

4. Online Platforms: provide a flexible and accessible way to learn data analytics. Websites like Coursera, edX, Udacity, DataCamp, and LinkedIn Learning offer a wide range of courses and specializations in data analytics, data science, and related fields. These courses are often created by top universities and industry professionals, providing high-quality content and hands-on exercises. Online learning platforms allow you to learn at your own pace, making them suitable for individuals with busy schedules or those who prefer self-paced learning.

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.

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!