Data Analysis, and Data Science, one of the important questions that coins up is, what are the career options that we can take up in Data Science?
We have learned about the real-life applications of data and data science. Many of us may have found it interesting and may want to pursue this in our career to explore it further.
To help you nail through the right choice, let us understand which different careers we can take up in Data Science.
Some common job titles for data scientists include:
1. Data Scientist
2. Business Intelligence Analyst
3. Data Mining Engineer
4. Data Architect
5. Senior Data Scientist
Let us now briefly go through these job titles to get a better understanding:
1. Data Scientist - Data Scientists are data enthusiasts who gather and analyze large sets of structured and unstructured data. A data scientist's role combines computer science, statistics, and mathematics. They analyze, process, and model data and later interpret the results to create actionable plans for companies and organizations.
Data Scientists are analytical experts who utilize their skills both in technology and social science to find trends and manage data. They use their industry knowledge and context specific understanding to find solutions to business challenges.
2. Business Intelligence Analyst - Business Intelligence Analysts use data to assess the market and find the latest business trends in the industry. This helps to develop a clearer picture of how a company should shape its strategy.
3. Data Engineer - Data Engineer examines not only the Data for their own business but also that of third parties. In addition to mining data, a data engineer creates robust algorithms to help to analyze the data further.
4. Data Architect - Data Architects work closely with users, system designers, and developers to create a blueprint that data management systems use to centralize, integrate and maintain the data sources.
5. Senior Data Scientist - Senior Data Scientists anticipate the business's needs in the future. Although they might not be involved in gathering data, they play a high-level role in analyzing it. Using their vast experience, they can design and create new standards for analyzing data. They can also create ways to use statistical data and develop tools to further analyze the data.
