What Is Data Science? A Beginner’s Guide

What is Data Science? What does it? What are the career options?

Image of What Is Data Science? A Beginner's Guide

Data Science is the new rage among tech enthusiasts of today. We bet you’ve picked up some Data Science jargon at random, and wondered at the din. For most lay folks, anything remotely connected to data invokes the imagery of a digital utopia: a domain of undecipherable digits and symbols working in sync to make the magic happen.

The reality, however, is far less inscrutable and much more reachable. As a field, Data Science is not exclusive to the highly mathematical group of aspiring programmers or statisticians. Virtually anyone can step on this turf and learn the ropes.

In this beginner’s guide, we unravel this exact mystery. Be prepared to learn more about: What is data science? What it can do? Who does it? How do they do it? And, how you can do the same. Here we go!

What Is Data Science? – Definition, Overview

Image What is data science? Overview

So, what is data science? Simply put, Data Science is an interdisciplinary field in which clusters of large data are subjected to scrutiny using modern tools and techniques.

This is done to identify recurring patterns, gaps, and trends, develop key insights based on these identifiable metrics, and finally leverage these insights to help organizations with better decision-making.

The digital bots of today are drowning in heaps of new data every minute. Internet giants such as Google, Twitter, Facebook, Amazon, YouTube, Instagram, etc. together with the emerging IoT (Internet of Things) technology have resulted in a sea of retrievable data.

And it sure didn’t take organizations and companies long to anticipate all the ways they can leverage this data to derive meaningful insights and make predictive analyses for greater efficiency. This gave rise to the study of Data Science (DS) where the foundational equation looks something like this:

What is Data Science?
DS = Big Data + Data Analysis –> Insights + Trends + Informed Decision Making 

And the person making this equation happen is referred to as a Data Scientist.

As a field of study, Data Science requires a two-tiered approach.

  1. Knowledge of the inter-related fields of science that hinge upon Programming, Linear Algebra, Statistics, Calculus, Machine Learning, and Deep Learning. 
  2. Knowledge of the most pertinent programming libraries and machine learning algorithms to carry out functional interdisciplinary tasks with greater ease.

Following is a more in-depth analysis of what each of these core fields entails. If you are interested in learning paths, our lists of machine learning courses, data science courses, data analytics courses, artificial intelligence courses, and business analytics courses might assist you further. These lists aim to help you find the best classes.

Data Science – Technical Subjects

Image What is data-science? Overview Technical subjects

For being a cross-disciplinary field of data-driven action1, Data Science will quickly put you abreast of the following technical subjects.

  • Programming – Wrestling with big data and successfully executing solutions will require you to quickly immerse yourself into coding. In the beginning, at least, basic coding skills will suffice. After that, you can quickly build upon your previous knowledge through hands-on projects.

    Python and R are two of the most popular programming languages for data analyses. The former offers a rich assortment of libraries, while the latter specializes in the statistical and analytic treatment of data.
    Learn: Python Courses Online | R Courses Online
  • Probability and Statistics Together the two make up the baseline for Data Scientists. Good command over Statistical methods can help with crucial tasks such as calculating percentiles, performing more meaningful analysis, and rendering verifiable hypotheses, etc. Probability will help you with predictive analysis where it is important to determine the likelihood or improbability of events to occur in the future.
  • Calculus and Linear Algebra – These will help you with executing some routine mathematical operations such as qualifying data sets in the form of matrices and vectorization. Calculus-based tools such as models, derivatives, and integrals help in drawing faster conclusions and performing rapid calculations.
  • Machine learning – At the heart of Data Science lies Machine learning. Not long after you begin wrangling data, you will be required to carry out complex-level insights and base predictions on them. Machine learning smoothens out this process by automating analytical model building.
  • Deep Learning – It is a subset of machine learning that helps with tabulating deep neural networks to allow unsupervised learning from complex, unstructured data. This results in the execution of more complex machine learning tasks such as voice recognition, social networking filtering, machine translation, etc.

