Multiple Areas to Choose From in Data Science
Today data science is being used by industries, so prolifically that the demand of data scientists has risen too. Data analysts are those professionals who collect and analyze unstructured data and find insights which will help in strategic decision making.
Data analytics business is increasing its revenue every year, not just domestically but also getting involved in analytics export to countries like USA, UK, and Australia. And it’s always seen that when an industry spreads exponentially, so is their need for human resources and in this case data scientist.
Data science as a career option has many other subgroups. It has many activities in its data cycle and usually has different experts working on them.
BRANCHING OF DATA SCIENCE
Data science as a field is divided into different areas and handled by respective experts.
- Data engineering: it involves formatting the raw data into an accessible form, includes managing the storage, source of data, quality and structure maintenance. This makes analyzing easy and one can easily find the details related to it. Jobs in this area are data engineer, database developer.
- Cloud computing and architecture: it involves maintaining and developing the infrastructure needed for cloud management. Also, it makes sure that the analytics are integrated with business applications and uses. Related jobs to this area are platform and cloud engineer, cloud architect.
- Database management: this area involves maintaining and developing databases according to their need in data transactions during different uses. Jobs related to this area are data specialist, database engineer, and architect.
- Data mining: this involves exploring the data using different statistical analysis. This helps in building 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 the data sources, finding analytical solutions, communicating with shareholders, test designing and documentation. Jobs related to this area are data strategist, BI analyst, BI engineer and developer.
- Machine learning: this involves getting inputs for algorithms and designing data cycles, testing hypothesis, and data infrastructure. This area usually makes use of standard data tools and different statistical models. Jobs related to this area are a cognitive developer, machine learning specialist, and AI specialist.
- Data visualization: this involves presenting insights in a visually appealing way. Designing graphics interfaces and customer appealing designs is the main agenda here. Job related to this area is a software developer and data engineer and developer.
- Data analytics: this involves problem-solving and finding patterns and opportunities in the data scenario. Analytics can be a market or sector or internal operations based. Jobs related to this area are communications data hk , planning, decisions, web, market, product, sales analysts.
SKILLS REQUIRED TO BE DATA SCIENTISTS
To succeed in any profession one needs to have certain skills to complement their interests, similar is the case of data science. Some needed skills are.
- Education: to be a data scientist one needs to have a background in mathematics, computer or statistics.
- R programming: 45% of data science problems can be solved using this specific build tool.
- Python coding: it is one of the most versatile coding languages which can work in any format of data and can import any kind of datasets from external sources.
- Hadoop: though not the most commonly used, but it can be of major importance in certain cases when data volume exceeds system memory and one need to transfer it. Also heavily used for data filtration, sampling, and summarization.
- SQL coding: one should 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 building and involves adversarial and reinforcement learning, decision treeing, logistic regression etc.