Several areas to choose from in data science

Today, industries use data science so prolifically that the demand for data scientists has also increased. Data analysts are those professionals who collect and analyze unstructured data and find information that will aid in strategic decision making.

The data analytics business is increasing its revenue every year, not only domestically, but also participating in the export of analytics to countries such as the US, UK and Australia. And it has always been seen that when an industry expands exponentially, so is its need for human resources and, in this case, data scientist.

Data science as a career option has many other subgroups. You have many activities in your data cycle and generally have different experts working on them.

DATA SCIENCE BRANCH

Data science as a field is divided into different areas and is handled by the respective experts.

• Data engineering– Involves formatting the raw data in an accessible way, includes managing storage, data source, quality, and structure maintenance. This makes the analysis easy and one can easily find the details related to it. Jobs in this area are data engineer, database developer.
• Cloud computing and architecture: involves maintaining and developing the infrastructure necessary for cloud management. In addition, it ensures that the analyzes are integrated with applications and commercial uses. Jobs related to this area are Platform and Cloud Engineer, Cloud Architect.
• Database managment: this area involves the maintenance and development of databases according to their need in data transactions during different uses. Jobs related to this area are data specialist, database engineer, and architect.
• Data processing: This involves exploring the data using different statistical analyzes. This helps build predictive models for various business problems and their future trends. Jobs related to this area are a business analyst, statistician.
• Business Intelligence– This involves managing data sources, finding analytical solutions, communicating with stakeholders, designing tests, and documenting. Jobs related to this area are data strategist, BI analyst, engineer, and BI developer.
• Machine learningThis involves obtaining inputs for algorithms and designing data cycles, testing hypotheses and data infrastructure. This area usually makes use of standard data tools and different statistical models. Jobs related to this area are cognitive developer, machine learning specialist, and artificial intelligence specialist.
• Data visualization: This involves presenting ideas in a visually appealing way. The design of graphical interfaces and attractive designs for the client is the main agenda here. The job related to this area is a software developer and data engineer and developer.
• Data analytics– This involves troubleshooting and looking for patterns and opportunities in the data scene. Analytics can be based on a market or sector or internal operations. Jobs related to this area are communications, planning, decisions, web, market, product, sales analysts.

SKILLS REQUIRED TO BE A DATA SCIENTIST

To be successful in any profession, you need to have certain skills to complement your interests, similar is the case in data science. Some necessary skills are.

• Education: To be a data scientist, you need a background in math, computer science, or statistics.
• R programming: 45% of data science problems can be solved with this specific compilation tool.
• Python coding– It is one of the most versatile coding languages ​​that can work on any data format and can import any type of data sets from external sources.
• Hadoop: Although it is not the most used, it can be of great importance in certain cases when the volume of data exceeds the memory of the system and it is necessary to transfer it. It is also widely used for filtering, sampling, and data summarization.
• SQL encoding– one must know how to code and execute complex queries in SQL.
• Apache Spark– It is almost similar to Hadoop, but it is faster and can prevent data loss.
• Machine learning: It is used in predictive analysis and algorithm construction and involves reinforcement and adversary learning, decision tree, logistic regression, etc.