What is data science? | Built in

What is data science?

Data science is a discipline that combines mathematics, statistics, artificial intelligence and computer science to process large volumes of data and determine patterns and trends. With these insights, organizations can better understand why certain events happen and develop more informed decision-making processes.

Data science is the field of data scientists, who often rely on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques.

Why is data science important?

Data science makes it possible to analyze large amounts of data and spot trends through formats such as data visualization and predictive models. Given the ability to take proactive measures, businesses can then make smarter decisions, design more efficient operations, improve their cybersecurity practices and, as a result, provide better customer experiences. Teams are already applying data science across a range of scenarios such as diagnosing diseases, detecting malware and optimizing transport routes.

What is data science used for?

Data science is used to look for connections and patterns within complex information, leading to insights that businesses can then use to make better decisions. More specifically, data science is used for complex data analysis, predictive modeling, recommendation generation, and data visualization.

Analysis of complex data

Data science allows for fast and precise analysis. With various software tools and techniques at their disposal, data analysts can easily identify trends and detect patterns within even the largest and most complex data sets. This enables businesses to make better decisions, whether it’s about how best to segment customers or conducting a thorough market analysis.

Predictive modeling

Data science can also be used for predictive modeling. Essentially, by finding patterns in data through the use of machine learning, analysts can predict possible future outcomes with some degree of accuracy. These models are particularly useful in industries such as insurance, marketing, healthcare and finance, where predicting the likelihood of certain events occurring is central to the success of the business.

Recommendation Generation

Some companies—like Netflix, Amazon, and Spotify—rely on data science and big data to generate recommendations for their users based on their past behavior. It is thanks to data science that users of these and similar platforms can be served content tailored to their preferences and interests.

Data visualization

Data science is also used to create data visualization—think graphs, charts, dashboards—and reporting, which help non-technical business leaders and busy managers easily understand otherwise complex information about the state of their business.

What are the benefits of data science?

Industries are realizing the benefits of using data science, including these general benefits.

Improved decision making

Being able to analyze and gain insights from massive amounts of data gives leaders an accurate understanding of past developments and concrete evidence to justify their decisions moving forward. Companies can then make sound, data-driven decisions that are also more transparent to employees and other stakeholders.

Increased efficiency

By collecting historical data, businesses can identify workflow inefficiencies and devise solutions to speed up production. They can also test different ideas and compile data to see what works and what doesn’t. With a data-first approach, companies can then design processes that maximize productivity and minimize unnecessary work and costs.

Complex data interpretation

Data science allows for handling large volumes of complex data, which businesses can then use to build predictive models for anything from predicting customer behavior to predicting market trends. If other organizations cannot extract insights from complex data, companies that do have the distinct advantage of being the first to anticipate upcoming events and prepare accordingly.

Better customer experience

Collecting data on customer behavior enables companies to determine customer buying habits and product preferences. Teams can then use this data to design personalized customer experiences. For example, businesses can create marketing campaigns tailored to certain demographics, offer product recommendations based on a customer’s previous purchases, and customize products according to customer usage and feedback.

Enhanced cyber security

Data science tools give teams the ability to monitor large volumes of data, making it easier to spot anomalies. For example, financial institutions can review transactional data to determine suspicious activity and fraud. Security teams can also collect data from network systems to detect unusual behavior and catch cyber attacks in their early stages.

What is the data science process?

Data science is typically thought of as a five-step process, or life cycle:

1. Capture

This stage is when data scientists collect raw and unstructured data. The capture stage typically includes data acquisition, data input, signal reception, and data extraction.

2. Maintain

This stage is when data is put into a form that can be utilized. The maintenance stage includes data warehousing, data cleansing, data processing, data processing and data architecture.

3. Process

This stage is when data is examined for patterns and biases to see how it will work as a predictive analytics tool. The process phase includes data mining, clustering and classification, data modeling and data summarization.

4. Analyze

This stage is when various types of analyzes are performed on the data. The analysis stage involves data reporting, data visualization, business intelligence and decision making.

5. Communicate

This stage is when data scientists and analysts present the data through reports, charts and graphs. The communication stage typically includes exploratory and confirmatory analysis, predictive analysis, regression, text mining and qualitative analysis.

What are data science techniques?

There are many data science techniques that data science professionals must be familiar with in order to do their jobs. These are some of the most popular techniques:

Regression

Regression analysis allows you to predict an outcome based on multiple variables and how those variables affect each other. Linear regression is the most commonly used regression analysis technique. Regression is a type of supervised learning.

Classification

Classification in data science refers to the process of predicting the category or label of different data points. Like regression, classification is a subcategory of supervised learning. It is used for applications such as email spam filters and sentiment analysis.

Grouping

Clustering, or cluster analysis, is a data science technique used in unsupervised learning. During cluster analysis, closely associated objects within a data set are grouped together, and then each group is assigned attributes. Clustering is done to reveal patterns within data – typically with large, unstructured datasets.

Anomaly Detection

Anomaly detection, sometimes called outlier detection, is a data science technique in which data points with relatively extreme values ​​are identified. Anomaly detection is used in industries such as finance and cyber security.