How to do Research in Data Science?

In today’s world, as Software engineers, we all want the highest-paying job. Most of the research from different organizations, institutions, academics, and many more are denoting Data Science, Data Analysis, and Data Engineering. According to my wisdom, day by day, data is growing exponentially. The time is quite close when we may run out of memory. If you check around, the number of digital devices is significantly expanding. So, let’s see what exactly data science means and the research scope present in it?

Broadly, an organized study of any form of data is known as data science. Let’s suppose you spent your whole salary. Now, in the end, you are wondering where you had spent all the money? So to solve this, you started analyzing all your spendings. You started writing down all the spendings and making plans. In this case, your spendings are data, and your programs are actions. Hence, we can relate we analyze to make corrective actions for the future. Similarly, we analyze the stock market, financial data, medical data, environmental data, astrological data, and many more to provide some useful insight for the future.

The current generation is building code in artificial intelligence, machine learning, deep learning, time analysis, genetic algorithms, data-driven process, and the rest. The research in this field is pretty much more spacious. I will suggest some of the tools, conferences, and journals to you which will help you to begin research in this area. It will help you find the gaps in the ongoing experimentation, and then you will come across different ideas.

Flow Diagram of Research.

To research effectively, follow the given steps one after another. The most important step among all is to maintain the Diary of Ideas. In this diary, you will write down all the ideas that you are getting at any point. For example, you are attending a conference, and there you got an idea, don’t miss it, write it down immediately in your diary. You never know that idea may help you out. Implement your idea one by one and write down the results, even if the results are not according to your project. Those bad results may help you to defend your thesis or research in the future. So, the wrong findings are also a new finding. If in the end, the idea works, then write your research in words and publish it in reputed journals or conferences.

Important tools:
Tableau: One of the most prominent tools to perform data analysis tasks. Non-computer science people like business administrators, managers, higher-level management, and many more can use this tool effectively. It can connect directly with CSV, database, and extracts. This tool helps to visualize the data with many attractive widgets.

Excel: The most active tool for performing daily life data analysis tasks. It is user-friendly, and everyone can use it. Excel has very elementary functionality like formulation, spreadsheet, group by, sorting, listing, and many more.

SAS: It is a statistics tool employed in many scientific activities. SAS uses its language, which is called SAS language. It consists of many data science libraries by which we can easily organize and model the data in perfect shape for the organization.
Jupyter NoteBook

Jupyter Notebook: If you are familiar with python and know some of its data science libraries, then the Notebook is perfect for data analysis. Popular libraries are Pandas, NumPy, Matplotlib, Scipy, TensorFlow, PyTorch, OpenCV, and many more.

Important Conferences:
ODSC: The most popular conference in the field of data science is Open Data Science Conference. Many countries like Boston, San Francisco, Brazil, London, and India annually organized this conference. At this conference, renowned researchers have discussed novel ideas and experiments. World-famous companies also participate in this conference, so it is the best exposure point for data science enthusiastic beginners.

ICML: International Conference of Machine Learning is an academic conference. It has scheduled preplan up to 2023 in various countries. It is organized and managed by the International Machine Learning Society. Many top universities participate in this conference and showcase their research.

NeuIPS: Conference on Neural Information Processing System is organized every December in various countries. From different corners of this world, Renowned lecturers took part in this conference. This conference gives you enough knowledge to accelerate and boost your wisdom in data science.

Other: Joint Statistical Meetings, Strata Data Conference, Python Conference, Conference on Innovative Data Systems Research, and many more.

Important Journals:
Data Mining and Knowledge: Springer Science published this journal bi-monthly wise. It is one of the famous and subscribed journals of data science. It has a wide area of research and novel ideas in the current era.

Data in Brief: It is published by Elsevier. it has a high impact factor and a high citation score. Many academic researchers target high-impact factor journals and conferences.

Other: PNAS, Journal of the ACM, Scientific Data, Journal of Machine Learning Research, Order, Statistics and Computing, and many more.

The Research is Endless, We may stop the person but not the Research.

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Rishipal Singh

He is a Research Scholar of the National Institute of Technology, Jalandhar. He has published many research papers in reputed conferences and journals