Data analyst jobs are highly sought-after. In 2019, IBM predicted that there would be more than 2.7 million employment openings for data analysts by 2020, with. Aapproximately 40% of those positions require a master's degree or higher in advanced data analytics. The average starting pay for data analyst jobs at entry-level is around $60,000. Earnings for senior positions might approach $135,000.
According to Technopedia, a data analyst is "a professional who works with data to provide insights. These individuals translate numbers into plain English – they take raw or unstructured data and come up with analyses that produce digestible results that executives and others can use to make decisions.” A data analyst gathers, cleans, models, and interprets data to solve a specific problem and assist in decision-making.
What are the key responsibilities of a Data Analyst Job?
1. Gathering of raw data:
A data analyst gathers data through surveys, website analytics, social media monitoring and so on. This step also includes reorganizing the data from primary and secondary sources so that both humans and machines can read it.
2. Data cleaning:
Raw data usually has inaccuracies, duplicates, or outliers. A data analyst is responsible for maintaining data accuracy in a spreadsheet or through specific programming languages to avoid incorrect or skewed interpretations.
3. Modelling Data:
A data analyst models data by creating and designing databases. This is the stage where the data analyst decides on the data elements to save or discard to save or discard, how to tie different data types to one another, and how the data will look.
4. Interpreting data:
A data analyst evaluates data and responds to questions at hand by looking for patterns or trends in the data. This also involves analyzing data sets with statistical tools to help with diagnostic and prescriptive analytics.
5. Presenting data:
A significant portion of a data analyst job will involve presenting the outcomes of one’s research. Typically, this entails some form of data visualization, such as creating graphs and charts. Additionally, a data analyst produces interactive reports, presentations, documents, and dashboards. They will be creating executive leadership reports that use pertinent data to communicate trends, patterns, and projections. In addition to adequately interpreting results, a data analyst also needs to communicate the results to non-technical staff.
6. Decision making:
Data analysts also develop data governance standards while working with programmers, engineers, and corporate leaders to identify potential process improvement areas.
Skills required for a Data Analyst Job
The skills required for a data analyst job differ in specific ways based on their role. For instance, it is crucial to understand the industry you work in. However, you can pick this up on the job.
A study by Mckinsey states that data analyst jobs require people with the following skills:
- Analytical skills with the capacity to gather, arrange, examine, and communicate a substantial amount of information with precision and care.
- Technical skills in data models, database design and development, data mining, and segmentation approaches.
- Numerical skills are essential for data measurement and statistical analysis.
- Understanding statistics and practical expertise using statistical software for dataset analysis (Power BI, Excel, SPSS, SAS etc.)
- Basic machine learning knowledge: Nobody will expect you to be an expert in machine learning. Nevertheless, many data analytics jobs are supported by machine learning principles.
- Critical thinking: The capacity to critically analyze the information in front of you may be the most crucial talent in data analytics. A data analyst needs to approach projects using logical thinking and deduction, must have a naturally curious mindset, and should not accept anything at face value.
- Ethics: A data analyst must uphold a strict code of ethics. Without it, data can be readily abused, which may hurt the people and groups impacted by one’s work in the real world.
According to McKinsey, data analysts also need a solid foundation of business knowledge and technical abilities, including problem-solving, communication, and decision-making. Another crucial data analyst ability is attention to detail, which helps to minimize errors and maximize the effectiveness of data analysis.
Tools for a data analyst
Data analyst jobs require people to use a wide range of tools to improve the accuracy and effectiveness of their work during the data analysis process. The following are some of the most popular tools in the data analytics sector:
- Microsoft Excel: MS Excel is a requirement for any data analyst job. This enables a data analyst to sort data, divide it into smaller subgroups, and has many other inbuilt functions. These tools include pivot tables, XLOOKUP and VLOOKUP search engines, the AVERAGE function (which displays the average of a specified range of integers), and the SUMIF function (which lets you calculate the sum of different cells). These features, together with a great number of additional functions, make Excel a crucial piece of software for data analysts.
- SQL: A data analyst uses SQL to pull, add, or change data without understanding the language used to create the database. SQL (sometimes pronounced "sequel") is a computer language that was created with relational databases in mind.
SQL is becoming increasingly important to master, especially for individuals who are not analysts, because most businesses now store information digitally or online. It is even more crucial for those who operate in the data analysis industry.
- R or Python: Another popular programming language in data analytics is R. Despite being more challenging than Python, R is used extensively today because of its long history in statistical programming (which has benefits in a field like data analytics). For jobs like image processing, R lacks Python's ease of use, but it has more built-in data analytics features. It is frequently employed in scientific domains. R includes a software library similar to Python called CRAN that provides a substantial number of add-on packages.
Python can be used for almost all stages of the data analytics process. Pandas, for instance, excel at handling time series and other types of quantitative data. Matplotlib is ideal for displaying data. Furthermore, NumPy is renowned for handling a wide range of challenging mathematical operations.
- Databases and management systems: Understanding databases’ and data warehouses' workings is essential for data analytics. For instance, MySQL is a well-liked and relatively simple to use relational database management system. Data Analysts also use Apache Hadoop, which is a more complex design that uses distributed databases to store, manage, and process enormous amounts of data.
Industry-specific data analyst job tools
Along with the previously mentioned technologies, the industry is beginning to develop increasingly complex sector-specific applications to enable data analytics. These tools include general business intelligence programs like Microsoft Power BI to dashboards and data visualization tools like Tableau.
Need to know more? Look at this list of the best tools for data analysts.
This article covered everything you need to know about a data analyst job. We have looked at a data analyst job, the skills required, and the tools used. To better understand what a data analyst job involves, you can try this 5-day analytics course for free.
Nicole Chimwamafuku is Finance, Strategy and Performance Management Consultant at Industrial Psychology Consultants (Pvt) Ltd, a business management and human resources consulting firm.
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