Four pillars of data science

Data science is the scientific discipline that uses data to answer questions, solve problems and conduct hypothesis validation. Data scientists use programming languages, statistical analysis and algorithms to create models that help them gather insights from the data they collect.

They use their skills across different industries, including medicine, finance, insurance and retail, and work with massive amounts of data daily to find trends and patterns, thus helping businesses improve their services. 

For example, a data scientist may predict when people will buy a specific product or service based on past purchases or product reviews.

Four pillars of data science

Data science is a field that’s constantly changing and evolving. It is hard to keep up with the latest trends, tools, and methods. However,  there are some concepts that you can’t ignore if you want to make your data science career successful.

Domain knowledge

Math & statistics skills

Computer science

Communication & visualization

Following are the four pillars of data science –

1. Domain knowledge

Domain knowledge is the first pillar of data science. It is the understanding of the business and its customer base that allows you to develop products and services relevant to your customers and their needs.

Domain knowledge is also essential because it helps you make better predictions based on customer behavior, leading to better optimization of your product or service.

While domain knowledge is essential, there are many ways to acquire it without being an expert in every field. 

For example, if you are working with a retail company, you can read about how other retailers have marketed their products in the past or attend industry conferences where experts discuss trends in retail marketing.

2. Math and statistics skills

Math and statistics skills are crucial to data science, as you will use your knowledge of these subjects to analyze, interpret and present the data you collect.

The most common mathematical techniques in data science include probability theory and statistical analysis. Probability theory is used to understand the likelihood of an event occurring, while statistical analysis helps you determine whether a specific event has occurred.

Statistical analysis also involves calculating averages or medians or determining how close or far apart two sets of numbers are from each other. You can use this information to predict future events based on past events.

3. Computer science

The computer science pillar of data science is the foundation for all other 4 pillars of data science. Computer scientists use their expertise to solve complex problems and develop algorithms, software, and systems that allow for data collection, storage, analysis, and dissemination. They are also responsible for setting up decision automation processes that ensure accurate results from these systems.

Computer scientists have a wide range of skills they can apply to data science, such as –

• In-depth knowledge of the computer’s functioning
• Ability to write code in languages like Python or R
• Working knowledge databases like SQL
• Ability to design algorithms that process hypothesis validation efficiently.

4. Communication and visualization

The last pillar of data science is communication and visualization. This means that you need to be able to communicate your findings and present them in a way that’s easy for people who aren’t experts in your field. You also need to visualize your data so that others can quickly grasp what it shows.

Communication is vital because it allows you to share what you have learned with others in the organization and helps them understand how it will impact their lives. On the other hand, the visualization allows people unfamiliar with the information you have gathered from your data set to see its value more efficiently. 

You can use graphs, charts, or diagrams to present information visually rather than just presenting raw numbers or text-based reporting data.

Career path in data science

Data science is a field that touches on several other disciplines, including math, statistics and computer science. Data scientists work with large amounts of data and then use statistical techniques for pattern discovery and obtaining insights. If you are seeking to build a strong career in this domain, you must strongly consider enrolling in the data science course at Online Manipal. Exclusively designed by MAHE, the course helps you acquire the necessary skills along with expert guidance. You will learn about big data, machine learning and artificial intelligence – all critical for success in the modern workplace. 

In this course, you will gain a solid foundation in statistics, probability theory and linear algebra from your instructors at MAHE. You will also learn about various machine learning algorithms that can be used for predictive modeling, classification and regression analysis using Python programming. 

You will get hands-on experience with real-world data sets by working through case studies and applying what you have learned in each unit. You will also have access to an exclusive job placement team that can help you find employment after graduation or help you land interviews if you are already employed but looking for a career change.

ALSO READ: Career path in Data Science

Bottom line 

From the ability to make better decisions faster, to being able to predict what will happen before it does, data science has become a critical component of our society. The amount of data generated today will only continue to increase and become more complex. Therefore, it is important that you learn how to analyze and use this data in your life and career.

MAHE offers an online MSc in Data Science course at Online Manipal that can help you get ahead in this ever-changing world of data science. Experts in the field design our courses to be fun and engaging while still being informative. The best part is that they are all 100% online, so you don’t even have to leave your house! 

Key takeaways:

• The four pillars of data science are domain knowledge, math and statistics skills, computer science, communication and visualization. Each is essential for the success of any data scientist.
• Domain knowledge is critical to understanding the data, what it means, and how to use it. Data may be organized in several ways; this understanding helps you determine which pattern best fits your hypothesis.
• Math and statistics skills are essential because they allow you to manipulate your data to create meaningful results. 
• Computer science allows you to manipulate large amounts of data quickly and easily—something that would be impossible without computers today! It is also necessary for programming languages like Python or R, which many businesses use today.
• Communication and visualization should not be neglected. They are just as crucial as math & statistics skills because they help others understand what you have done with the data by presenting it in an easy-to-understand format (visuals!).