Data science: a complete overview

With the growing use of data in all sorts of professional fields, is it any wonder that the demand for data scientists has also risen? After all, in 2021 it’s estimated that the world produced 79 zettabytes of information. That’s 79 followed by 21 zeroes.

It’s more important than ever that companies utilize data well, and the best way to do that is to hire data scientists to help them analyze and interpret data. But what exactly does a data scientist do? What is data science, exactly?

In this article, we’ll cover:

What is data science?

Data science is a broad term that refers to a multidisciplinary field which has the goal of learning new insights from real-world data through the application of statistical and computational techniques. That includes methods such as data gathering profiling, wrangling, modeling, and interpretation.

Why is data science important?

Data science makes companies able to more efficiently understand the huge amounts of data they produce and take in, then utilize those insights to make better quality, data-driven decisions. Utilizing data to its fullest extent can help to take the guesswork out of making future-facing decisions. And, as we stated at the start of this article, the world is generating increasingly-massive amounts of data each year. So in order to stay competitive in this data-driven economy, a business has to focus on using data to the best of its ability.

The data science lifecycle

Since, as we stated in our definition above, data science is a broad term that encompasses a lot of disciplines, it can seem overwhelming to understand the different branches under it. That’s why we’ll start by walking through the broad data science project lifecycle here. This starts with the definition of a problem, the collection of data, then moves into preparation, analysis, and the end stage of communication and deployment.


The first (and possibly most crucial) step in the data science lifecycle is to define what the problem or goal of your project is. If you don’t have a definition in mind, then you won’t be able to ensure that the rest of your process will solve the problem, fill the need, or answer the question that started it all. 

The best way to accomplish this is to start by asking “why.” Why are we doing this? Why do we need to run this project? Then clearly state your problem, and why it needs to be solved. That gives you the basis to build your project documents and align stakeholders to your project.


This is the stage where you ensure that you have the data needed to analyze for your project. You may already have all the data you need, in which case you can move onto the next stage of cleaning and preparation. But if you don’t, you’ll need to enter into the collection phase of your data science project.

There are three main ways to acquire the data you need:

  • Get access to internal data at your organization.
  • Capture further data you need.
  • Purchase readily-available data from trusted sources.

Data collection happens at all levels across an organization, so it’s important that you gather data from all sources. This may be customer data you can collect from your IT systems, or sales, employees, satisfaction ratings, and so forth. If you’re in a more specialized field, you may need to design and follow your own data collection process to ensure that you gather everything you need to complete your project.

Cleaning and preparation

Now that you have the data you need, it’s time to ensure that data is good enough quality to use for analysis. That’s where data cleaning and preparation comes into account. Data preparation is the process of taking raw data and turning it into something suitable for processing an analysis, which includes cleaning, labeling, and ensuring correct formatting. Data cleaning is the process of ensuring that data is good quality by removing incorrect, incorrectly formatted, duplicate, or incomplete data from your dataset. 

Key steps in the data cleaning process include:

  1. Remove duplicate or irrelevant data.
  2. Fix structural errors such as typos, problems with naming conventions, or incorrect capitalization.
  3. Filter outliers.
  4. Either remove or input missing data as observations.
  5. Validate data to ensure quality.


The next step is the most obvious and exciting – it’s time to analyze the data! It can seem like there are a lot of steps leading up to this one, but all of the preparation above is necessary to ensure that your analysis is thorough, accurate, and usable. 

Data analysis is the process of applying statistical techniques to your raw data in order to draw conclusions. That may include predictive modeling, prescriptive analysis,or diagnostic analysis, among other things. 

The goal of the analysis stage is to walk out with useable information, which is why knowing your end goal is so important. Do you need to know what future-facing business decisions to make? You may need to do some predictive analysis. Do you need to know the pain points of specific kinds of customers? You’ll need to do some data segmentation. The key is to know what you need so you can apply the appropriate technique.

Communication and deployment

Once you have your analysis and you’re certain of its accuracy, it’s time to communicate your findings and deploy them into being.

Communication entails distilling your findings down into a form that’s easy to understand (and, of course, accurate) so you can present it to key stakeholders and other affected members of your organization. That may involve creating a powerpoint and presenting, or a written overview of your process and findings. 

