A Comprehensive Guide To Data Science: The Building Block Of Our Future

In the past decade, the biggest challenge faced by most industries was a lack of infrastructure to store the ever-increasing amount of data. All companies were competing against each other in the race to build frameworks and solutions to store data. But soon enough, Hadoop and several other new frameworks successfully solved the lack of storage crisis.

In the year 2012, the total amount of data in the world was 2.7 zettabytes, but, in 2020, this number has already gone up to 44 zettabytes (Builtin). Just in the past eight years, the amount of data that we have created has increased at an exponential rate. Studies have shown that over 90% of the total data to ever exist in the world, was created in the last two years. Data science has a crucial role to play in the generation and storage of our data. Therefore, understanding how it works is essential.


How Data Science Works?

Data science is a field that uses a wide array of scientific methods to gain insights and extract knowledge from data. In simple words, data science takes raw data and refines it using sophisticated techniques and expertise in various disciplines. To be a data scientist, one must be skilled in a wide range of fields such as mathematics, engineering, computing, visualisations, and statistics. This expertise allows them to extract useful insights and information from massive volumes of data. The data obtained by data scientists consists of the most crucial bits of information about various industries. Therefore, it helps drive innovation and increase the efficiency of existing processes. Accordingly, we can say that the future of data analytics would be bright.

The life-cycle of data science is generally fixed and consists of the following five stages:


The first step of data science deals with how data is collected. Data is always distributed across a variety of business applications and systems; it is never in one place. New data can also be entered into a system, and this process can either be manual or automated. Another way to collect data is by sourcing it through data devices. The rise of the Internet of Things (IoT) has made it significantly easier to collect data through data devices. Data can also be extracted from various sources such as web servers, databases, logs, and online repositories, through a process called data extraction.


This step deals with what happens to the data once it is sourced. The process of data warehousing stores data collected from different sources. Then, inaccurate, unreliable, duplicate and missing data is removed from the database. The remaining data is staged and processed for interpretation using machine learning algorithms. Finally, the data is efficiently transferred from one location to the other using a framework.


Once the data is free of any errors, it is processed to find trends and insights. Data mining is used to identify trends and future patterns in a data set. Processed data is then classified into groups on the basis of similar traits. This data is used to produce a descriptive diagram that shows the relationships between different types of data. The last step is to summaries the data to create a concise description of the dataset.


After classifying and modelling the data, the next step is to analyse the data. Data science can help make predictions based on the data. It can also be analysed by using regression, text mining, and qualitative analysis methods. 


It is essential to display the results of your analysis to gain utility from the data. This can be done using reports consisting of the results of research and analysis of the data. It can also be visualised by representing it graphically. This can help identify trends, patterns, and outliers in data. Most importantly, it helps companies to produce actionable insights.

How To Get Started With Data Science?

Our guide to data science can help to start a data science venture, but maintaining it efficiently is a difficult task. Therefore, even before you start your company, you need to plan ahead of time to ensure the smooth running of your company. Here are a few tips to get your data science operation started and help it stand out from the crowd:

Even if you do not want to start a company that solely focuses on data science services, there are numerous benefits of integrating data science into your existing operations. Here are some applications of data science that have resulted in numerous benefits for several organisations:

We Transform Organizations With The Innovation Of Data Science

How To Execute A Successful Data Science Strategy?

Even though our guide to data science rapidly gaining popularity among businesses and IT leaders, many companies are having a hard time implementing and executing their data science strategies. The following steps will help you effectively execute your science strategy

Identify Key Business Drivers

Understand why your company needs data science and how it can contribute to the success of your business.

Build An Effective Team

Create a stable team consisting of multiple talented individuals, rather than a team of one or two experts who can do it all.

Emphasize Communication Skills

The insights provided by data science analytics are of no value unless their value can be properly articulated. Communication is the most critical element that contributes to the success of an organization.

Improve Data Science Processes

Your data science team must continually focus their efforts towards decreasing the time taken to develop and deploy new analytical models.

Protect Your Data

Implement governance policies to ensure the security of sensitive data during data science processes. Personally, identifiable information should not fall into the wrong hands under any circumstances.

Michael Wu

Chief AI Strategist at PROS

To get started with data science, you need 3 things: a list of high-impact business problems, a detailed catalog of the data asset that you have accumulated and can access immediately, and a good understanding of the data science talent in your organization.

Once you have these 3 things, you need to find where these 3 sets intersect. That is you need to identify the problem that you can address with the data you have now through your data science talent.

Why Mindbowser For Data Science

Our lean and agile team of full-stack data scientists, engineers, and application developers accelerate innovation and implementation of custom machine learning and AI products. We bring extensive cross-industry expertise backed by scientific rigor and deep knowledge of state-of-the-art techniques to design, build, and deploy bespoke AI solutions.



Data science is a relatively new technological field and the interest in the field has grown over time. As this technology advances and even more enormous amounts of data keep getting generated, we can use artificial intelligence and machine learning to extract meaningful insights and better predict future patterns and behaviours. As the importance of Data science in our lives grows, the future of data science will be essential to our safety and security in the future.  

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This blog is from Mindbowser‘s content team – a group of individuals coming together to create pieces that you may like. If you have feedback, please drop us a message on contact@mindbowser.com
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