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.
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 summarise the data to create a concise description of the dataset.
After classifying and modelling the data, the next step is to analyse the data. Using data analytics 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?
It is easy 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: