Data science is the practice of collecting and analyzing data in order to better understand situations and make decisions. From corporate intelligence to statistical analysis, data scientists can specialize in a wide range of fields within the discipline. Learning more about the many areas of data science might be helpful whether you're a data scientist or want to be one. This article explains what a data scientist does, examines 11 different sorts of data science careers, and offers practical advice on how to become one.
What does a data scientist do?
Data scientists utilize the data to address issues and assist others in making wise judgments. A data scientist may be in charge of certain duties, for example:
- Develop and use data models
- cooperating with other experts
- Display visual information.
- review emerging data science technologies
- synthesize information and data
Data scientist salary and job outlook
The salary a data scientist may expect to make may vary based on their performance, experience, and education. Data scientists make an average yearly income of $103,043 in the United States. Computer and information research experts may anticipate a 22% growth rate in their sector, which is substantially faster than the national average, according to the Bureau of Labor Statistics.
Also, check scope of data science in future here.
11 types of data science specializations
These are 11 types of data science specializations you can pursue:
1. Business intelligence-
Data scientists that work in business intelligence acquire and analyze data with the goal of educating company executives. Businesses are able to make decisions that will lead to success thanks to the information they examine throughout business intelligence data operations. Business intelligence data scientists, for instance, can compile data on rivals' performance to help executives plan.
2. Cloud computing
Delivering services, storage, and databases through the internet is known as cloud computing. Data scientists concentrate on collecting and interpreting information held in cloud databases. In cloud computing data storage sites, they also aid in organizing and storing data.
3. Cybersecurity analysis
Information on the internet and internal information security of a business is gathered by cybersecurity data scientists. To ascertain how simple it is for outside sources to get information, they evaluate safety statistics. Data scientists in cybersecurity can assist other cybersecurity experts in ensuring the confidentiality and security of an organization's information.
4. Data engineering
The process of designing and developing systems that collect and store data is known as data engineering. Additionally, they develop software that can synthesis information from raw data into materials that allow scientists to reach conclusions. Engineers also create systems that provide several employees in a business access to data.
5. Data mining
Software is used by data scientists that specialize in data mining to identify patterns in massive data sets. In order to find out more about accuracy, they might look for correlations and anomalies. Data miners can forecast results and recognize trends by looking for patterns.
6. Data visualization
Data visualization specialists concentrate on taking unprocessed data and presenting it to their peers in a way that makes sense. To show how data compares and what it implies, they could use graphs and figures. For instance, a data visualization specialist may develop a presentation to demonstrate to a corporate executive how a new product might fare on the market.
7. Data warehousing
Data management includes data warehousing. Some data scientists concentrate on creating and developing data warehouses to contain massive volumes of data. For those who work in business intelligence, data warehouses are quite beneficial.
8. Machine learning
In data science, machine learning refers to the process of instructing machines and algorithms how to act in certain ways. A data scientist with expertise in machine learning utilizes facts and formulas to teach machines how to respond to issues and situations. They create artificial intelligence that can enhance manufacturing and design processes.
9. Market data analytics
Scientists that study how goods and services operate in markets are known as market data analysts. They might compile data on how certain situations effect revenue. Additionally, they can forecast the performance of brand-new goods or services and aid in the preparation of leaders for potential global events.
Also check, pros and cons of data science here.
10. Operations data analytics
This kind of business analysis is concerned with how to incorporate data into daily operations. An operations data analytics specialist puts more of an emphasis on developing tools based on analyses than just trying to interpret data and forecast patterns. For instance, a specialist in operations data analytics can recommend modifying supply chain management based on data.
11. Sector-specific
Data scientists that specialize in a certain industry are individuals who work in that industry. Health care data analysts who learn more about the population's health and treatment patterns might fall under this category. A social media expert who focuses on developing new strategies to draw users to an app based on their usage patterns and approval ratings is another example of a sector-specific data scientist.