Introduction to Data Science

Many companies rely on data science due to the volume of data being created. As a result, it is one of the most contested subjects in IT circles. However, the popularity of data science has expanded over time, and businesses have begun to use data science approaches to develop their businesses and boost consumer happiness. In this chapter, we’ll define data science and discuss how to become a data scientist.

What Is Data Science?

We can define data science as a collection of tools, algorithms, and machine learning methods to uncover hidden patterns in raw data. It is very different from what statisticians have done for years.

A statistician often explains the present trends by analyzing past data. However, a Data Scientist does exploratory research to uncover insights and employs numerous advanced machine learning algorithms to predict the future recurrence of a specific event. Therefore, a data scientist must examine the data from various perspectives, including those previously unknown.

As a result, Data Science generates judgments and predictions using predictive causal analytics, prescriptive analytics, and machine learning.

• Predictive Analytics: If you want a model that can forecast the likelihood of a specific occurrence in the future, use predictive causal analytics. For example, if you lend money on credit, you are concerned about your client’s capacity to make future credit payments on time. Here, you may create a model that can do predictive analytics on the customer’s payment history to anticipate whether or not they will make future payments on time.

• Prescriptive analytics: Prescriptive analytics is necessary if you want a model with the intelligence to make its judgments and the capacity to alter it using dynamic parameters. This relatively young profession is all about giving advice. In other words, it predicts and offers a set of recommended behaviors and results.

The best illustration is Google’s self-driving automobile. First, Google uses vehicle data to program self-driving or driverless cars. Then, data scientists run algorithms on this data to give it the necessary commands. This will let your car understand when to turn, which course to take, and whether to slow down or accelerate.

• Machine learning for prediction: If you have transactional data from a finance organization and need to develop a model to anticipate future trends, machine learning algorithms are your best choice. This comes under the supervised learning paradigm. We refer to it as “supervised” because you already have data on which to train your computers. A fraud detection model, for example, can be prepared using a historical record of fraudulent purchases.

• Machine learning for pattern discovery: If you don’t have the parameters from which to create predictions, you must identify hidden patterns within the dataset to make meaningful predictions. It is unsupervised because the groupings have no predetermined labels. Clustering is the most often used pattern detection technique.

Assume you work for a telephone business and need to build a network by placing towers around an area. Then, using the clustering approach, you may select tower sites to ensure that all customers receive optimal signal strength.

The Data Science Lifecycle

With a basic understanding of data science, let us look at the data science lifecycle. The data science lifecycle has five stages, each with its tasks:

1. Capture: This stage includes data acquisition, data entry, signal reception, and data extraction. This step entails collecting both organized and unstructured data.

1. Maintain: This stage includes data warehousing, data cleansing, data staging, data processing, and data architecture. During this phase, raw data must be gathered and transformed into a format that can be used.

1. Process: It includes data modeling, data summarization, clustering/classification, and data mining. Data scientists assess the generated data for patterns, ranges, and biases to determine its usefulness in predictive analysis.

1. Analyze: Exploratory and confirmatory analysis, predictive analysis, regression, text mining, and qualitative analysis are all part of the analyses. The core of the life cycle is this. The data must be subjected to numerous analyses during this phase.

1. Communicate: Data reporting, data visualization, business intelligence, and decision-making analysts present the studies in clearly legible forms like charts, graphs, and reports in this last stage.

Prerequisites for Data Science

Before beginning the data science journey, you should know the following technical terms.

• Machine Learning

It is at the heart of data science. Therefore, data Scientists must be well-versed in ML and have a basic understanding of statistics.

• Modeling

Mathematical models allow you to make rapid calculations and predictions based on prior data. Modeling is a subset of machine learning that entails determining which algorithm is best for solving a particular issue and how to train these models.

• Data and Statistics

Statistics are fundamental to data science. A firm grasp of statistics can assist you in extracting more intelligence and obtaining more relevant outcomes.

• Computer Programming

Some amount of programming experience is required to complete a decent data science project. Python and R are the most popular programming languages. However, python has a clear advantage because it is simple to learn and includes several data science and machine learning libraries.

• Databases 

A competent data scientist must understand how databases operate, maintain, and extract data.

What Does a Data Scientist Do?

A data scientist is simultaneously a mathematician, computer scientist, and business strategist. This complicated skill set requires data scientists always to have one foot firmly planted in the information technology sector while the other is in the business world. This is part of what makes this skill so valuable and why becoming a data scientist is one of the best career choices.

Data science is concerned mainly with deep knowledge discovery through data exploration and inference. A skilled data scientist must be well-versed in statistics and computer skills. This field focuses on employing mathematical and computational tools to solve some of the most analytically complicated business issues, harnessing vast amounts of raw data to uncover hidden insights.

The foundation of the data science area revolves around exact and often minutia-driven analysis, as well as the development of strong decision capabilities. It may be a slow and solitary career at times. However, data scientists must also have excellent verbal, written, and visual communication skills. These skills will be required to present their findings to their superiors, colleagues, and company stakeholders, who may or may not be able to understand complex statistical jargon.

