Data science is an essential part of any industry today, given the massive amounts of data that are produced. Data science is one of the most debated topics in the industries these days. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we’ll learn what data science is, and how it works.
A groundbreaking study in 2013 reported 90% of the entirety of the world’s data has been created within the previous two years. Let that sink in. In just two years, we've collected and processed 9x the amount of information than the previous 92,000 years of human-kind combined. And it isn’t slowing down. It’s projected we’ve already created 2.7 zettabytes of data, and by 2022, that number will balloon to an astounding 84 zettabytes.
What do we do with all this data? How do we make it useful to us? What are its real-world applications? These questions are the domain of data science.
Every company will say they’re doing a form of data science, but what exactly does that mean? The field is growing so rapidly, and revolutionizing so many industries, it's difficult to fence in its capabilities with a formal definition, but generally, data science is devoted to the extraction of clean information from raw data for the formulation of actionable insights
Commonly referred to as the “oil of the 21st century," our digital data carries the most important in the field. It has incalculable benefits in business, research, and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights, and making our lives more convenient.
Broadly, Data Science can be defined as the study of data, where it comes from, what it represents, and the ways by which it can be transformed into valuable inputs and resources to create business and IT strategies.
The image represents the five stages of the data science life cycle:
Capture: data acquisition, data entry, signal reception, data extraction.
Maintain: data warehousing, data cleansing, data staging, data processing, data architecture.
Process: data mining, clustering/classification, data modeling, data summarization
Analyze: exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis
Communicate: data reporting, data visualization, business intelligence, decision making.
All five stages require different techniques, programs, and, in some cases, skillsets.
Data science involves a plethora of disciplines and expertise areas to produce a holistic, thorough, and refined look into raw data. Data scientists must be skilled in everything from data engineering, math, statistics, advanced computing, and visualizations to be able to effectively sift through muddled masses of information and communicate only the most vital bits that will help drive innovation and efficiency.
Data scientists also rely heavily on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques.
Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML in addition to basic knowledge of statistics.
Should be aware of some machine learning algorithms which are beneficial in understanding data science clearly. The most basic and essential ML algorithms a data scientist use include:
Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of ML and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.
Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.
Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.
As a capable data analyst, you need to understand how databases work, how to manage them, and how to extract data from them.
|Data Analysis||R, Python, Statistics||SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner|
|Data Warehousing||ETL, SQL, Hadoop, Apache Spark||Informatica/ Talend, AWS Redshift|
|Data Visualization||R, Python libraries||Jupyter, Tableau, Cognos, RAW|
|Machine Learning||Python, Algebra, ML Algorithms, Statistics||Spark MLib, Mahout, Azure ML studio|
We have come a long way from working with small sets of structured data to large mines of unstructured and semi-structured data coming in from various sources. The traditional Business Intelligence tools fall short when it comes to processing this massive pool of unstructured data. Hence, Data Science comes with more advanced tools to work on large volumes of data coming from different types of sources such as financial logs, multimedia files, marketing forms, sensors and instruments, and text files.
Mentioned below are relevant use-cases which are also the reasons behind Data Science becoming popular among organizations: