What is Data Science?

Do you remember when you planned on opening a new branch of your Italian restaurant but couldn’t decide where to? 

Imagine if you had a way to assess and compare different suburbs to identify where most recently, the Italian population started settling! That should be a promising place to open your new branch.

Or how about determining a reasonable and fair price for your house to sell, based on locality, prevailing rates, number of bedrooms, number of baths, or maybe that beautiful garden you have that no one else has!

By the simplest definition, Data Science is a process to extract meaning insight from raw data. 

In 2013, a study discovered that about 90% of all data in the world is generated in the past two years. By estimates, End of the year 2020 will see the size of our digital universe expended to 44 zettabytes.

So, it begs the question, what can we do with all this data? How do we make it useful to us? How can we make use of this data to answer the questions we have and solve real-world problems? All these questions come under the domain of Data Science.

How Data Science Works?

The field of Data Science involves a plethora of disciplines and expert domains to offer a complete and refined look into the raw data. The experts of the Data Science field are known as Data Scientists.

They have skills ranging from maths, statistics, programming, and visualization to produce muddled masses of information to deliver only the insights that can address the problem and help increase efficiency.

Data Scientists also utilize AI, especially in the sub-field of machine learning and deep learning. Applications of these technologies help Data Scientists to create models and perform predictions using algorithms.

Data science lifecycle typically has 7 stages:

Data Science Lifecycle
Data Science Lifecycle
  1. Business Understanding: Ask relevant questions and define objectives for the problem that needs to be tackled
  2. Mining Data: Gather and scope the data for the project
  3. Data Cleaning: Fix the inconsistencies within the data and handle the missing value
  4. Exploration: Form hypothesis about your defined problems by visually analyzing the data
  5. Feature Engineering: Select important features and construct more meaningful ones using the raw data
  6. Predictive Modeling: Train machine learning models, evaluate their performance, and use them to make predictions
  7. Data Visualizations: Communicate the findings with key stakeholders using plots and interactive visualizations.

The above stages require different sets of tools, programs, and techniques, and in some cases, different sets if skillset altogether.

Use Cases

Just a few years ago, some of these use cases were not possible or required a significant amount of time and energy, But now with Data Science it is possible:

  • Anomaly detections like Fraud, Disease, crime, etc.
  • Decision Making and Automation of background checks and creditworthiness
  • Classifications such as marking emails as “Important” or “Junk”
  • Forecasting your company sales, revenues, and customer retention
  • Detecting patterns in weather, financial markets, etc.
  • Voice and Image recognitions
  • Recommendations based on user profiles, buying or viewing habits, locality, etc

Businesses are using Data Science practices to innovate their sectors, create new experiences and products, all the while making the world around us more efficient.