As a data scientist, one must describe what they’ve discovered, how they discovered it, and what we can do once they know it, all in an easy-to-understand manner. But unfortunately, it is not always a simple process.

A data scientist extracts data from a database, prepares the data for various analyses, developing and testing a statistical model, or providing reports with clearly understood data visualizations on any given day. While data science projects and responsibilities may differ based on the organization, there are several basic job activities that all data science professionals share, such as.

• The data scientist must determine the problem by asking the right questions and acquiring insight before collecting and analyzing data.
• The data scientist then determines which variables to use and data sets to analyze.
• For the next stage, the data scientist collects structured and unstructured data from various sources, including company data, public data, and so on.
• After gathering the data, the data scientist analyses it and turns it into an analysis-ready format. This entails simplifying and verifying the data to ensure consistency, completeness, and correctness.
• The data is supplied into the analytic system—an ML algorithm or a statistical model—once it has been deemed valid. Data scientists use this stage to investigate and find patterns and trends.
• After analyzing the data, the data scientist examines it to identify possibilities and solutions.
• The data scientists finish the job by spreading the findings and preparing the findings and insights for dissemination to the appropriate stakeholders.

We should now be aware of specific machine learning techniques that can help us grasp data science more clearly.

Why Become a Data Scientist?

You now understand what data science is. Did it sound interesting? Here’s another compelling reason to consider data science as a career. According to Glassdoor and Forbes, demand for data scientists will rise by 28% by 2026, indicating the profession’s durability and endurance; thus, if you desire a solid career, data science provides that opportunity.

Furthermore, data scientist was ranked second in the Best Jobs in America for 2021 study, with an average base income of USD 127,500.

So, a data scientist is the best opportunity if you want an intriguing profession with security and great pay!

Where Do You Fit in Data Science?

Data science allows you to focus on and specialize in one part of the industry. Here are some examples of how you may fit into this attractive, fast-growing area.

Data Scientist

• Job Role: Determine the nature of the problem, what questions you must answer, and where you can find the data. They also mine, sanitize, and show pertinent data.

• Skills Needed: Programming abilities (SAS, R, Python), narrative and data visualization, statistical and mathematical expertise, and knowledge of Hadoop, SQL, and Machine Learning are required.

Data Analyst

• Job Role: Analysts bridge the gap between data scientists and business analysts by organizing and evaluating data to answer queries posed by the enterprise. They translate technical analyses into qualitative action items.

• Skills Needed: Statistical and mathematical expertise, computer skills (SAS, R, Python), data wrangling, and visualization experience are the necessary skills.

Data Engineer 

• Job Role: Data engineers are responsible for creating, installing, managing, and optimizing the organization’s data infrastructure and data pipelines. Engineers assist data scientists by transferring and transforming data for queries.

• Skills Needed: NoSQL databases, programming languages such as Java and Scala, and frameworks are essential skills.

Data Science Tools

The data science profession is difficult, but many tools are available to assist data scientists in thriving.

• Data analysis: Its tools include SAS, Jupyter, R Studio, MATLAB, Excel, and RapidMiner.
• Data Warehousing: Informatica/ Talend, AWS Redshift
• Data Visualization: Jupyter, Tableau, Cognos, RAW
• Machine Learning: Spark MLib, Mahout, Azure ML studio

Difference between Business Intelligence and Data Science

You now understand what data science is, the distinction between business intelligence and data science, and why you cannot use the terms interchangeably. Corporate intelligence is a set of tactics and techniques to analyze business data/information. It may give historical, present, and predictive views of corporate processes like data science. There are, nevertheless, some significant variances.

Applications of Data Science

Data science has applications in nearly every business.

1. Medical care: Healthcare organizations use data science to develop advanced medical equipment for illness detection and treatment.

1. Gaming: Video game development firms use data science to build video games to enhance the gaming experience.

1. Image Recognition: One of the most crucial data science applications is recognizing photo patterns and finding objects in images.

1. Recommendation System: Netflix and Amazon recommend movies and products depending on what you watch, buy, or explore on their platforms.

1. Logistics: Organizations employ logistics data science to improve routes to ensure faster product delivery and increased operational efficiency.

1. Fraud Detection: Banks and financial institutions utilize data science and associated algorithms to detect fraudulent transactions.

Data Science Use Cases

Here are some quick overviews of a few application examples demonstrating data science’s adaptability.

Law Enforcement: Belgian police use data science to better understand where and when to deploy officers to prevent crime. Data science dashboards and reports boosted officers’ situational awareness as a dispersed police force faced limited resources and a wide area to cover.

Fighting the Pandemic: The governor of Rhode Island sought to reopen schools but was understandably wary given the ongoing COVID-19 pandemic. Instead, the government employed data science to speed up case investigations and contact tracking, allowing a small team to manage an enormous volume of worried citizen calls. In addition, this data aided the state in establishing a call center and coordinating preventative actions.

Driverless Vehicles: Lunewave, a sensor manufacturing business, sought a means to reduce the cost and accuracy of sensor technology. They used data science and machine learning to teach their sensors to be safer and more dependable and to optimize the manufacturing process for 3D-printed sensors.