What Skills Are Required for Data Analytics?

• Data analysts help organizations better understand their customers and markets.
• Data analysts provide solutions to organizational problems.
• Required technical skills include data visualization and programming language fluency.
• Essential soft skills include communication, problem-solving, and critical thinking.

Organizations everywhere use data to learn more about their customers, target markets, and competition. With data analytics, businesses can better understand how their customers buy their products, the effectiveness of their marketing campaigns, and the demand for their products and services. Data insights can also help improve operations and prevent companies from taking unnecessary risks.

Valuates Reports projects 13.5% growth for the big data and business analytics market between 2021 and 2030, totaling about $684 billion by the turn of the decade. The power and insights available from data analytics have created more business applications and a greater demand for data professionals.

So, what is data analytics? We break down the discipline and explore what it might take to secure a career in data analytics. We also examine the skills professionals need to succeed in this field, including technical skills and soft skills.

What Is Data Analytics?

Data analytics is the science of using data to generate insights for decision-making purposes. With the help of powerful data systems, databases, and tools, data analysts can mine, format, and interpret a massive amount of information quickly and effectively. They evaluate historical information, look for trends and patterns, and use algorithms and machine learning to identify and prepare for many possible scenarios.

According to IBM, there were over 2.3 million job listings for data professionals in the country in 2015. Depending on their specific roles and goals, these professionals may work on database systems, create mining and analytical parameters, and analyze the results. Data analysts also work with programmers and leadership to optimize their efforts and communicate their findings through reports, visualizations, and presentations.

Data Science vs. Data Analytics

Comparing data science vs. data analytics can be challenging because they overlap in many ways. In general terms, data science is the overarching field that encompasses data analytics. In professional terms, data science can be thought of as the more advanced version of data analytics.

While data analysts primarily find solutions for known organizational problems from limited datasets, data scientists identify unforeseen risks and make forecasts using predictive modeling and data from multiple sources. However, these two terms and professions may be used interchangeably in many cases.

Data Analytics Technical Skills

Data analysts come from many backgrounds, but certain technical skills can help data professionals stand out. Having and demonstrating abilities in these areas will help you secure employment in the field and perform in most entry-level roles.

Data Visualization

Data visualization refers to the process of turning analyzed data into a visual representation, such as a chart, graph, picture, video, or map. When visualized, data analytics become more accessible, more interesting, and more communicable for many people.

Data visualization is not just an extension of data analytics; it can help generate further questions for future analysis. Visualization also helps people better understand the data and can highlight the need for other types of information and areas of investigation.

Data Cleaning and Preparation

Before the data gets analyzed, it needs to be prepared. Data cleaning is one of the first steps in this preparatory process. Data professionals need to examine the raw data, look for errors, duplicates, and missing values, double-check the rows and columns, and confirm the data mining process was successful.

Without proper preparation, the analytical side will be affected. Unclean data can lead to inaccuracies in the analysis and unclear conclusions. Cleaning and preparation can also save time and avoid complications as a result of mixed messaging.

Programming Languages

Data professionals may need to work with various programming languages for analysis and visualization. Perhaps the most popular language in this field, Python offers built-in data-processing tools for pattern recognition and analysis. It also offers a powerful library of graphical options and visualization tools.

The R programming language is another popular choice for analysts, as the language and environment offer various functions perfectly suited for statistical computing. R can be used for data storage and statistical modeling. The graphical capabilities lend themselves to visualization applications.

MATLAB is used primarily for plotting, manipulating, and analyzing data. The programming language and platform allow data professionals to pull data from various sources, clean and prepare the information, analyze it, and visualize it.

Database Languages

Database languages allow data professionals to manage data and databases. SQL, for example, enables analysts to extract data, create tables and views, and perform complex analyses. Users can also create their own tools and dashboards.

Additionally, data professionals may want to learn to work with NoSQL, databases that store data differently than traditional relational databases. NoSQL databases offer flexible schemas and powerful scaling options. These non-tabular databases work best when working with graph, document, and key-value databases.

Linear Algebra and Calculus

Data science has several math prerequisites, but linear algebra and calculus are most important. Data professionals should understand linear algebra, for example, to work with matrices and vectors and their operations. This allows them to represent large amounts of information, understand the properties, and analyze it all.

Calculus is another requirement, as this discipline allows professionals to compute probabilities and mark the rate of change. Most professionals need familiarity with differential and inferential calculus.

Microsoft Excel

Proficiency in Microsoft Excel might seem a basic requirement, but data analysts use the software regularly. Not only is it popular at most organizations, but the software offers powerful analytical features. Users can sort, filter, and format data. They can also create tables, charts, and other visualizations.

Excel features numerous functions specifically for data analysts, such as the vlookup, pivot tables, concatenate formulas, what if formulas, and lookup feature. Excel also offers a solver function and Analysis Toolpak for data analysts.

Data Analytics Soft Skills

Data analysts rely on more than just technical skills. These professionals need interpersonal skills to work and thrive as part of a team and an organization. Here, we list the top data analytics soft skills, which also align with the best soft skills for tech jobs.

Problem Solving

The primary job duty for many data analysts is to solve organizational problems. They investigate issues and look for insights by examining data. These professionals need to know what kind of questions to ask and how to ask them. They also need to know how to best approach the data and determine which type of analysis to perform.

Critical Thinking

Critical thinking is another important soft skill for a data analyst. These professionals need to take an unbiased approach when addressing how results can help their organizations. They need to identify the importance of the data and analysis, be aware of any blindspots and how they might impact results, and understand what additional questions should be asked.

Writing and Communication

Data analysts rely on their writing and communication skills when presenting their findings. Communication skills come in handy when working with data suppliers, programmers, analysts, and management. Data professionals need to know how to frame their findings and present them.

Frequently Asked Questions About Data Analytics

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