Data science is a trend right now. Many companies have job openings in data science, and many online data science Bootcamp are available as courses. Data science is the most sought-after job right now, and many industries desperately need data scientists. This leads to the question, why are data scientists in such high demand?
The world is always changing rapidly. The internet is at the heart of all technology lovers. The internet is made up of apps, websites, and various other things. All of these websites and apps require information from the user. The more webs and apps change and expand, the more data there is. That's why there's so much talk about big data. Many people believe that data is the new oil or gold mind. In a machine analogy, AI is the machine, and data is the oil that keeps the generator running. The larger the data set, the greater the likelihood of gaining insight from it. The insight can take the form of a prediction or domain knowledge. In this day and age, this insight is critical for a company to stay ahead of the competition. The role of data science is to provide insight. This is why data scientists are in such huge demand.
In general, data science is a field that involves the analysis of data to draw conclusions that are significant for a variety of purposes. Data science is a diverse field, and data Science is an inclusive term for several subjects. You will learn about many different abilities needed for applying data science in the Data Science Venn diagram.
Additionally, there are various jobs involved in data science. Therefore, a data scientist must carry out multiple duties such as data preparation, analysis, modelling, predicting outcomes, among other things. Know more about guide to a career in data science.
Explanation of Drew Conway’s Venn Diagram
The Data Science Venn diagram was suggested to provide a clear understanding of this concept and drew Conway came up with the idea for the Data Science Venn Diagram. The diagram depicts the data science skills required to become a Data Scientist.
Data Science Venn Diagram
He believed that Data Science is primarily composed of three components and represented them in the form of a Venn Diagram indicating their respective roles.
These fundamentals are:
- Statistics and mathematics
- Domain Understanding
- Computer Programming
Data Science combines all these skills and is at the center of this Venn Diagram. The Data Science Venn Diagram depicts how these areas of Data Science interact with one another.
How to Read the Data Science Venn Diagram
To better understand this Data Science Venn diagram, consider how these skills are important in Data Science one by one.
1. Hacking Skills or Capabilities
Hacking necessitates exceptional coding abilities. Coding is important because it aids in collecting and preparing data, which is often unstructured or in unusual formats. You will also need programming skills to apply statistics to your problems, manage the database, and so on. Hackers can use computer programming to implement extremely complex algorithms.
As the market for Data Science is growing and there is a lot of competition, you need to be a good hacker to stand out. That means you must be able to manipulate data to achieve the best results for any problem.
Hacking skills will enable you to work creatively with data and various algorithms to produce novel results. The hacking skills required for a successful data hacker include managing text files at the command line, learning vectorised operations, and thinking algorithmically.
2. Math and Statistics Proficiency
After gathering and preparing the data, the process of extracting insights from it begins. Mathematics is necessary for data analysis. You will need several mathematical tools to analyse the data, such as probability, algebra, etc. It aids in problem diagnosis by applying various mathematical and statistical approaches to your data.
Mathematics is important because it allows you to choose the best method to solve your problems based on the information. This is not to say that a Ph.D. in statistics is required to be a skilled data scientist; however, understanding what ordinary least squares regression is and how to explain it is required. Know more about role of statistics in data science.
3. Substantive expertise or domain knowledge
Domain knowledge is knowledge about a specific field, whether in business, healthcare, finance, education, or another field. You must understand the goals of that field and the various methods and constraints you will face. Domain knowledge is extremely beneficial to data science because there are some things where data science cannot be implemented due to field constraints. This is a problem if we use data science without domain knowledge as we may draw incorrect conclusions from the data. Before implementing a data science model or machine learning, you must first understand the field's goal, methods, and constraints. The goal of domain knowledge in data science is for the model or insight to be well implemented in the field.
Thus, familiarity with the field will enable you to apply Data Science to your problems more easily and effectively. As a result, domain knowledge is extremely valuable to data science during implementation.
Skills Required To Understand The Venn Diagram
With the help of data science courses, a person can learn mathematical skills, hacking skills, and substantive experience, as shown in the diagram's quadrants. Data scientists assist businesses in developing, producing, and processing insightful analytics. Machine Learning, Traditional Research, and Danger Zone are some areas in the Data Science Venn Diagram that include the interconnection of these skills.
1. Machine Learning
Machine learning, according to the Data Science Venn Diagram, requires knowledge of computer programming and mathematics but not domain expertise.
This means that you can simply enter your data into the model without knowing anything about it, such as what data is, what it means, and so on, and it will return some results.
2. Conventional or Traditional Research
This section represents your knowledge of math, statistics, and being an expert in your field, but you do not know coding or programming. However, this is not a major issue in this case because the data used in Traditional Research is highly structured. As a result, you don't need to worry about data preparation because the data is ready for analysis. Traditional research simplifies your task by allowing you to concentrate entirely on data analysis and extracting insights from it.
The three areas of knowledge can also be described in terms of their core competencies, intuition, validity, and automation. Upscaling is another term for automation, and we gain better insight as a result of their convergence. As shown, insight might also just result from having a good gut feeling (automation is not a prerequisite). That has undoubtedly been the case for a large portion of history. Automating data collection, processing, and analysis is a key component of the advanced analytics buzz right now.
3. Danger Zone
As the name implies, it is the most dangerous area of the Data Science Venn diagram.
Danger Zone combines coding and domain knowledge but lacks Math and Statistics. Drew Conway proposed this Data Science Venn Diagram because he believed it was the most unusual and unlikely to occur.
Let us illustrate this with examples such as word counts, maps, etc. These do not require math or Statistics, but they can still be very informative and useful.
After looking at all of the elements of this diagram, we can conclude that people from various backgrounds, such as coders with knowledge of math, statistics, and business, can try their hand at Data Science.
Statisticians who can code and have basic business skills, as well as business people with programming and mathematics knowledge, can try their hand at Data Science.
If you come from a background that includes knowledge of these fundamental skills, you can start your journey with Data Science.
Conclusion
Data Science Venn Diagram shows that various fundamental abilities, including mathematics, programming, and domain knowledge, are needed for the application of data science. An overview of how these abilities are combined in data science is provided in the diagram. You can start your adventure with data science if you come from a background where you are familiar with these fundamental skills. Keen learners can go to Knowledgehut Online Data Science Bootcamp for better understanding.
