What is Data Science?

Introduction

There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days. — Eric Schmidt, Executive Chairman at Google

This quote sums up the sheer volume of data we are producing on a daily basis. With the advent of digitalization and the arrival of the Internet of Things (IoT), this number is only going to grow further. Based on a report by International Data Corporation (IDC), our world is estimated to generate around 175 zettabytes of data. Let’s see some facts about how much data is produced and processed each day by tech giants or large organizations -

• Google processes 8.5 billion searches every day.
• WhatsApp users exchange up to 65 billion messages daily.
• 720,000 hours of video content are uploaded to YouTube every day.
• Each day 95 million photos and videos are shared on Instagram.
• Google, Facebook, Microsoft, and Amazon store at least 1,200 petabytes of information.
• Electronic Arts (EA) processes roughly 50 terabytes of data every day.

In today’s world, success for any organization is strongly associated with how they leverage the data they generate. It has become paramount for organizations worldwide to process large amounts of data and extract valuable insights that can help them better understand their customers and how they relate to the business's products or services. This has resulted in the demand for Data Science professionals surging in recent years. Data Science can be defined as a process to extract valuable insights from data by applying scientific methods to it. The Data Science field is growing rapidly and has revolutionized many industries in the past. For e.g., Data Science helps Netflix save around 1 billion USD every year on customer retention. Based on a report by NewVantage, 97.2 percent of organizations are investing in Big Data and Artificial Intelligence based solutions to grow their business and improve customer experience. Data Science is probably the most talked about the keyword in the 21st century and has become an essential part of any organization worldwide.

In this article, we will discuss what is Data Science, why it is important, what are various roles associated with Data Science, Data Science applications, etc.

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What is Data Science?

• Data Science is the field of study to process the data residing in the organization’s repositories by applying various scientific methods.
• Data Science is a discipline that brings together Statistics, Data Analysis, Machine Learning, Computer Science, and their related methods to collect, process, and analyze the data and discover the underlying patterns to extract valuable insights.
• The Data Science process can be divided into multiple stages. The Data Science lifecycle consists of 6 steps, as shown in the diagram below.
  • Business Requirements - Any Data Science process starts with an understanding of the various business requirements and defining the problem statements. For e.g., building a recommender system to improve customer experience and engagement, predicting customer churn to improve customer retention, etc.
  • Data Acquisition - Once the business requirements are understood and formulated into a Data Science problem, the next step is to identify and gather relevant data sources. For e.g., collecting customers’ purchases and browsing history for building the product recommender system.
  • Data Processing - In this step, raw data gathered from the previous step is transformed into a suitable format that can be further processed, explored, and analyzed.
  • Data Exploration - In this step, Exploratory Data Analysis (EDA) is performed using various statistical and visualization methods to discover the underlying patterns and trends in the data.
  • Modeling - In this step, Machine Learning algorithms are used to build predictive or prescriptive models. This entire process includes cleaning & preparation of the data and training, testing & evaluating the Machine Learning model.
  • Deployment - Once the Machine Learning model is trained and evaluated, it is deployed in the business processes.

Why Data Science is Important?

There is no doubt that in today’s world, Data has become the most important asset for any organization. In today’s world, the success of any organization is strongly linked to how it utilizes and processes data. It has become paramount for organizations worldwide to process and analyze large amounts of data to extract valuable insights that can help them grow and succeed.

Data can be defined as raw information that can be captured, stored, and analyzed. Data is meaningless until it is converted into meaningful information. Data Science helps organizations analyze large amounts of structured and unstructured data and identify hidden patterns to extract actionable insights. As Organizations worldwide have realized the true importance of data, Data Science has become an essential part of their decision-making and strategic planning processes. The importance of Data Science lies in its innumerable applications that range from daily activities like asking Siri or Alexa for recommendations to more complex applications like a self-driving car.

