4 Important Pillars of Data Science to know in 2023

4 Important Pillars of Data Science to know in 2023

Data science is a scientific discipline that utilizes data to answer questions, solve issues, and test hypotheses. Data scientists employ programming languages, statistical analysis, and algorithms to create models that draw insights from the collected information.

They employ their talents across numerous industries, such as medicine, finance, insurance, and retail. They use large amounts of data daily to identify trends and patterns to help businesses enhance their services.

Data science’s growth has been exponential, so it is essential that everyone is informed on its current state and how we can add value to it.

Data science can mean different things to different people, but at its core, it’s about using data to answer questions. This definition is fairly broad because data science itself is quite expansive!

4 Pillars of Data Science

Data scientists typically come from various educational and work experience backgrounds, though most should possess proficiency or mastery in four key pillars 

  1. Domain Knowledge.
  2. Math Skills
  3. Computer Science
  4. Communication Skill

Domain Knowledge

Domain knowledge is the cornerstone of data science. It involves an in-depth understanding of a business and its customer base, allowing you to craft products and services tailored to those needs.

Domain knowledge is vital because it enables you to make more accurate predictions based on customer behavior, ultimately leading to improved product or service optimization.

Domain knowledge is essential, but there are numerous ways to acquire it without being an expert in every field.

Math Skills

Math and statistics skills are vital for data science since you will use your understanding of these subjects to analyze, interpret, and present the collected data.

Probability theory and statistical analysis are two of data science’s most fundamental mathematical techniques. Probability theory helps you predict events, while statistical analysis allows you to verify whether an event happened.

Statistics also involves computing averages or medians or determining how close or far two sets of numbers are from one another. With this knowledge, you can predict future outcomes based on past outcomes.

Computer Science

The computer science pillar of data science is the backbone for all four other pillars. Computer scientists apply their expertise to solve complex problems and create algorithms, software packages, and systems for data collection, storage, analysis, and dissemination. They’re also accountable for setting up decision automation processes that guarantee accurate results from these systems.

Computer scientists possess a range of abilities that they can apply to data science, such as –

  • Knowledge of computer operations
  • Ability to code in languages like Python or R
  • Working knowledge of databases such as SQL
  • Design algorithms that efficiently validate hypotheses.

Communication 

Data science’s final pillar requires communication and visualization. You need to be able to explain your findings straightforwardly for non-experts in your field while also visually representing your data so others can quickly grasp its meaning.

Communication is essential in the workplace, allowing you to share what you have discovered with others and ensure they comprehend its relevance. On the other hand, visualizing data sets allows those unfamiliar to understand their importance better.

Other essential pillars of Data Science include the following:

Reporting

Reporting involves transforming raw bulk data into easily-digestible information to gain valuable insights. Data visualization can be achieved in numerical summarization or visual representation. Not all summaries need to be; raw data visualizations may serve as an active source for pattern discovery or process fault alerts.

We can create custom metrics from raw data and report them to monitor the overall health of various products, services, functions, and practices.

Hypothesis Validation

Reporting is essential to gaining clarity, but the next crucial step requires us to detect major shifts or challenges to our business assumptions through data. For example, sales will always experience fluctuations, and that slight bump or slump may not necessarily indicate a major issue. We must rely on statistical methods to determine whether it’s statistically significant.

Statistics provide an objective way to judge whether things have improved or worse instead of relying on wishful thinking or biased hunches. Statistics allow us to make these determinations with precision and certainty.

Decision Automation

This is a major and highly sought-after practical use for Data Science. Look for scenarios in which there is a constant reliance on data to make similar and repeated business decisions. Example-

Clicking on Ads: People from various geographical locations and with various browsing habits come across online ads and either take action or don’t. In this instance, interpersonal attributes, browsing behaviors, and product and ad attributes provide abundant data. Each triggered ad can either result in repeated clicks or be ignored entirely.

Pattern Discovery 

Decision automation is a major benefit of data science, yet only some decisions can be automatically automated. However, an immediate outcome may not always be predicted with advanced predictive models to automate a process fully.

Sometimes, manual intervention is necessary before making a decision; this involves recognizing existing patterns and arriving at an informed choice.

Are you looking for a career in Data Science?

FunctionUp’s Data Science course is an integrated program in AI and data science which will prepare you for exciting career opportunities in data science. Master the field of data science and work with core technology frameworks for analyzing big data. You’ll learn

  • Developmental and programming Frameworks
  • Process massive data
  • Develop expertise in complex data science
  • Implementation using R
  • Statistical processing

FunctionUp’s data science program is exhaustive and a door to take a big leap in mastering data science. The skills and learning by working on multiple real-time projects will simulate and examine your knowledge and will set your way ahead.

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