Over the last few years, data science has emerged as a popular career option. With organizations realizing the need for data, the demand for data science has grown as well. And we have seen aspirants flock to this field of study.
And why not? The market size for data science has grown exponentially over the last few years. The compound annual growth rate is 27.7% (which is higher than any other industry). In 2021, the data science market size was USD 95.3 billion. It is projected that by 2026, the market size for data science will be USD 322.9 billion.
Now getting back to the topic of data science aspirants, the number has increased. From healthcare to retail, telecom to cybersecurity, marketing to sales, everyone relies on data science to make better decisions.
Data scientists are thus now required to scale up their knowledge. Data science is not only limited to learning about new techniques and tools. An aspect of it now revolves around specializing in certain subdomains to perform better.
While data science is under the spotlight, organizations are looking to hire individuals with a specific understanding of any particular vertical of data science.
This blog talks about data science specializations and which one you should choose.
Specialization 1: Business Intelligence & Strategy Making
This is a specialization preferred by many because most organizations want business intelligence professionals to drive data-backed decisions. Under this domain, the analyst collects massive volumes of data and draws insights from them to help the business propel to its maximum capacity. The idea is that the analyst would cultivate accurate back-end data sources to avoid errors.
This data science specialization allows businesses to solve their critical problems using their collected data. Organizations also use it to analyze the industry trends, understand competition, and predict customer demands.
This specialized data science domain is also responsible for identifying the areas incurring losses to the business and processes that can help improve the same.
After opting for this specialization, the job roles you may get include Business Intelligence Engineer, Business Analyst, Business Developer, and Data Strategist.
Specialization 2: Data Mining & Statistical Analysis
Data science is a vast subject, to say the least. The first specialization in data science hinges on learning from the data. This is where data mining and statistical analysis come to play.
Professionals learn about the best methods to discover data in this specialized domain. They understand the procedure of identifying significant structures in data so that they can map all of them together. This helps them to put together meaningful information.
Data in its raw form is incomprehensive and can be confusing. This specialization teaches professionals to put data into proper structure.
So, in this domain, you learn about ways to analyze data with predictive models. The main goal here is to understand patterns and trends in data. This will then help the data science professionals to look at the problems faced by the business and interpret the questions that need to be asked.
This domain is also responsible for finding solutions to business queries. Various tools and statistical algorithms are used with the data to create predictive models.
The most common job roles that come out of this specialization include that of Statisticians and Business Analysts.
Specialization 3: Data Architecture
With big data becoming big, organizations need to align their data with industry standards.
After the collected datasets are cleaned, it needs to be maintained in a database for future transactions. This specialization ensures that complex data can be kept in proper databases.
The most popular job role for this specialization is that of Database Administrators.
Specialization 4: Data Engineering
Data engineering as a specialization includes converting data into meaningful formats. The idea is to help professionals clean the datasets to make error-free analysis.
Professionals with this data science specialization integrate the data from various heterogeneous sources and structure them to draw meaningful insights for better decision-making.
This specialization includes managing, arranging, storing, and retrieving data for other data science professionals.
The most popular job role for this specialization includes that of Data Engineers.
Specialization 5: Data Visualization
No matter how advanced we become, human civilization still relies heavily on the visual representation of things. And that includes complex data. Now data can be complicated to understand when presented raw. The graphical representation of data is necessary to make it more impactful and ensure that the concerned people understand it.
Data visualization is the specialization domain responsible for depicting data in graphical representation.
This data science specialization uses tools like charts, graphs, tables, infographics, and much more to represent complex data. By presenting data in a visually appealing manner, its impact increases.
This specialization has professionals tweak the data to appear better. The most popular job profile for this specialization is that of Data Visualization Engineer.
Specialization 6: Machine Learning & AI
When it comes to one of the top data science specializations, machine learning and AI is right there. The idea behind this specialization is simple – professionals can develop AI-based algorithms to make decisions.
Although it may seem very similar to data mining specialization, machine learning is a larger and more complex domain. Professionals here not only identify the data but also focus on building and training models, conducting A/B testing, identifying data sources, and benchmarking base systems.
Since machine learning and AI has become a significant part of modern organizations, many rely on this particular data science specialization to increase business efficiency.
Enterprises hire professionals with this particular specialization to build the algorithms and models, given that a machine learning algorithm can help reduce repetitive tasks.
People with this specialization find themselves in job roles such as Machine Learning Engineers, AI Specialists, Data Scientists, and Data Researchers.
Specialization 7: Market Analysis
This vertical is used majorly by the sales and marketing teams. The data is collected from external sources like customer feedback, competitor analysis, and marketing and sales data. This specialization is used chiefly to understand the customer experience, track performance, and find new business opportunities and customers.
Under the market analysis specialization, experts collect data, measure it, and analyze it to manage the business’ performance in the market. The idea is to find better ways to optimize performance and increase return on investment using data.
For a business to do well in the market, it is essential to understand the market traits and customer demands. This will help reduce wasted efforts and money. By analyzing market data like customer preferences and industry trends, companies can better project their products/services.
This vertical uses different tools like paid search marketing and search engine optimization to collect data. This data is then leveraged to make informed decisions.
Aspirants with this data science specialization can work as Product Analysts, Web Analysts, and Market Analysts.
To Conclude
Other than the data science specializations mentioned above, there are a few more. These include operation-related data analysis and cybersecurity data analysis.
Each of these specializations has a particular problem to solve. As we mentioned before, data science is a vast subject. Aspirants usually choose a field based on their interests and preferences. Such choices are provided when opting for data science courses.
AnalytixLabs, in its data science course, offers students with these specializations. You can get in touch with us to learn more about the course curriculum, the domains offered, etc.