Data Science – Popular Fields & Skills

Image Data Science - Popular Fields & Skills

Data Science is now a parent domain to several related sub-fields. Remember that it is best to be a jack of all trades and master of at least one!

After a period of trial and error and a few exertions, you’ll come to know a bit about common Data Science domains while honing your expertise in at least one of these. Below is a concise list of the most common fields in Data Science and what these entail.

  • Data Mining and Statistical Analysis – Data mining involves the implementation of statistical methods, predictive models, and exploratory analysis to identify key trends, patterns, and gaps in the existing assortment of Big Data.
  • Data Warehousing and Data Engineering – These are all about managing the source, structure, and quality of data for external analysis and queries.
  • Machine learning – Machine learning can be thought of as the next stage in data mining and statistical analysis. Due to its enormous applicability, Machine learning can be further broken down into its related sub-fields, so identifying your ideal niche would require some experience and assessment.
  • Data Visualization – This is where experts playout data to the best of their creative ability. It involves presenting information in a graphic, design-oriented, and visually appealing way. Most Data Science professionals, at some point or another, have to learn the ropes of data visualization. But when this becomes your area of focus, be prepared to generate some high-end Business Intelligence solutions.
  • Cloud and Distributed Computing – This field entails designing and implementing infrastructures that allow cloud computing and distribution for various enterprises. Cloud experts supervise system integrations, ensuring that any potential error or loophole is removed.

Data Science – Real-Life Use Cases

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The cost and time-effective solutions introduced by Data Science when dealing with the problem of storing and computing exponentially growing data have brought forth several use cases. Below is a list of the various ways Data science is transforming our world.


The healthcare sector has perhaps seen some of the biggest transformations ever since the introduction of Data Science and its related sub-fields. BenchSci has successfully introduced Machine learning2 in the process of drug development.

Medical Image Analysis has brought precision to the processes of tumor detection, artery stenosis, and organ delineation. This is thanks to the application of Support Vector Machines (SVM), machine learning, image indexing, and various methodical applications from the field of data science.

Other medical niches to have benefited from Data Science include research in genetics for more personalized treatments and various natures of virtual assistance for patients.

Banking and Finance

Banking companies have begun to leverage the methods and tools of Data Science to curb losses and risks of bad investments.

This is achieved through the employment of probabilistic and statistical methods during customer profiling, analyzing customers’ past expenditures, and other variables.

Predictive analytics and risk modeling have not only mitigated losses but enhanced customer experience to boot.

Targeted Advertising and Internet Search

Digital marketing has undergone a complete shift thanks to intuitive Data Science algorithms. Such algorithms are responsible for automating targeted marketing to relevant consumers through displaying banner ads, recommended feeds, and digital billboards.

These digital ads have also proven to incur greater returns on investment than traditional advertising. Search engines, such as Yahoo, Bing, and Youtube employ Data Science to deliver the most pertinent results for queries in the shortest possible time.

These websites-cum-search engines also manage to retain traffic through relevant recommendations generated by machine-learning algorithms.

Highway Asset Management

Computer Vision, a sub-field of machine learning, is now being implemented by highway authorities to prevent routine occurrences of traffic congestion, bad or illegal parking, and road accidents.


Popular gaming enterprises, such as Nintendo, EA Sports, Sony, and still others have taken their gaming experience up a notch by introducing complex level artificial intelligence and machine learning. The latter allows a highly intuitive virtual opponent (computer) as well as eerily realistic settings.

Bear in mind that the afore-mentioned list of Data Science use cases is by no means exhaustive. The technology’s application is widespread and continues to evolve.

Data Science – Programming Language Libraries

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As mentioned earlier, Data Science can be best mastered via a two-tiered approach. Apart from a cross-disciplinary assessment performed in the previous section, it is imperative to shed light on the pragmatic skills in programming libraries and, further below, machine learning algorithms that are at the heart of most operations in Data Science.