Data Science has transformed many industries in the past and helped organizations become profitable with its various applications. We have listed some ways below how Data Science can help an organization -

• Interpreting large volumes of Data
• Faster and better decision-making
• Better marketing and sales strategies
• Identify target audience
• Reduce operating costs
• Personalized customer services
• Promotes automation

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Who Oversees the Data Science Process?

In most organizations, Data Science projects are typically supervised by three types of managers.

• Business Managers - Business Managers could be the head of a department in the organization such as marketing, sales, finance, product, etc. They work with the Data Science team to define their business needs and problems and develop a Data Science strategy to solve them. They work closely with the Data Science and IT Managers to ensure that projects are timely delivered.
• IT Managers - Senior IT managers are responsible for providing infrastructure and architecture to support various Data Science operations. They are also responsible for maintaining, monitoring, and updating IT infrastructure and architecture to ensure that Data Science teams operate efficiently and securely.
• Data Science Managers - Data Science managers are responsible for building and managing the Data Science team and their day-to-day work. This team typically consists of various Data Science professionals such as Data Scientists, Data Engineers, Data Analysts, etc.

The Data Science process is generally supervised by various types of managers mentioned above but the most important player in this process is the Data Scientist. In the next section, let’s understand who is a Data Scientist, and what a Data Scientist does.

Who is a Data Scientist and What Does a Data Scientist Do?

Data Scientists are the practitioners of the Data Science discipline, who are responsible for solving various business problems by examining large amounts of data residing in the organization’s repositories by applying various scientific methods. They collect, process, and analyze large amounts of structured and unstructured data from a business point of view and apply various methods, such as statistics, and machine learning, etc. to derive valuable insights that can help drive business decisions.

Data Scientists combine software engineering and statistics concepts to convert raw data into meaningful and actionable information. Data Scientist was a completely unknown term just a decade ago but as organizations realized the true importance of data, demand for Data Scientists has skyrocketed in recent years. Harvard Business Review has already regarded data scientists as the sexiest job of the 21st century. And for three years in a row, it is named the number 1 job in the USA by Glassdoor.

The fundamental responsibilities of a Data Scientists include -

• Understanding Business Problems - Data Scientists apply their business acumen/domain expertise to understand various business problems by having open-ended discussions with multiple stakeholders or performing an extensive literature survey.
• Data Collection - Post understanding business requirements, Data Scientists, translate them into Data Science problems. To solve them, Data Scientists find relevant data sources and gather a large volume of structured or unstructured data stored in different databases. Data Scientists use various programming languages or methods, such as SQL, Web Scraping, APIs,, etc. for data collection.
• Data Preparation - Post data collection, Data Scientists, employ various sophisticated statistical or analytical methods such as discarding irrelevant information, handling outliers, imputing missing values, etc. to clean and prepare data for further analysis.
• Data Exploration - Once data is cleaned and prepared, Exploratory Data Analysis (EDA) is performed by Data Scientists on data by using various statistical (mean, mode, standard deviation, correlation, p-value test, etc.) or visualization methods (histograms, density plot, scatter plots, box plots, bar charts, etc.) to detect and understand underlying patterns.
• ML Model Development - It is one of the most important responsibilities of a Data Scientist job. Data Scientists use various programming languages such as Python, R, etc. to build and develop machine learning-based predictive or prescriptive models.
• Communication - Once data is analyzed and insights are derived, Data Scientists communicate their findings to business stakeholders or management and recommend changes to existing procedures or strategies to solve business problems.
• Stay Up to Date - Data Science is a fast-evolving field and to excel in this field, Data Scientists need to stay tuned with ongoing research in related fields such as Machine Learning, Deep Learning, Natural Language Processing, other analytical techniques, etc.

