As a profession, this term is relatively new; a data scientist helps the organization develop strategies and analyze data efficiently, and not only analyze everything related to data, such as procuring, preparing, exploring, and envisaging data to build business models. These business models also require executing programming languages, such as Python, HTML, etc., and convert these models to applications.
For this purpose, one would require to complete an MSc data science online course to become a specialist in this domain and work in big multinational firms, as these firms have already started to understand the importance of data for effective and reaping business activities. Data scientists usually work and analyze in teams to make it more productive. And it’s not that you will only find a data scientist in such teams. They are a group of business and strategic analysts, data engineers, and application or web developers. They all have individual roles and responsibilities to deliver analytic output.
As this is a relatively young concept, there are not many people who are studying or dreaming of working in this niche domain. Many universities and colleges have started offering various courses to cater to these needs, especially for those who are already working in the IT sector and can sign up for such courses to upgrade and grow in their current careers.
So this is how one can learn and upgrade themselves in the following way:
- Collaborative engagement and interactive discussions which are based on real-life industry projects
- Specific cases that require one-on-one learning experience with some proficient mentors from the industry
- To provide a great experience and facilitate improvement, the mentors are open to subjective feedback and interaction.
Both public and private companies require candidates for such job roles. The course focuses on the following specialized topics:
- Research Design: This basically deals with planning the way forward that will be followed during the analytical process of research which is done for large sets of data. Especially when one is dealing with big data, all the eyes are majorly on the output. If the output seems right, only then is the process not questioned. Hence, the process should be correct to reach the desired output, which is managed under research design.
- Data Cleansing: The next step after a process is finalized is to identify which kind of data is appropriate and which is incorrect. The data should be accurate and complete, and there should not be missing parts to help the team to reach an accurate conclusion for the business. This sets the foundation of data science as a whole.
- Data Engineering: For people to analyze and work on data, there should be a proper system to collect the right kind of data, which data scientists and business analysts can then use to interpret and derive workable solutions.
- Data Mining and Exploring: Professionals that are engaged in the work of data mining usually set and create parameters and directions for mitigating their way through large sets of data and identify certain trends that can be compared for improvement. Exploring data is initially done to establish a relationship between two variables on which then data mining takes place.
- Data Visualization: Visualizing data is a creative process of creating a graphical or illustrative representation of the data presented to make things easy to present and understand without having to go through large data sets. Using bright color pallets and patterns can help distinguish between variables and draw an inference based on the same.
- Information Ethics and Privacy: When you apply for either an offline or online master degree in data science, one of the most important subsets of data science is privacy and understanding of ethics, i.e., to know what is wrong and right. When you’re responsible for these volumes of data, there cannot be any integrity issues and misuse of personal data for any kind of wrongdoing. Hence, this forms an extremely important part of Data science.
- Statistical Analysis: English has a grammar for rules and correct representation of languages, and in the same way, statistics is the grammar of science. From anything to everything to be done with data collection, analyzing data, interpreting, and making inferences from data, every step requires the use of statistics. This can also be in various ways, such as descriptive statistics, inferential statistics, predictive statistics, prescriptive statistics, etc.
- Machine Learning: Now, obviously, we are in the 21st century, and processes are automated and do not rely entirely on humans. One can train machines to become smarter and correct to perform the same process quicker and more efficiently. A particular set of algorithms, along with machine learning, can provide computers or machinery which can enable them to learn from the data without any necessary human intervention.
As you can see, these are just some domains of data science, there is much more to it and can make things interesting for many.