30 Sep · 6 min read
A data analyst interview sheds light on the heart of any company's statistical analysis. The handling of data is the foundation of a company's strategic algorithms. The admiration for data analysts has reached new heights as the popularity of data analytics and data manipulation has grown over the years. Many people disagree about the role of a data analyst, but they are in charge of all statistical analysis required to make strategic business decisions.
So, a data analyst may be required to perform a variety of tasks in addition to statistical analysis, depending on the company. The role becomes more specialized as the business grows. A data analyst from a software company, for example, will perform duties that may sound dissimilar to a data analyst from a finance company.
A Data Analyst is someone who collects and analyses data using various tools designed for this purpose (e.g., MS Excel, QlikView, Microsoft Business Intelligence, Tableau, RapidMiner), and then uses it to prepare or improve a company's business strategies and marketing plans. A Data Analyst is a professionally trained individual with strong mathematical and analytical skills, as well as the ability to think logically. Data Analyst is one of the most important positions today, as most businesses are constantly looking to expand or improve their profitability. A Data Analyst's most important job is to assess real-world risks, or more specifically, to analyze statistical data to assess the opportunities and risks of taking certain actions.
Typical responsibilities include, but are not limited to, extracting data using BI (Business Intelligence) technology and data analysis, coordinating with other members of the IT team(s), and communicating with stakeholders. A Data Analyst may also be hired to correct critical errors, conduct a preliminary analysis, or conduct financial research and analysis. An employee can gain experience in the field of data analysis in a variety of ways, depending on the position chosen and the scope of activities.
In short, a data analyst uses a variety of skills to analyze data collected from customers, the web, smartphones, sensors, and other sources (e.g. statistics, computer science, and business knowledge).
Because data science tools differ depending on the role, company, or industry, we learned to focus on skills rather than tools (like Python, R, SQL, Excel, Tableau, Power BI, etc.).
When hiring a data analyst, you will need to consider if the candidate has these particular skills.
The following are the top data analyst skills:
According to research, data cleaning and preparation will account for roughly 80% of the work of most data professionals. As a direct consequence, this skill is crucial.
A data analyst will frequently be required to retrieve data from multiple sources and prepare it for numerical and categorical analysis. Data cleaning also entails resolving missing and inconsistent data, which may have an impact on analysis.
Data cleaning isn't always exciting in data analytics, but it can be fun and challenging when approached as a problem-solving exercise so it is an important skill for your company.
It may seem strange to include "data analysis" in a list of required data analyst skills, but the analysis is required.
Data analysis is fundamentally concerned with taking a business question or a need and analyzing relevant data to develop an answer to that question.
Exploration is another type of data analysis. Data exploration entails searching for interesting trends or relationships in data that may be useful to a business.
A business question may guide exploration, but it may also be unguided. Looking for patterns in data may lead to the discovery of a business opportunity to reduce costs or increase growth.
Another important data analyst skill is the ability to communicate in multiple formats. Communication skills such as writing, speaking, explaining, and listening will help you succeed in any data analytics role.
When working with colleagues, communication is essential. In a kickoff meeting with business stakeholders, for example, careful listening skills can help you understand the analyses they require.
Similarly, they will be required to explain a complex topic to non-technical teammates during your project.
Written communication is also crucial, especially when writing a summary of your analysis or explaining a discovery made during data exploration.
Data visualizations help to explain data trends and patterns. Humans are visual creatures, which means that most people will understand a chart or graph faster than a spreadsheet.
Colors and patterns catch human attention. We quickly distinguish between colors like red and blue or shapes like circles. Everything in our culture is visual, from art and ad campaigns to TV and movies. Data visualization is another form of visual art that captures attention and keeps it there. When we look at a chart, we can quickly identify trends and outliers. When we see something, we quickly internalize it. It's a narrative with a purpose. If you've ever looked at a massive spreadsheet of data and couldn't find a trend, you understand how much more effective visualization can be.
This entails producing clean, visually appealing charts that will assist others in comprehending your findings. It also implies avoiding items that are either difficult to interpret (such as pie charts) or can be misleading (like manipulating axis values).
Visualizations can also be useful in your data exploration. When you only look at the numbers, you can miss things you can see visually in the data.
In your company, data analysts will be responsible for empowering others to use data to make critical decisions. By creating dashboards and reports, they will remove technical barriers that prevent others from accessing critical data.
This could be as simple as a chart and a table with date filters, or as complex as a dashboard with hundreds of interactive data points.
Understanding topics specific to your industry and company is referred to as domain knowledge.
For example, if your company has an online store, then your staff may need to understand the complexities of e-commerce.
In contrast, they might need to know how those particular systems work if they are analyzing data about mechanical systems in your company.
Statistics and probability are important data analyst skills. This knowledge will guide your company’s data analysis and exploration, as well as can help assist you in deciphering the data of your company.
Understanding statistics will also help to ensure the validity of the analysis and avoid common fallacies and logical errors.
The precise level of statistical knowledge required will vary according to the demands of your company and its data.
If your company relies on probabilistic analysis, for example, you'll need a data analyst who has a much more rigorous understanding of those areas than you would otherwise require.
As the demand for Data Analysts grows, an increasing number of people are choosing to develop or retrain. They are currently among the most in-demand professionals on the market. Based on the information in this article, you can see why Data Scientist has been named one of the most important professions in the twenty-first century.