Deployment is taking your findings and actually applying them in the real world. If you learned that your business is predicted to have a boom and sell out in products in the next quarter, it means ensuring that your manufacturing can handle the added production needs. Or, perhaps, you learned that you need to implement a new tool to save your team time on paper shuffling. It’s time to find and put the resources toward putting it to use. Whatever it may be, you’ll be taking steps toward using the conclusions you drew and making better, data-driven decisions.

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Data science skills

There are a myriad of skills needed for data science, both technical and non-technical. But all are important for data science techniques to be implemented to their full potential.

Non-technical data science skills include:

  • Critical thinking
  • Effective communication
  • Problem solving
  • Curiosity
  • Business sense

Technical data science skills include:

  • Data preparation
  • Knowledge of analytics platforms
  • Write code
  • Knowledge of math and statistics
  • Ability to leverage machine learning and artificial intelligence (AI)

Learn more about data science skills.

Data science careers

For those who enjoy data and mathematics, going into the field of data science can seem like a no-brainer. But there are many ways someone can learn to use an apply data science in their career. Three of the most common data science careers include data scientist, data analyst, and data engineer.

Data scientist

Data scientists are responsible for laying a data foundation in the company in order to perform a robust analysis. That includes ensuring that data is cleaned and prepared correctly so their analysis is correct. They have to come up with their own questions to answer before performing the data analysis.

Typical job requirements include:

  • Collect, clean, organize, and analyze data for companies.
  • Find patterns in large sets of data.
  • Ask the right questions to direct the company toward data-driven decisions.

Data analyst

There’s a lot of overlap between data scientists and data analysts. Both are responsible for the collection and analysis of large amounts of data. However, data analysts tend to be of a less senior rank and with fewer responsibilities than your typical data scientist. Data analysts are usually less self-directed than data scientists and tend to work in teams instead of on their own.

Typical job requirements include:

  • Perform data analysis and deliver conclusions to teams and stakeholders.
  • Develop and maintain databases and data systems.
  • Working with broader teams to provide relevant data and analysis. 

Data engineer

Data engineers are responsible for creating and maintaining data systems. Those systems can include data collection, warehousing, storage, access, and analysis. They also work to create streamlined pipelines used by data scientists. This requires a deep knowledge of artificial intelligence and computer programming.

Typical job requirements include:

  • Develop and maintain architectures.
  • Identify ways to improve data reliability and readability.
  • Use data to find areas that can be automated.

Data science project challenges

When any field becomes as crucial as data science to a business’ success, it comes with its own set of challenges and hurdles. These aren’t deterrents to whether someone should implement data science or become a data scientist. They are, however, something that anyone should be aware of when walking into the field of data science. 

Some key challenges of data science can include:

  • Maintaining data security and integrity.
  • Vetting data and data sources.
  • Undefined metrics and goals.

What’s the difference between data science and business intelligence? 

So what’s the difference between data science and business intelligence (BI)? Both are useful to businesses and have to do with giving business decision makers a fuller understanding of all their key data. But there are a few key differences between the two.

As we stated above, data science is a broad field that has to do with the process of analyzing data and turning it from its raw form into useable information. The term itself is broad and applies to many different branches and types of data analysis, gathering, and processing.

On the other hand, business intelligence refers to a platform that gives your business a view into different kinds of key data visualizations. It combines business analytics, data mining, data tools, and gives it all a centralized infrastructure where stakeholders can peruse it all in one place. 

So, broadly speaking, the difference is that BI utilizes data science to deliver its analytics and data visualizations, but BI is a much more niche term that refers to a business tool.

Additional data science resources

This article is a high-level overview of the basics of data science, but we’ve barely scratched the surface of everything you can learn. Data science is an incredibly broad field that encompasses both simple and complex techniques, and on top of that, it’s also ever-evolving. Which is why it’s important to further your data science education in every way you can. 

That’s why we’ve gathered a list of great resources for people wanting to learn more about data science. From data science books for beginners, to blogs for people at any level (including, of course, the Tableau blog). For people wanting to learn more about specific types of charts, graphs, and the like, you can see Tableau’s reference library for full explanations and visuals. And if you want to work on developing your data science skills, you can find that in Tableau’s data skills education center.