Programming languages contain a standard assortment of in-built libraries although they can also be created from scratch. These are sets of precompiled task functions that can be employed in the routine course of configuring and documenting data types, setting up templates, creating classes or subroutines, and performing other specifications.

Relying on these libraries will save you from having to write complicated codes for frequently used routines.

Popular R Libraries

While it is not fair to rate libraries in terms of functionality given their unique specifications, certain libraries continue to be more popular among programmers. R features over 16 thousand libraries with dplyr, tidyr, and ggplot2 being the most popular ones.

Dplyr mainly assists with filtering, mutating and summarizing data types. You can also select variables and rearrange the ordering of the rows.

tidyr is a core package in the Tidyverse ecosystem and allows programmers to tidy up messy data. It transforms each column into a variable, each row into an observation, and each cell into a singular value.

With ggplot2, you can perform compelling visualizations and lucid storylines through graphs, charts, and plots.

There are many more packages popular with programmers. It is best to begin your journey with the most commonly used libraries and work your way up with more experience.

Popular Python Libraries

Over 137,000 Python libraries assist programmers in machine learning, visualization, manipulation, complex Data Science applications, and much more. Following is a list of the most commonly used Python libraries.

  • Numpy
  • Pandas
  • Tensorflow
  • Scikit-learn
  • SciPy
  • Keras
  • PyTorch
  • LightGBM
  • Eli5

Numpy, with its interactive Array interface, simplifies complex mathematical implementations and allows expressing sound waves, images, and various binary raw streams as an array of real numbers.

Pandas offers high-end tools for working with structured data in machine learning and translating complex operations with just a few commands.

TensorFlow is an open-source computational library that helps with quick linear algebra operations and writing new algorithms for machine learning.

Scikit-Learn is considered one of the most popular Python libraries when working with complex data and boasts features such as cross-validation, unsupervised learning algorithms (more on this later), and data extraction.

SciPy is mostly used by engineers and app developers for optimization and operations in linear algebra, Statistics, and Integration.

There are many more popular libraries but these will serve well for starters, providing a comprehensive set of features for most classical applications and methods.

Data Science – Machine Learning Algorithms

Image Data science machine learning algorithms

Machine Learning is the real tech-whizz behind most Data science processes today. If ever the self-driving cars of the future are to turn into a commonplace event, it is these digital beasts who will make it happen.

The project of Machine Learning begins with core ML algorithms, which, broadly speaking, can be divided into three distinct categories:

  • Supervised Learning – These algorithms will allow you to create functions that can map inputs, generated from a given set of independent variables, to desired outputs for predicting the behavior of a dependent variable. Common algorithms that help in supervised learning include Regression, Decision Trees, KNN, Logistics, etc.
  • Unsupervised Learning – There are no dependent variables here for predictive analysis. Instead, these algorithms help with clustering populations in different groups which are used for customer profiling. K-means and Apriori are some commonly used algorithms for Unsupervised Learning.
  • Reinforcement learning – These algorithms train machines for making a set of decisions through a process of trial and error conducted in an open environment.

Data Science – Types of Data

Image Data Science - Types of Data

When it comes to wrangling data, there are multiple ways to go about it. These ways correspond with both the goal of analysis as well as its nature.

Numeric/Categorical and Time-Series

These types are self-explanatory. Numerical data is available in the form of numbers. The typical way to deal with it is to classify such data into a given range that falls within the natural limits of what we are looking to measure.

Categorical data classifies items/units into various categories. Time-series data refers to a special kind of numeric data that is mostly used in the Internet of Things.

Structured Vs. Unstructured Data

Often, analysts working with data have to change their mode of operations and processes based on whether it is structured or unstructured.

Structured data is organized in the form of spreadsheets, rows, and columns. Unstructured data is unorganized and is very difficult to filter. It is not so much as the impossibility of organizing unstructured data that separates it from its counterpart.

Indeed certain types of unstructured data, such as emails, can be easily systematized. But the sheer variety of data makes it unique. Unstructured data can include data obtained from sensors to images, surveillance data, invoices, entertainment data, and much more.

