Human beings have been analyzing data since the dawn of civilization. The drive to measure and study the world around us has proven pivotal for history’s greatest innovations. Now, as artificial intelligence accelerates the creation of data to unfathomable heights, the ability to analyze data will become increasingly vital.
Enter data analysts, the professionals responsible for collecting, processing, and analyzing data to provide insights that drive business decisions.
Overview of Data Analytics
A data analyst’s primary role is to analyze data to uncover patterns, trends, and insights that can help businesses make informed decisions. They collect, clean, and organize data from various sources and use statistical analysis tools to create reports and visualizations that highlight key findings. Data analysts work closely with business stakeholders, such as marketing teams, product managers, and executives, to understand their requirements and provide them with data-driven recommendations.
On a more technical level, the core job responsibilities of data analysts include:
- Writing high-quality code
- Collecting, processing, cleaning, and organizing data
- Analyzing data to identify patterns and trends
- Creating data visualizations and dashboards
- Presenting findings to stakeholders
- Conducting experiments and A/B tests
- Collaborating with cross-functional teams
- Keeping up-to-date with advancements in technology
What Kinds of Companies Hire Data Analysts?
Companies such as Google, Microsoft, Amazon, and Facebook rely heavily on data analysis to improve their products and services.
Finance and Banking
Banks, investment firms, and insurance companies use data analysts to monitor and analyze financial data, make predictions and manage risk.
Hospitals, medical research institutions, and pharmaceutical companies hire data analysts to analyze patient data, clinical trial results, and research outcomes.
Retail and E-commerce
Retail and e-commerce companies hire data analysts to analyze customer behavior, sales data, and marketing trends to improve their products and services.
Government and Non-profit Organizations
Government agencies and non-profit organizations use data analysts to analyze large data sets and make data-driven decisions.
Manufacturing and Logistics
Manufacturing and logistics companies hire data analysts to optimize production processes, analyze supply chain data, and identify areas for cost reduction.
Types of Data Analyst Positions
Data analyst job titles vary widely, depending on experience, specialization, and industry.
Early career-level professionals will typically start their career with an entry-level title like junior data analyst or data analyst I. They’ll typically work in that role for one to three years, gaining experience and domain expertise.
A data analyst’s title may also vary depending on the industry they work in. Industry-specific job titles include:
- Business Intelligence Analyst
- Marketing Analyst
- Financial Analyst
- Healthcare Analyst
- Operations Analyst
- Data Science Analyst
From there, they may have the opportunity to move into more senior-level roles with more hands-on experience, such as senior data analyst or lead analyst. While they spend several years honing their skills, their responsibilities expand to include taking more ownership of projects, working independently in a team environment, and mentoring project team members.
With some experience under their belt, a data analyst often faces a crossroads in their career. The first path is to pivot into people and team management functions, where hiring, mentoring, resource planning and allocation, strategy, and operations become a larger component of their role. The other possible career path is to continue as an individual contributor, where they can develop deeper technical expertise in various technology languages and frameworks.
Requirements to Become a Data Analyst
Data analysts use several programming languages and frameworks to collect, process, analyze, and visualize data. The choice of programming language depends on the type of analysis required, the size and complexity of the data, and the individual preferences of the analyst.
Python is one of the most popular programming languages for data analysis. It has a large and active user community and is widely used in scientific computing and data analysis. Python has several libraries and frameworks useful for data analysis, including Pandas, NumPy, Matplotlib, and Scikit-learn.
R is another popular programming language for data analysis. It has a comprehensive set of libraries and packages that make it ideal for statistical analysis and data visualization. R is particularly useful for working with large datasets and conducting advanced statistical analysis.
SQL (structured query language) is a programming language used to manage and manipulate relational databases. It is commonly used for data analysis, particularly in industries such as finance and healthcare, where data is stored in databases. SQL is useful for querying, manipulating, and aggregating data, and for creating complex reports and data visualizations.
MATLAB is a programming language commonly used for numerical computing, data analysis, and data visualization. It has a wide range of toolboxes and functions for signal processing, statistics, and machine learning. MATLAB is particularly useful for scientific computing and data analysis in fields such as engineering and finance.
Julia is a high-performance programming language designed for numerical and scientific computing. It has a simple syntax and is easy to use for data analysis, machine learning, and other scientific applications. Julia is particularly useful for working with large datasets and conducting complex statistical analysis.
Tableau is a popular data visualization tool that allows users to create interactive dashboards and reports. It provides a wide range of built-in visualization options and a drag-and-drop interface for creating custom visualizations.
Microsoft Excel is a powerful tool that data analysts use for a variety of tasks. Some of the ways data analysts use Excel include:
- Data cleaning
- Data visualization
- Data analysis
- Pivot tables
Microsoft’s Power BI is a powerful data visualization and business intelligence tool that’s tightly integrated with Excel. Data analysts use Power BI to analyze data, create interactive dashboards, and share insights with others.
SAS (Statistical Analysis System) is a software suite that data analysts use to manage, analyze, and report on data. Key functionalities in SAS include data management, statistical analysis, data visualization, machine learning, and reporting.
Mathematics & Statistics
Beyond programming, data analysts also need to be skilled in mathematics and statistics. Competency in the following subjects is key:
- Linear Algebra
Technical competency alone isn’t enough to succeed in a data analyst role. Soft skills are a must in any data analysis role, Soft skills that are important for data analysts include:
- Time management
- Project management
- Problem solving
Experience & Education
After competency, the most important qualification for data analysts is experience. For most employers, on-the-job experience and training is a critical requirement.
Then, there’s the question of education. 65% of data analysts have a bachelor’s degree, and 15% have a master’s degree. If you’re hiring data analysts, there’s a high likelihood that many of them will have a degree. And many companies still require data analysts to hold four-year degrees. However, many employers are broadening their candidate searches by prioritizing real-world skills.