Top data analytics skills you need to get hired in 2023

Companies have more data than ever at their disposal. They need employees capable of using technology to capture, analyze, and leverage the information they extract. Demand for business and data analysts has increased significantly, with over 160,000 jobs posted over the last 12 months. It will grow by 17% over the next decade.

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What is a business or data analyst?

Business and data analysts gather information from various sources and interpret patterns and trends. They prepare reports that summarize their discoveries and provide recommendations that drive decision-making for their organizations. 

What skills do you need to get hired as a Data Analyst in 2023?

Soft skills Data Analysts need

1. Critical Thinking – to gain insights from data, you need to know what to ask in the first place. Successful analysts can solve complex problems by identifying which questions to ask, knowing what data to collect, and how to process it to extract the appropriate information or uncover connections. 
2. Problem-solving – When analysts run up against roadblocks or technical issues, they should be capable of finding an effective solution through problem-solving. For example, you run up against a technical problem and troubleshoot by researching quirks of particular software or coding language.  
3. Communication & Presentation – You will be expected to communicate with various stakeholders across all departments with little experience with data or the technology you use. You must write up your findings and recommendations or provide insight that will influence an organization’s strategies clearly and concisely. Communication across different mediums (email, PowerPoint presentations, reports) is essential. 
4. Attention to detail – As an analyst, you need to focus because analyzing involves reading and assessing intricate coding or technical structure. You can’t afford to miss important information.
5. Collaboration – You will be working with internal and external stakeholders frequently. Therefore, respecting and appreciating teamwork can help you complete your day-to-day tasks. Internally, cross-functional collaboration amongst data scientists, analysts, predictive modelers, and engineers will be required to ensure well-rounded business insights are delivered to executive leadership. 

Technical skills required for Data Analysts

1. 1. SQL – Structured Query Language (SQL)  is a programming language that communicates with a database to allow an analyst to view and manipulate the data stored inside the database; Analysts use SQL to query and manage data stored in databases. The SQL programming language is one of the best methods for collecting, storing, and organizing information obtained during data analysis. Learning SQL is vital if you want to become a business or data analyst or work with data. Fortunately, it is one of the easier programming languages to learn.
   2. Microsoft Excel/ Google Sheets – Some companies use other spreadsheet software. It’s important to learn how to use traditional, widespread spreadsheet tools. 
   3. Python or R Programming Knowledge – In addition to SQL, more analysts are using Python or R. These programming languages are used for statistical purposes and to create machine learning algorithms. Python is one of the most popular languages for completing tasks such as data cleaning, modeling, and crawling. You will need to learn how to use these programming languages. 
   4. Data Visualization – Spreadsheets can be overwhelming to review during meetings where you present your findings. Skilled analysts will create clear and compelling charts that organize data and are easier to interpret. To perform this skill, you must learn to use Tableau. Its visualization software is considered the industry-standard analytics tool and is user-friendly. 
   5. PowerBI – Another business intelligence tool you may need to be well-versed in is Power BI. It is a collection of software services, apps, and connectors that combine to turn your unrelated data sources into coherent, visually immersed, and interactive insights. 
   6. Data Warehousing is creating virtual storage and organization systems to host data. Analysts will manage these systems, monitor the data, and ensure no security issues.

Top Data Analytic skills for working in machine learning and artificial intelligence industries

The applications of machine learning and AI are vast and varied. They are used in a wide range of industries, including finance, healthcare, retail, and transportation, to name just a few. Some examples of how these technologies are being used include:

Data analysts who want to work with machine learning and artificial intelligence (AI) need to have a strong foundation in a number of key skills. These skills include:

1. Programming: For data analysts who work with machine learning and AI, proficiency in at least one programming language is required. Languages such as Python and R are widely used for data analysis and machine learning and are often the first choice for data scientists and analysts who want to build and deploy machine learning models.
2. Statistics and statistical analysis: Data analysts who work with machine learning and AI need to be familiar with statistical concepts and techniques such as hypothesis testing, regression analysis, and probability. These skills are essential for understanding how machine learning algorithms work and for interpreting the results of those algorithms.
3. Data wrangling: Data analysts who work with machine learning and AI will often be responsible for preparing data for analysis and modeling. This may involve tasks such as gathering and collecting data from various sources, cleaning and preprocessing the data to remove errors and outliers, and transforming the data into a format that is suitable for machine learning algorithms.
4. Data visualization: Data analysts who work with machine learning and AI should be skilled in creating visualizations to help stakeholders understand the insights that have been uncovered through the data. This may involve creating charts, graphs, maps, and other visual elements to represent data in a clear and concise way.

The demand for machine learning and AI skills is only expected to grow in the coming years, as more and more companies look to incorporate these technologies into their operations. If you’re interested in pursuing a career in data analytics, it’s worth considering investing in some training or education in these areas. With the right skills and experience, you could be well on your way to a rewarding and lucrative career in this exciting field.

How to develop data analyst skills

Now that you know what data analyst skills you need to break into the field, let’s discuss the key to a successful career: education. Suppose you want to transition into a business or data analyst career or even score a promotion. In that case, there are many ways to start. If you are new to data analytics, we recommend researching the industry. It would help if you began to familiarize yourself with the terminology and explore different career paths. We encourage you to seek out connections already employed as analysts within your professional network. You can learn more about the role through informational interviews and better understand the job requirements. 

Once you have done your research, you will need to master the technical data analyst skills to build a successful career. Enrolling in a data analytics certificate program is one of the most efficient ways to build a strong foundation and practice your new skills. The Tableau Data Analytics Certificate Program is designed to build a solid theoretical foundation and prepare our students with hands-on projects to apply their new data analyst skills. It yields the same results faster, cheaper, and in less time than going back to college or starting a boot camp. We also provide career guidance to help graduates get jobs in this highly competitive field.