What Is Data Science? A Beginner’s Comprehensive Guide



Data science was once dubbed as the “sexiest job of the 21st century,” and with good reason. As the amount of data generated every day grows, it’s become a powerful tool for organizations to remain relevant in the market. This has left companies scrambling for qualified data scientists who can utilize data to their advantage. 

Below, we put together a complete guide to data science. We tackle all corners of the field, from what data science entails right down to the resources you can use to become a data scientist.

What Is Data Science?

Data science is a multidisciplinary field designed to analyze real-world data to derive insights for decision-making processes. It combines the principles of statistics, mathematics, computer science, and information science, among others. All of these culminate in the formulation of data-driven decisions that seek to assess consumer demands, stimulate business growth, and so on.

What Is Data Science Used For?

The demand for data scientists isn’t going away. And that’s because data science is a field that transcends industries, from sales and marketing to healthcare and transportation.

  • Commerce. Data science is a core component of business survival. Through data science, a company gains critical insights into its performance. How well did a particular service or product perform in the last quarter or year? 

What was the consumer response? More importantly, how can these be used to drive a company’s growth? Data scientists answer these questions and more.

  • Advertising. The ubiquity of the Internet and smart devices has led to the daily generation of 2.5 quintillion bytes of data. Data scientists tackle these large amounts of data to assess changes in trends and behaviors. 

Their insights are then used to look at what’s in and what’s out in the market. This, in turn, helps advertising companies decide which products to phase out, launch, or improve.

  • Healthcare. Although less known, data science has taken giant leaps in the healthcare industry. Data science, for instance, is used in medical imaging or the interpretation of data to derive insights from CAT scans, X-rays, and so on.

Data scientists also analyze patient data to know which populations are more likely to develop certain diseases. Finally, medical applications leverage data science to measure a user’s health status. The app, Clue, for instance, can predict menstrual cycles by analyzing user data. LYNA predicts metastasis in breast cancer tumors.

  • Transportation. Data science has been a boon to the transportation industry, especially land transport. It’s used to optimize courier services to ensure faster delivery. It’s also used by food delivery or taxi clients to predict fuel costs based on traffic conditions, vehicular consumption, and so on.

What Is a Data Scientist?

A data scientist oversees the entire data science project lifecycle, generally divided into five stages.

  • Collecting data. Just like with any research, a data science project starts with data collection. Data scientists collect data from numerous data sources. Some big data sources include government sites and social media platforms such as Facebook and Twitter. 

Government data are usually free and open for all while the latter can be collected by connecting to their public APIs.

  • Preparing and cleaning data. The data collection often, if not always, yields vast quantities of unstructured data. Data scientists conduct this stage filtering out irrelevant data, checking and replacing missing data, and making sure the data collected follows a single format.

  • Exploring data. In data exploration, data scientists take a closer look at the scrubbed data and read how each character or variable relates to one another. Doing so allows them to move on to the next stage.

  • Building a data model. It’s in this stage that machine learning, statistics, and algorithms come into play. Data scientists create data visualization tools in the form of graphs, tables, and diagrams. These tools make it easier for data scientists to identify patterns in the data and forecast trends or behaviors.

  • Interpreting data. After collecting data, scrubbing it, analyzing its elements, and identifying patterns, data scientists can then derive insights that answer the project’s research problem. Stakeholders use these insights to make strategic decisions based on their goals.

Data Scientist Skills

To do all the above steps, data scientists need to have a certain set of skills. These include the following:

  • Problem-solving skills. Data scientists use their integrated experience in computer science, statistics, analytics, and math to come up with recommendations and/or solutions to problems.

  • Intellectual curiosity. Because data scientists spend their time dealing with tons of data, they must show a strong sense of inquisitiveness. This allows them to explore a subject deeply and cover the necessary bases. After all, one can’t determine relevant data nor spot data patterns if s/he doesn’t understand what the collected data is about.

  • Business acumen. For data scientists who work in the business sector, it isn’t enough that they know the technical aspect of data analysis. To be able to derive actionable insights, data scientists must have a clear knowledge of the market they’re working in. They should also have an understanding of the business challenges their employer is looking to fix.

  • Communication skills. Firms looking for a good data scientist want someone who has data storytelling skills. This is the ability to ‘narrate’ numerical findings in a way that non-technical teams understand. As a data scientist, awareness of the fact that not everyone has a data science background is important.

  • Interpersonal skills. A data scientist cannot operate on his/her own. You’ll often find yourself collaborating with other teams, from business and project management to sales and marketing.

Learning Data Science



Because data science incorporates mathematics, artificial intelligence, data analysis, and statistics, learning it isn’t as easy as ABC. Various paths can be taken to learn data science, from attending a college, going to a data science bootcamp, or taking courses online. 

How Long Does It Take to Learn Data Science?

The length of your data science study depends on your choice of learning path. First, you can take the traditional path of pursuing a bachelor’s degree in computer science or information science. 

This may take between three and six years to complete depending on your availability. Some data scientists choose to pursue a master’s degree in data science to increase their chances for career advancement. This may take up to two years to complete.

