Top Data Analyst Skills

Who is a Data Analyst?

Analytics is one of the most in-demand jobs today in this age of digitization. With data as the most powerful tool for business transformation, organizations are now on the lookout for resources that have an understanding of data mechanics and the ability to interpret hidden trends in them, which can influence business decisions.

The domain of analytics has three components – Business Context, Technology, and Data Science. Data Science involves techniques for statistical and operations research, machine learning, and deep learning algorithm. When we drill deep down Data Science, we see that Data Science calls for both Data Scientists as well as Data Analysts. At the most advanced level, there is little difference between a Data Scientist and a Data Analyst. Both have to handle large datasets, resolve complex problems, and define new machine learning algorithms.

A Data Scientist is responsible for identifying a business problem that has a substantial business value once solved. A Data Analyst addresses those business problems, finds answers to the questions put forward by the data scientist, and presents different perspectives on the problem at hand.

Data Analysts are the people who can identify customer requirements, forecast using predictive analysis, and exceptional clarity in data visualization and presentation to prescribe business decisions.

Required Primary Data Analyst skill sets

1. Analytical Thinking

Specified business requirements need analysis. It requires defining the main business questions, the answers to which need extraction from datasets. Analytical thinking involves figuring out the parameters to consider for defining the range of datasets, analyze them from different perspectives, determine variable dependencies, and derive meaningful information from the results. Analytical thinking is the ability to break down a complex problem into simple components and resolve these components one-by-one.

2. Basic Mathematics & Statistics

Having a mathematical concept is very important in Data Analysis. Mathematical concepts help in logical thinking, identify patterns, and design algorithms. Concepts include Linear Algebra, Calculus, Optimization Theory, and Discrete Math. A proper grasp of statistical methods gives you the ability to collect relevant data, perform the correct analyses, and present the results in the most useful form. This entire process helps analysts to make predictions based on data. Key concepts required in this field are the probability theory, data transformation, regression, classification, statistical computation, and graphics.

3. Programming Skills

The responsibilities of Data Analysts are more inclined towards data crafting and presenting rather than coding. However, without knowing programming languages, a data analyst is not able to put his knowledge into practice. The programming languages in this context are R, Python, Matlab, and SAS. A knowledge of these dominant languages helps a data analyst to perform advanced analytics on large datasets without depending on an addon programming expert. It also proves to be a preferable factor for recruiters.

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4. Databases

In addition to programming languages and as a data analyst, you must have a sound knowledge of databases. You need to understand the concepts of data storage, data warehouses, and data lakes. SQL is the standard for current big data platforms. To work on data, you need to extract it from databases that call for expertise in SQL. It enables you to handle structured data, query them from databases, perform data wrangling, and data preparation. A Data Analyst hence, must possess sound knowledge in RDBMS, SQL Queries, Indexes, Keys, and Tables. Some of the leading platforms to consider are Apache Hadoop, Oracle, MySQL, HiveQL, and Microsoft SQL.

5. Communication Skills & Team Work

It is a non-technical skill but of utmost importance. Data Visualization and Storytelling are essential responsibilities of a Data Analyst. A Data Analyst needs to work with several members of the organization, such as business analysts, software teams, marketing teams, and more. You need to communicate with your audience. Before that, you must be able to acquire all the relevant information required to perform your analysis. Without a fluent ability to communicate, it is also challenging to work as a team.

6. Research:

In order to infer accurate trends and patterns, a Data Analyst needs to have the ability to perform research in the right area up to the right details. A Data Analyst should have the ability to select all relevant and necessary data in order to reach an accurate insight and present a strong argument for the company’s next big decision.

7. Problem-Solving Skills:

Despite all the attention to detail, there are instances when problems arise in algorithms. In such situations, data analysts are expected to use their problem-solving skills, work with the team, troubleshoot what went wrong and provide a solution via data analysis.

Career Prospects

The career path of a data analyst mainly depends upon the employer. Employers can be significant investment firms, healthcare industry, retail, hospitality, marketing, insurance, or technology firms. A few times, Data Analysts are labeled as Information Scientists. It involves working with the organization’s core database infrastructure, thus acquiring additional technical expertise. Government Sectors, insurance, and healthcare are such domains that rely on information scientists heavily for their deep data infrastructures. Job opportunities for Data Analysts are plentiful with a multilane career path.

Data Analysts can have the following career prospects:

• Market Research Analyst
• Actuary
• Business Intelligence Developer
• Business Analyst
• Budget Analyst
• Credit Analyst
• Data Warehousing
• Data Administrators
• Financial Analysts
• Fraud Analyst
• HR Analyst
• Machine Learning Analyst
• System Engineers
• System Analysts
• Strategy Analyst
• Sales Analyst
• Social Media Data Analyst
• Web Analyst

Apart from data analysis expertise in the relevant domain, the basic skill sets required for all of the above careers are the same. They are:

• Mathematical and Statistical Acumen
• Analytical and Problem-Solving Skills.
• An understanding of Statistical Modelling Software
• Communication Fluency
• Attention to Detail

Conclusion

Data Analysts are predicted to be high in demand as they are required across all domains. The study of the origin of data, its possible inferences, distortions, and perspectives, is going to be the job of the future. Unless the entire humanity decides to renounce the use of all data generating processes, it would be an intelligent move to get oneself industry ready in this domain as the skills would be in demand for a long time, and new job roles would be created regularly.

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