Data Science is all about using the right mix of data, techniques, tools, and people to create business value for an organization. Machine Learning, Business Analytics can be considered as reincarnations or subsets of Data Science.



As we all know, data science is undoubtedly one of the fastest-growing fields, along with its job opportunities. Companies across various industries are interested in digital transformation, which uses digital technologies to make traditional business processes much better. With data science, most organizations have realized the significance of data-driven decision making. Machine Learning and Business Analytics are subsets of Data Science, and they have their relevance singly.

According to Gartner's recent report, 2.3 million new jobs are estimated by the end of the year 2020. This shows that there is a huge demand in these fields. Though there is a huge demand for people in these industries, the supply is not complementing. According to the report, in 2020, data science and data analytics job requirements are expected to increase by 364,000 openings to 2,720,000. As per a survey conducted by Analytics India Mag and Great Learning, around 97000 analytics, data science jobs are vacant in India.

What is missing?

The main reason for the insufficient supply concerning the high demand is the lack of skills. Most people are unaware of the skills that fall under these disciplines and even assume that getting trained in a few skills is enough to gain expertise, which is not valid. The first and foremost thing that needs to be clarified is the necessary skills for these disciplines and their relationship. The mind map given below provides a solution to all the confusion. The Venn diagrams in the mind map are used to convey the connections between the disciplines. The black circles depict the skills, and the white processes are used to define the prominent techniques.

Business Analytics

Business Analytics focuses on developing new insights and understanding of business performances based on data and statistical modeling. The necessary skills to become an expert in business analytics are advanced visualizations, statistical modeling, SQL modeling, and technologies and tools that make the work easier, and business/domain knowledge.



1) Advanced Visualizations are useful to create visualizations, which helps to understand and draw insights from massive data quickly. Tableau, Power BI, etc., are tools used to create aesthetically pleasing visuals from the data available.

2) Statistical modeling is another necessary skill used to build predictive models using the data available. Building predictive models using population data and sample data are two approaches used in statistical modeling. R is the most common and convenient tool used to build models. 3)SQL Modelling is used for retrieving structural data using queries. MySQL is the most common tool for SQL modeling.

Machine Learning

Machine Learning is the study of computer algorithms that improve automatically through experience. It can also be considered as a subset of Artificial Intelligence as well as Data Science. The Machine Learning algorithms build a model based on sample data to make predictions or decisions without being explicitly programmed. The necessary skill set is Big Data, Mathematical Modelling, tools and technologies, and business/Domain knowledge.



1) Big Data is a field that treats to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be handled by traditional data-processing application software. Hadoop is a commonly used Big Data tool.

2) Mathematical modeling is used to build models. Supervised learning, Unsupervised learning, Optimization techniques, Deep Learning, Reinforcement Learning, and Text analytics/NLP are essential techniques used in mathematical modeling.

Artificial Intelligence



Artificial Intelligence is the technology of simulating human behavior into machines. Since machine learning is a subset of artificial intelligence, all the skills discussed above are necessary. Other areas of expertise in Electronics/IoT, Mechanics/Robotics, and Other Applied Sciences also play a crucial role. As we know that an AI mimics a human being, the machine learning models can be compared to the human brain, which gives commands to other parts of a body. The IoT/Robotics skills are used to build the machine's peripheral parts, which can be compared to the human body parts.

Conclusion

The most important thing that can be understood from the mind map is that though they are interrelated, each field/discipline has its excellence. The Venn diagrams in the above mind map indicate that Data Science is a combination of Business Analytics and Machine Learning. To become an expert in the Data Science field, one must possess all the discipline skills. It also conveys that Machine Learning forms an integral part of Artificial Intelligence. Some other insights drawn from the above mind map are that Business/Domain knowledge and knowing relevant technologies and tools are essential in any field.

These days, the misconception among people is that learning any tool or being able to code is enough to get a job or excel in that field. Knowing how to use a device without having an idea of the concept that needs to be applied concerning a problem is not so cool. Hence, one must master all the skills required in any of the disciplines to become an expert because each skill has its part to play in that field.