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Data Science

DATA SCIENCE

What is

Data Science?

Data science is a multidisciplinary field of study that applies techniques and tools to draw meaningful information and actionable insights out of noisy data. Involving subjects like mathematics, statistics, computer science and artificial intelligence, data science is used across a variety of industries for smarter planning and decision making.

Data science is the realm 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.

Data Science Definition

BASICS OF DATA SCIENCE

Data Science Used for

Data science is used by businesses of all kinds, from Fortune 50 companies to fledgling startups, to look for connections and patterns and deliver breakthrough insights. That explains why data science is a rapidly growing field and revolutionizing many industries. 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 quick 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 datasets. This enables businesses to make better decisions, whether it’s regarding how to best segment customers or conducting a thorough market analysis.

Predictive Modeling

Data science can also be used for predictive modeling. In essence, by finding patterns in data through the use of machine learning, analysts can forecast possible future outcomes with some degree of accuracy. These models are especially useful in industries like insurance, marketing, healthcare and finance, where anticipating the likelihood of certain events happening is central to the success of the business.

Data Visualization

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

Data Science Tools

Python, R, SQL.

C/C++

Popular Data Science Tools.

Apache Spark (data analytics tool), Apache Hadoop (big data tool), KNIME (data analytics tool), Microsoft Excel (data analytics tool), Microsoft Power BI (business intelligence data analytics and data visualization tool) MongoDB (database tool)

Qlik (data analytics and data integration tool), QlikView (data visualization tool), SAS (data analytics tool), Scikit Learn (machine learning tool), Tableau (data visualization tool), TensorFlow (machine learning tool)

Data science can be thought of as having a five-stage lifecycle:.

Capture - This stage is when data scientists gather raw and unstructured data. The capture stage typically includes data acquisition, data entry, signal reception and data extraction.

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

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

Analyze - This stage is when multiple types of analyses are performed on the data. The analysis stage involves data reporting, data visualization, business intelligence and decision making.

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

Data Science

Most Popular Techniques

Regression

A type of supervised learning, regression analysis in data science 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.

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’s used for applications such as email spam filters and sentiment analysis.

Clustering

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 characteristics. Clustering is done to reveal patterns within data — typically with large, unstructured data sets.

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 like finance and cybersecurity.

DATA SCIENCE

APPLICATIONS, BENEFITS & RISKS

Data science has led to a number of breakthroughs in the healthcare industry. With a vast network of data now available via everything from EMRs to clinical databases to personal fitness trackers, medical professionals are finding new ways to understand disease, practice preventive medicine, diagnose diseases faster and explore new treatment options. The sensitivity of patient data makes data security an even bigger point of emphasis in the healthcare space..

Data science is showing up on the road too. Tesla, Ford and Volkswagen have implemented predictive analytics in their autonomous vehicles. These cars use thousands of tiny cameras and sensors to relay information in real-time. Using machine learning, predictive analytics and data science, self-driving cars can adjust to speed limits, avoid dangerous lane changes and even take passengers on the quickest route.

UPS turns to data science to maximize efficiency, both internally and along its delivery routes. The company’s On-road Integrated Optimization and Navigation (ORION) tool uses data science-backed statistical modeling and algorithms that create optimal routes for delivery drivers based on weather, traffic and construction. It’s estimated that data science is saving the logistics company millions of gallons of fuel and delivery miles each year..

Data science is useful in every industry, but it may be the most important in cybersecurity. For example, international cybersecurity firm Kaspersky uses science and machine learning to detect hundreds of thousands of new samples of malware on a daily basis. Being able to instantaneously detect and learn new methods of cybercrime through data science is essential to our safety and security in the future.

Do you ever wonder how Spotify seems to recommend that perfect song you’re in the mood for? Or how Netflix knows just what shows you’ll love to binge? Using data science, these media streaming giants learn your preferences to carefully curate content from their vast libraries they think would accurately appeal to your interests.

Many businesses rely on data scientists to build time series forecasting models that help with inventory management and supply chain optimization. Data scientists are also sometimes tasked with making proactive recommendations based on budget forecasts made through financial models. Some even use data mining to segment customers by behavior, tailoring future marketing messages to appeal to certain groups based on previous brand interactions.

Machine learning and data science have saved the financial industry millions of dollars, and unquantifiable amounts of time. For example, JP Morgan’s contract intelligence platform uses natural language processing to process and extract vital data from thousands of commercial credit agreements a year. Thanks to data science, what would take around hundreds of thousands manual labor hours to complete is now finished in a few hours. Additionally, fintech companies like Stripe and Paypal invest in data science to create machine learning tools that quickly detect and prevent fraudulent activities.

Helps with making predictions and business decisions, Assists in data analysis, even for complex datasets, Enhances cybersecurity protection, Allows for quick business reporting and visualizations, Optimizes scheduling and recommending services.

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