Data Science for Beginners: A Quick Guide

If all the hype around data has confused you, read this super simple article to get your head around understating data science at its very basic.

We are witnessing an exciting phenomenon in the 21st century. Our decision-making is increasingly becoming machine-mediated. Smartphones have brought the world into our pockets. Just a few clicks/touch provides us with any information from anywhere.

The convenience of usage has made people across the globe post a plethora of information on the internet, and eventually, all our decision-making has become machine-mediated. It will not be a stretch to say that our smartphones and computers have started making decisions for us.

For example, which is the next movie that you should see? What is the best route to the destination? How many steps should you walk every day?. There are services, “apps” to answer all kinds of questions for us.

If you are fascinated by this phenomenon, suppose you have ever wondered how Google accurately provides answers to your queries, how Facebook finds friends for you, or how your smartphone decides how many calories you need to burn today. In that case, data science is the answer to your wonder.

What is Data Science?

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

Data science is an umbrella term that extends statistics and encompasses fields like Data Analytics and Data visualization. Data science uses complex machine learning algorithms to build predictive models. The data used for analysis can come from many different sources and be presented in various formats.

With the ever-growing amount of data being processed every day, fields like Big Data Analytics and Cloud Computing have also emerged as extensions of Data Science.

Why should you Learn Data Science?

As per the LinkedIn Survey 20211, Data Science and Artificial Intelligence specialist are two of the 15 most sought-after careers of the decade. The average salary being offered can be anywhere between 100000$ and 150000$.

The demand will only increase with every passing day. More and more new use cases are coming up, and data science is being applied to various fields, including Business Intelligence to Health Care systems.

But demand and salary should not be the only criteria to decide on a career path. It would help if you also looked at the skill set and interest to decide on the right career. For example, data science requires vital statistics with machine learning fundamentals and hands-on experience in one or more programming languages.

If you are comfortable with statistics and like to spend time writing programs to discover exciting information from the data, data science is the right career for you.

What skills are required to become a data scientist?

Many areas of data science offer plenty of new job opportunities. [Refer to article 2 to know more about various fields of data science] and [refer to article 3 to know more about various job roles of data science]

Following is a brief list of skills that can help you become a data scientist

Data Annotation: Data annotator processes the raw data and converts it into a format that a machine can process. Various machine learning algorithms require data to be in different formats, and the success of a prediction model widely depends on the quality of the data.

Therefore, creating high-quality data is of paramount importance for the success of any data science exercise. Usually, domain experts work as data annotators, e.g., a linguist is required to process text data, and a healthcare professional is required to prepare data in the medical domain.

Statistics: Statistics is the most critical unit of Data Science basics. It is the method or science of collecting and analyzing numerical data in large quantities to get valuable insights.

Visualization: The visualization technique helps you access vast amounts of data in easy-to-understand and digestible visuals.

Machine Learning: Machine Learning explores the building and study of algorithms that learn to make predictions about unforeseen/future data.

Deep Learning: Deep Learning method is new machine learning research where the algorithm selects the analysis model to follow.

Applications of Data Science

Internet Search: Google search uses Data science technology to search for a specific result within a fraction of a second

Recommendation Systems: To create a recommendation system. For example, “suggested friends” on Facebook or “suggested videos” on YouTube, everything is done with the help of Data Science.

Image & Speech Recognition: Speech recognition systems like Siri, Google Assistant, and Alexa run on the Data science technique. Moreover, Facebook recognizes your friend when you upload a photo with them, with the help of Data Science.

Gaming world: EA Sports, Sony, and Nintendo are using Data science technology. This enhances your gaming experience. Games are now developed using Machine Learning techniques, and they can update themselves when you move to higher levels.

Online Price Comparison: PriceRunner, Junglee, and Shopzilla work on the Data science mechanism. Here, data is fetched from the relevant websites using APIs.

Data Science for Future Technologies

Digital “twins” that track your health: QBio,tThe US company, has built a scannerthat will measure hundreds of biomarkers in around an hour, from hormone levels to the fat building up in your liver to the markers of inflammation or any number of cancers. It intends to use this data to produce a 3D digital avatar of a patient’s body – known as a digital twin – that can be tracked over time and updated with each new scan.

Q Bio CEO Jeff Kaditz hopes it will lead to a new preventative, personalized medicine era. The vast amounts of data help doctors prioritize which patients need to be seen most urgently and develop more sophisticated ways of diagnosing illness.

Virtual reality universes: Facebook has become Meta to move into the metaverse – and embodied internet mainly accessed through virtual and augmented reality. Announced back in 2021, Meta has been developing a new headsetunder ‘Project Cambria.’

The Cambria has been reported to be focused on the advanced eye and face tracking (to improve the accuracy of avatars and your in-game movements), a higher resolution, increased field-of-view, and even trying to make the headset significantly smaller.

Between Meta, Google, Sony, and plenty of other big tech companies, VR is getting lots of funding and seeing drastic improvements in the next couple of years.


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