Data analysts help companies find patterns and trends in business data. Learn about the key skills you’ll need to get hired as a data analyst.
Here’s what we’ll cover:
Becoming a data analyst requires key business intelligence and the ability to harness different perspectives when it comes to analysis. The role is in high demand, and will only continue to become increasingly crucial to running a business. Now is an excellent time to brush up on the skills needed to become a data analyst and learn how to get ahead of other applicants.
In a world spun by Big Data and digitization, the demand for individuals who can harness key information within data science to bring value to a business is skyrocketing. To be a well rounded and successful data analyst, a holistic approach is best. You'll need not only sharp technical skills, but also deploy effective soft skills, a deep understanding of statistical analysis, and strong data visualization capabilities.
The following is a breakdown of the data analyst skills and beneficial tools and programs necessary for getting your first job in data analytics.
The foundation of data analytics is built upon hard skills. These are measurable proficiencies that require a specific way of thinking and understanding raw data and drawing conclusions from it in a way that helps drive business strategy. Data analysts must be strong in the following:
Learning how to use specific data analysis tools such as SQL is crucial to a strong foundation in data-related problem-solving. You should be prepared to understand how to take an issue within a business and turn it into a question that can be answered with data, and then extract the solution by finding trends, relationships, and discrepancies within the dataset.
It’s extremely important to know how to prepare your dataset to be analyzed efficiently and accurately. Data cleaning involves identifying any incomplete, irrelevant, or inaccurate parts of the database and how to proceed with these errors. Skilled data analysts have a well-trained eye that can recognize blips in the system and have the awareness of how to solve any problems before analysis.
The ability to wield your mathematical skills proves crucial in analyzing and understanding data. A deep understanding of statistics and probability will allow the discovery of any trends or discrepancies that may appear. Though not always explicitly listed in a data analyst job description, a solid foundation in linear algebra and mathematics is helpful for answering questions about large amounts of data and driving meaningful insights.
While emotional intelligence may not come to the forefront when considering data analysis, these soft skills are necessary to be a valuable data analyst and get your message across to team leaders, colleagues, and even customers.
To think critically is all about knowing what to ask. In data analysis, finding patterns is the name of the game. Critical thinking allows the analyst to hone in on what counts without getting overwhelmed. A set of data may contain many relationships, errors, and trends, so it’s important to be able to stay centered and keep the main questions in focus while problem-solving.
Written correspondence, presentation skills, and overall communication skills are fundamental for skilled analysts to add to their best practices. An analyst must know how to tell a story with their data that can be widely understood and applied.
To be able to effectively communicate and solve the problem at hand, an analyst needs a deep understanding of the field they are in. That means having a strong research foundation and a commitment to learning.
The following languages, programs, and tools are the keys to unlocking trends and answers within the data.
Microsoft Excel is the jumpoff point for any data analyst. It’s an absolute must in becoming an analyst and is the beginning of understanding data technologies. While much more advanced tools are available, Excel is the most widely used, especially in the startup world. It’s a light and quick tool for analytics.
SQL is Excel’s much stronger big brother. It is an industry-wide mainstay in terms of a database language as it can handle large data sets and can tackle Big Data. It is used to update and communicate with a database, as well as serve as a tool to access and manipulate data.
R and Python are the top two open-source programming languages to provide predictive analytics. The two are pretty much interchangeable, with Python being a more generalized version. They’re fast, advanced, and free! If you don’t have experience with either, there are plenty of online tools available to learn.
A programming language and multi-paradigm numerical computing environment, MATLAB is the final step in advanced data plotting, manipulation, and organization. It’s great for companies interested in Big Data and powerful in machine learning. Machine learning is hot in data science right now as a branch of artificial intelligence, and having a good grasp of its models can put you ahead.
Is data analytics the right career for you?
Springboard offers a comprehensive data analytics bootcamp. Our data analytics curriculum goes beyond just technical skills to focus on areas where employers find the biggest gaps: strategic thinking, problem-solving, and communication. Watch videos from Microsoft. Learn insights from McKinsey experts. Tackle case studies from Harvard Business School. No other data analytics bootcamp does this. You’ll graduate with an analytical mindset. That’s an edge not just for your job search, but throughout your career.
Check out Springboard’s Data Analytics Career Track to see if you qualify.
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