Another way is to attend a data science bootcamp, which generally lasts between three months and eight months. Others last longer, although they never go beyond one year of study. Data science bootcamps are a great option if you’re looking to break into the field as quickly as you can while still having the fundamental skills and knowledge in tow.

The Best Data Science Courses and Resources Online

Whichever path you choose, it’s clear that there’s no shortage of learning avenues for data science. Below are several of the courses you can take to equip yourself with the essential data science skills.

Online Data Science Courses

Precalculus through Data and Modelling Specialization

  • Provider: Coursera
  • Duration: 4 months
  • Cost: Free
  • Prerequisites: None

If you want to get started with machine learning or data science but are not so well versed with the math behind it, then this specialization is for you. Offered by John Hopkins University, it covers three courses centered around precalculus from relations and functions, periodic functions, and mathematical modeling.

Probability and Statistics in Data Science Using Python

  • Provider: Offered by the University of California, San Diego via EdX 
  • Duration: 10 weeks
  • Cost: Free ($350 for a verified certificate of completion)
  • Prerequisites: Undergraduate level education in linear algebra and multivariate calculus

Probability and Statistics are the core mathematical foundations of data science. This course covers the fundamental theories of both and how these apply in processing and visualizing data. 

You also get a first-hand application of such theories using Python libraries and the Jupyter notebook, one of the most popular tools data scientists use for research and data analysis. This course is effectively free and you are only required to pay if you wish to get a certificate.

Python for Data Science and Machine Learning Bootcamp

  • Provider: Udemy
  • Duration: 25 hours of on-demand video
  • Cost: $129.99 (subject to change)
  • Prerequisites: None

If you want to learn machine learning and coding in Python simultaneously, then this is probably the aptest course for you. While most courses only provide either theoretical knowledge or practical experience, this course does both. It starts with a brief Python crash course and then descends into the use of machine learning libraries.

Data Science Math Skills

  • Course Name: Coursera
  • Time: 13 hours
  • Fee: Available upon enrollment (comes with a 7-day free trial)
  • Prerequisites: Basic knowledge of high school algebra

This course teaches you the core math behind data science. It is great for beginners as it solidifies your fundamental understanding of data and its interpretation through mathematical models.

Online Data Science Resources

Excellent additional resources are listed below. These are easily accessible for anyone regardless of one’s level of expertise.

  1. GitHub. GitHub grants learners access to programmers, developers, and enthusiasts. It also contains multiple software templates and completed datasheets that help aspirants design their algorithms.

  2. PyData. PyData is a training initiative that arranges conferences globally to allow scholars and professionals to share their experiences in their work. You’ll learn about general Python best practices with examples of real-life scenarios that data scientists have focused on. You’ll also be introduced to several new libraries.

  3. Distill. Distill seeks to provide a clear and intuitive description of the principles of machine learning.

  4. ArXiv. ArXiv is Cornell University’s fully accessible archive for electronic preprinting of research articles in areas such as computer science and machine learning. It is essentially the way to search for new science and algorithms.

Data Science Career Outlook and Opportunities

With all businesses being data-driven and not having enough qualified individuals, data scientists enjoy a high demand at all levels. Data scientists took the third spot in LinkedIn’s 2020 Emerging Jobs Report, successfully making it into the list for three years in a row.  

Data Scientist Salary

Financially speaking, the data science field provides more than enough compensation to be pursued. According to PayScale, the median salary of a data scientist in the United States is $96,208. This number could increase to over $130,000 if you account for the cash bonus, profit-sharing, and commission.

Data Scientist Career Paths

Because of data science’s multidisciplinary nature, data scientists find themselves in a variety of roles including:

  • Business Intelligence Specialist. Business intelligence specialists look at data collected in the past and the present to provide a summary of the company’s performance. This gives business leaders a retrospective view of the company’s health.

  • Business Analyst. Business analytics is similar to business intelligence, although more forward-looking. Analysts of this field conduct data warehousing and look at stored data to forecast market trends and consumer behaviors. This makes predictive analytics a vital component of this field.

  • Data Analyst. The data analytics field operates in the same way as business analytics, albeit on a more technical scale. Data analysts deal with big data. They gather both raw and structured data, process and organize these through data mining, and build data visualizations to uncover trends and patterns.

  • Data Engineer. Put simply, data engineering is a combination of data analysis and software engineering. Data engineers build the infrastructures that data scientists need to obtain data and analyze data.

  • Data Architect. Data architecture is interrelated with data engineering. Just as how structural architects design buildings that engineers build, data architects design the data frameworks that data engineers develop.

Other data science roles include machine learning engineers, applications architects, and statisticians.

Is a Data Science Career Worth It?

Data science exhibits strong job growth, high salaries, and endless challenges for the mind. If you want to be at the receiving end of all three, a data science career is worth it. If you’re still on the fence about launching a career in data science, perhaps checking if you meet the following three conditions will help.

  1. You want to contribute to the machine learning boom and make a lucrative living out of it.
  2. Statistics is your forte and you love crunching numbers and big data. 
  3. You are comfortable working in closed spaces for extended periods. 

If you checked all three, then becoming a data scientist may be worth a try. To know more about the steps you should take to become a data scientist, read our guide here

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