Big Data and Databases 

As the name indicates, Big Data is classified by the sheer value of its size and not its type3. For any data to be qualified as Big there just needs to be too much of it. Most machine learning processes work with Big Data for predictive analysis.

The bigger the data, the more accurate will be the predictions that are used in various sectors, such as business, healthcare, finance, etc.

The size of Big Data however has created difficulties of “dark data” and storage. The problem of storage was met with the invention of databases: sets of structured data held in the computer’s memory or available on the cloud.

Lately, experts have begun to see Big Data as a database in itself since its size is beyond the capacity of traditional databases. But it differs from standard relational databases that store and process highly structured data.

Data Science – Jobs, Careers, Learning Paths

Image How to learn data science - Paths

By now you are already familiar with the popular sub-fields within Data Science. If you have a hunch the field is for you, here’s a list of the most common careers you can aim for.

  • Data Scientists, Business Analysts, and Statisticians: These are careers closely linked to the subfield of Data Mining and Statistical Analysis. Field experts would mainly require skills in Python/R and Statistics.
  • Data Engineers, Database Developers and Data Analysts: Relates to Data Warehousing and Data Engineering. Some key skills required here include ETL, SQL, Hadoop, Apache, and Spark.
  • ML Engineers, AI Specialists, and Cognitive Developers: These careers are linked to the subfield of Machine learning and Deep Learning. Python, Algebra, ML Algorithms, and Statistics are a few skills required of ML experts.
  • Data Viz Engineers and Data Viz Developers: These are careers typically associated with the subfield of Data Visualization. To excel as a Data Viz Engineer, you will need to hone your skills in the most commonly used R and Python libraries.
  • Cloud Architects, Cloud Engineers or Platform Engineers: As a cloud expert, you’ll need to step up your programming game in Python, along with networking, data storage fundamentals, and cloud-specific technologies.

Fortunately, the barrage of MOOCs and online bootcamps has made self-learning possible for many of us. You can either choose to formally enroll in a degree program offered onsite, or subscribe to online courses, learning paths, and career tracks.

Popular online platforms such as Udemy, DataCamp, Coursera, or Pluralsight offer beginner, intermediate, and advanced level courses in Data Science and its related fields.

Typically, these courses drill learners in the foundations of Programming, Linear algebra, Statistics, and Calculus, before diving into core technicalities, algorithms, and libraries.

Data Science – Your Miscellaneous Resource Pack

Image Data Science - Resource Packs

As you code your way through your Data Science career, a number of external sources will help make this journey easier. Here’s a short list of kits to keep an eye on.

Jupyter Notebooks – This computational notebook is a free, open-source web tool to help you develop codes interactively. It is a powerful Data Science tool for beginners looking for a way to combine multiple data science-related tasks, such as software code, multimedia resources, explanatory texts, etc, in a single document.

GitHub – Almost any Data Science project will require experts working in a team, and this is where GitHub comes in handy. Amongst other things, GitHub is a popular version control software that will allow you to keep track of your changes in a multiple-person setting, and remain organized.

Stackoverflow – It is an online Q/A community of novice and veteran developers from all over the world. When you find yourself stuck in the rut, feel free to reach out to the community by engaging in various asks-and-respond threads.

What Is Data Science? – Summary and Conclusion

Summarizing up our guide on what is data science. We’ve come a long way in establishing the foundational tenets of What is Data Science. Do not worry if you find the information a bit overwhelming. Learning is a highly personalized process, and it takes time, practice, and effort to master specific skills.

And although this guide hopes to have eased you out of the most common concepts in Data Science, get ready to meet your own set of bumps on the road to becoming a pro-level Data Scientist.

Nothing beats experiential learning and with the possibilities in Data Science being literally endless, feel free to carve your own career track and learning path in the process.

So what is data science for you?  What will it do in the future? Let us know in the comments below. If you have questions about reasonable learning options and paths, feel free to contact us to describe your goals.

Sources: 1 | 2 | 3

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