Prerequisites to Become a Data Scientist

To become a Data Scientist, you need to learn and master specific technical skills. The most essential skills for a Data Scientist include -

Machine Learning

• It is the most crucial skill to have for Data Scientists as they are required to build and develop machine learning-based predictive and prescriptive models.
• Data scientists must have an advanced understanding of underlying mathematical concepts and fundamentals of a wide range of Machine Learning algorithms spanning classification, regression, clustering, deep learning, etc.

Statistics and Mathematics

• Statistics and Mathematics are the core of most Data Science techniques and solutions such as Machine Learning, Data Analysis, etc. Therefore, Data Scientists must develop an in-depth understanding of various statistical methods such as correlation, p-value, A/B testing,, etc., and mathematical concepts such as Linear Algebra, Calculus, etc to execute various Data Science tasks.

Programming Languages

• Data Scientists use various programming languages for a variety of Data Science tasks such as collecting and preparing data, machine learning model development, exploratory data analysis, feature engineering, etc. Therefore, it is one of the most essential skills to have for a Data Scientist.
• Python, R, SQL, Scala, and SAS are some of the most popular programming languages used by Data Scientists. Developing knowledge of other advanced programming languages, such as C++, Java, etc. is an added advantage for a Data Scientist.

Data Visualization

• Visualization is a key part of a Data Scientist’s job to identify underlying patterns in the data by plotting the data using charts and graphs.
• Data Scientists should be familiar with various visualization tools such as Python, or R visualization libraries, Tableau, PowerBI, Excel, etc.

Big Data Frameworks

• Data Scientists frequently deal with large amounts of data. Data Scientists should have familiarity with various Big Data processing frameworks such as Apache Spark, Hadoop, etc. This will enable them to deal with large amounts of data efficiently and quickly.

Different Data Science Roles in the Industry

Let’s explore various Data Science roles, their job descriptions, and the skills required in the below table -

Data Science Uses (Applications)

Data Science has become an integral part of nearly every organization worldwide. There is no doubt that every single industry worldwide is dependent on data for its growth and success. However, Data Science applications have not evolved overnight. With the advent of faster computing and cheaper storage, we can now process complex data types such as images, text, etc., and train complex Deep Learning and Machine Learning models on large amounts of data within a reasonable timeframe. Due to this, Data Science has transformed almost all industries and has now become an integral part of their decision-making and strategic planning process.

Let’s explore a few popular applications of Data Science in the table mentioned below -

Data Science Tools

Data Science Tools help implement various steps involved in a Data Science project, such as Data Analysis, Data Collection from databases and websites, Machine Learning model development, communication results by building dashboards for reporting, etc. The most popular tools used in Data Science include -

Python

• Python is the most popular and widely used programming language among Data Scientists. One of the main reasons for Python’s popularity in the Data Science community is because of its ease of use and simplified syntax, which makes it easy to learn and adapt for people having no engineering background. The most popular Python libraries used in Data Science are Pandas, NumPy, SciPy, matplotlib, Seaborn, Scikit-learn,, etc.

R

• After Python, R is the second most popular programming language used in the Data Science community. It was initially developed to solve the statistical problem but has now evolved into a complete Data Science ecosystem. A few of the most popular R libraries are ggplot2, dpylr, readr, etc.

SQL

• SQL stands for Structured Query Language that is used by Data Science professionals to query, update, and manage relational databases and extract data.

Spark

• A product of the Apache foundation, Spark is an open-source analytics engine used by Data Science professionals to process Big Data efficiently.

Jupyter Notebook

• Jupyter Notebook is an open-source web application that allows interactive collaboration among Data Scientists, Data Engineers, and other Data Science professionals. It also supports all the major programming languages used by Data Science professionals.
• It provides a document-centric experience where you can write the code, visualize the data, and showcase your results in a single-page document, known as a notebook.

Difference between Business Intelligence and Data Science

Business Intelligence is a process of analyzing business data for quick decision-making and actions. While Data Science is responsible for extracting valuable insights from structured and unstructured data by applying advanced analytical and scientific methods. Let’s understand the difference between these two fields in more detail in below table -