Data Analyst Career Path in 2022: Skills, Roles and Trends
Data practitioners always remain on the search to boost their skills. It’s done to prepare them for a data analyst career path. Due to emerging trends such as machine learning (ML), the position requires highly qualified and experienced individuals, especially since business decisions have switched to intelligent systems. Thus, the public and private sectors now look for influential analysts to derive valuable results based on the raw data.
The purpose of data science is to manipulate available information to help enterprises make intelligent decisions. The primary goal is to interpret and analyze millions of data points. These insights culled from these analytics are then shared with key stakeholders. The end result is to bring innovative steps to the organization.
According to the U.S Bureau of Labor Statistics (BLS), the need for data analysts is expected to grow 25% by 2029.
Last Updated September 2022
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The following article addresses some basics to understand when interested in a data analyst career path.
Data retains a tremendous significance for organizations. They employ it to transform operations and maximize revenue. Experts with knowledge in data analytics utilize various tools and techniques to compile the information. The end product reveals evolving trends to support the business.
Soft skills as a data analyst
A career as a data analyst is a good fit for you if you prefer to deal with raw information. It’s possible to work in a variety of different sectors with this title. Data analysts continue to be more in demand in financial and technical sectors across the globe. The reason is these industries generate nearly 50 times as much data as other fields.
Thus, you want to consider the following soft skills.
1. Analytical expertise
You must have the capacity to analyze a massive amount of raw data and utilize the proper analytical skills to produce useful information for the organization.
2. Communication expertise
A data analyst’s communication expertise enables them to efficiently and simply present results to stakeholders. Regardless of their combined computer science skills, you need to forego complex descriptions in favor of straightforward interpretations.
3. Deep concentration
A good data analyst must focus on the essential details and accordingly verify the outcomes to boost the enterprise.
4. Critical thinking
The analyst must look at data sources from their enterprise and those of competitors. With this critical thinking, they develop innovative techniques to create positive data models.
Top courses in Data Analysis
Critical areas for a data analyst career path
There are several skills a data analyst must have and improve on to achieve a senior-level position as a data analyst. For instance, you need to focus on current technology patterns and the tools to reach your goals. A few of the most significant are discussed below.
1. Microsoft Power BI
Microsoft Power BI is a powerful application to utilize for industries that have vast amounts of data. It’s not only helpful for data analysis but also assists users in generating useful dashboards and effective visualizations. For instance, if you have billions of sales records and relevant data points on products and clients, Power BI presents the compiled results on a single screen.
2. SQL (structured query language)
SQL is considered an industry-standard language for databases. Analysts commonly use it to work with massive data sets in a structured form. This query language allows a data scientist to store, manage, retrieve, manipulate, and relate various data sets.
3. Mathematical expertise
Computer science is based on numbers. Hence, you must have a strong capability to process mathematical data to acquire the desired information.
4. Python/R programming languages
Python and R are the commonly used programming languages within data analytics. They provide frameworks for data crawling and collection. Being skillful in either language helps you to obtain a career in data analytics.
5. Data visualization expertise
Data visualization is also a key skill to learn. It’s used to discuss the straightforward interpretation of data with stakeholders. As mentioned, Power BI is a good tool to utilize for this form of visualization. Knowing this gives you an edge over other analysts.
6. Machine Learning
Machine Learning (ML) is vital for a data analyst. With this blend of artificial intelligence, statistical techniques, and mathematical formulas, ML goes beyond programming a device to do something. Instead, the machine learns how to properly correlate data as it runs the algorithms. Thus, it self-corrects itself through repeated operations.
Securing a position in the industry
To be considered for an entry-level data analyst position, you must have prior experience in the field. This includes skills in statistical techniques, data wrangling, cleaning, and visualization. Web mining, knowledge of SPSS & Weka, and basic programming skills are fundamental requirements to obtain a position.
1. Junior-level data analyst
As an entry-level analyst, one of your responsibilities is to assist senior team members. Massive amounts of raw information must be analyzed. At the same time, data sets require organization along with statistical analysis.
As you progress in your career, you need to develop strong communication skills and attention to detail. The size of the enterprise you work with determines if you need to team up with other specialists.
2. Senior-level data analyst
Senior data analysts handle complicated data systems to discover patterns and trends in the present and future. You also communicate with other stakeholders, keep tabs on key performance indicators, and generate analytical reports. In addition to these responsibilities, senior data analysts perform competitive research and conduct lead surveys for their clients.
Most companies prefer individuals with at least five years of experience for the role. However, proactive education could speed up your journey toward a mid-level data analyst role.
3. Deciding your area of specialization
The goal to make a decision on your specialization is a good idea. For example, if you want to be a statistician, you must have a solid background in the field. Therefore, it’s essential to have a strong understanding of pricing algorithms, forecasting, and inventory management if you want to be a successful data analyst. On the other hand, deciding on a career as a data engineer means you devote more time to programming data architectures.
4. Senior data analyst career paths
Having comprehensive knowledge and more than 10 years of experience allows you to work in data analyst sectors such as business intelligence and data quality management. As a data analyst in the government sector, you assist in the creation and evaluation of surveys on a wide range of topics. You could also work for a university or R&D firm to develop marketing tactics for clients.
Operational research analysts advise firms and solve issues in various fields like logistics, healthcare, and business. This position requires you to take courses in economics, politics, and technology in addition to the ones listed above.