Coursera Learner working on a presentation with Coursera logo and
Coursera Learner working on a presentation with Coursera logo and

Thanks to consistent advancements in technology, big data allows various fields to derive information and consecutive trends to predict behavior. As we gather more and more data to develop the new field, we require storage to save new data and develop new data with previous records. However, to store the data, IT experts have worked on developing various solutions and frameworks. This raised other questions, such as how we can process the data effectively. That is where the term data science comes in. For instance, in Hollywood Sci-Fi movies, we see how the characters rely on Data Science to accomplish difficult missions. Similarly, today’s world needs to use data science for various data-driven tasks.

What is Data Science?

Data science helps analyze a large amount of data and find solutions. Using these solutions, organizations make informed decisions and maximize their success rate. Data science’s main goal is to process the data and generate a visual representation that supports decision-making accuracy. Here are some of the functions of data science:





Fraud detection

Actionable insights



Life Cycle of Data Science

  1. Understanding

In this phase of data science, you need to ask questions. These questions relate to the field in which the organization operates. For instance, if you are a business data scientist, you will focus on data that supports every decision in the business to achieve maximum results. When trying to understand a problem, you need to ask a few questions:

  • How many?
  • What is the category?
  • What is the group?
  • Is it okay or odd?
  • What is the option we should take?

In short, you need to define the objective of the project you are assigned. This will help you find the best solution and your organization makes a suitable decision.

  1. Data Mining

After data, scientists find the objective of the problem or the project, and they start to collect data relating to the questions. They will find the solution to new questions such as: 

  • Where we can find the data?
  • What type of data will support the solution better?
  • What methods we can use to find the data?
  • How can we store the data for future reference?

This is the most time-consuming step  in the cycle. However, various new methods, techniques, and tools are in development to make this phase easier. You can use these tools to gather the data in less time with accuracy. For instance, if you collect the data to develop a mobile application, you need to go through the user experience with the competition, what problems users face that this application can solve, etc.

  1. Data Cleaning

The data you collect is in huge chunks. Some may relate to the topic more than others. You need to analyze the data and eliminate all the additional data. When you gather big data, you will get every piece of information related to the topic. This doesn’t mean you will use all of it to solve the problem. Hence, it’s time to extract all the useful data.

While eliminating less important data, you may find that some data is missing. If you don’t solve this problem while cleaning the data, you may face a problem later on.

  1. Data Exploration

Data analysis is also an essential step for data scientists. You need to explore the data and brainstorm. Connect the patterns, statistics, figures, and facts in the data that you collect. Creating graphs, histograms, and graphical presentation will help to explore the story behind the data.

You will use all the information to find any pattern or connection between the data. For instance, if your data is about real estate conditions in a city, you can design a heat map and try to find trends. You’re making graphical representations, so the information should be as accurate as possible for better results.

  1. Feature Engineering

In machine learning, features are the measurable properties and the one attributed when observed. Similarly, in this step, cut down features involving too much noise. You will use the data and apply filtering methods and create a feature. For instance, if the feature you require is age and the threshold you can select is the adult and child. So you will choose a threshold age of 18 and mark the category above or below the threshold.

  1. Predictive Modeling

Now, you will start to get the model of the project according to data science. A good model includes a statistical test to measure if the data is accurate and makes sense or not. You need to train your model and set the right algorithm, so the system runs automatically. Once the model is all set, you need to evaluate how accurate the results are. 

  1. Data Visualization

This is the most difficult step in the life cycle. This step includes the presentation of the data combining art, statistics, psychology, and communication skills. You need to design the outcome so that the people who receive the information can understand. The essential thing to consider in this method is communication.

  1. Understanding

After you go through all the processes, you come to a full circle, and draw your conclusions of the model. You need to evaluate the success of the model to understand actual problems. If you find out that you lack any of the information and insight, you can repeat the process to find even more data and insight to improve the project results.


To achieve goals, build strategies, design models, solve problems, data science is an essential and progressive field. Companies can gather a lot of data and utilize it to make a process that helps them make better decisions. For the success of a project or growth of the business, data scientists have a major effect on the success and positive impact. Hopefully, this article gave you an answer to the question, “what is data science?”


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