Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.
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Why is data science important?
Data science is important because it combines tools, methods, and technology to generate meaning from data. Nowadays, companies have a lot of information coming in from various sources because many devices can gather and save data automatically. Online systems and payment portals capture more data in the fields of e-commerce, medicine, finance, and every other aspect of human life. We have text, audio, video, and image data available in vast quantities.
What is data science used for?
Data science is used to study data in four main ways:
1. Descriptive analysis
Descriptive analysis examines data to gain insights into what happened or what is happening in the data environment. It is characterized by data visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives. For example, a flight booking service may record data like the number of tickets booked each day. Descriptive analysis will reveal booking spikes, booking slumps, and high-performing months for this service.
2. Diagnostic analysis
Diagnostic analysis is a detailed data examination to understand why something happened. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations. Multiple data operations and transformations may be performed on a given data set to discover unique patterns in each of these techniques. For example, the flight service might drill down on a particularly high-performing month to better understand the booking spike. This may lead to the discovery that many customers visit a particular city to attend a monthly sporting event.
3. Predictive analysis
Predictive analysis uses historical data to make accurate forecasts about data patterns that may occur in the future. It is characterized by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. In each of these techniques, computers are trained to reverse engineer causality connections in the data.For example, the flight service team might use data science to predict flight booking patterns for the coming year at the start of each year. The computer program or algorithm may look at past data and predict booking spikes for certain destinations.
4. Prescriptive analysis
Prescriptive analytics takes predictive data to the next level. It not only predicts what is likely to happen but also suggests an optimum response to that outcome. It can analyze the potential implications of different choices and recommend the best course of action. It uses graph analysis, simulation, complex event processing, neural networks, and recommendation engines from machine learning.
Back to the flight booking example, prescriptive analysis could look at historical marketing campaigns to maximize the advantage of the upcoming booking spike. A data scientist could project booking outcomes for different levels of marketing spend on various marketing channels. These data forecasts would give the flight booking company greater confidence in their marketing decisions.
What are the data science techniques?
Data science professionals use computing systems to follow the data science process. The top techniques used by data scientists are:
Classification
Classification is the sorting of data into specific groups or categories. Computers are trained to identify and sort data. Known data sets are used to build decision algorithms in a computer that quickly processes and categorizes the data. For example:·
- Sort products as popular or not popular·
- Sort insurance applications as high risk or low risk·
- Sort social media comments into positive, negative, or neutral.
Data science professionals use computing systems to follow the data science process.
Regression
Regression is the method of finding a relationship between two seemingly unrelated data points. The connection is usually modeled around a mathematical formula and represented as a graph or curves. When the value of one data point is known, regression is used to predict the other data point. For example:·
- The rate of spread of air-borne diseases.·
- The relationship between customer satisfaction and the number of employees.·
- The relationship between the number of fire stations and the number of injuries due to fire in a particular location.
Clustering
Clustering is the method of grouping closely related data together to look for patterns and anomalies. Clustering is different from sorting because the data cannot be accurately classified into fixed categories. Hence the data is grouped into most likely relationships. New patterns and relationships can be discovered with clustering. For example: ·
- Group customers with similar purchase behavior for improved customer service.·
- Group network traffic to identify daily usage patterns and identify a network attack faster.
- Cluster articles into multiple different news categories and use this information to find fake news content.
The basic principle behind data science techniques
While the details vary, the underlying principles behind these techniques are:
- Teach a machine how to sort data based on a known data set. For example, sample keywords are given to the computer with their sort value. “Happy” is positive, while “Hate” is negative.
- Give unknown data to the machine and allow the device to sort the dataset independently.
- Allow for result inaccuracies and handle the probability factor of the result.
What are different data science technologies?
Data science practitioners work with complex technologies such as:
- Artificial intelligence: Machine learning models and related software are used for predictive and prescriptive analysis.
- Cloud computing: Cloud technologies have given data scientists the flexibility and processing power required for advanced data analytics.
- Internet of things: IoT refers to various devices that can automatically connect to the internet. These devices collect data for data science initiatives. They generate massive data which can be used for data mining and data extraction.
- Quantum computing: Quantum computers can perform complex calculations at high speed. Skilled data scientists use them for building complex quantitative algorithms.
Data science tools
Data scientists rely on popular programming languages to conduct exploratory data analysis and statistical regression. These open source tools support pre-built statistical modeling, machine learning, and graphics capabilities.
- R Studio: An open-source programming language and environment for developing statistical computing and graphics.
- Python: It is a dynamic and flexible programming language. Python includes numerous libraries, such as NumPy, Pandas, and Matplotlib, for analyzing data quickly.
- SAS: A comprehensive tool suite, including visualizations and interactive dashboards, for analyzing, reporting, data mining, and predictive modeling.
- IBM SPSS: Offers advanced statistical analysis, a large library of machine learning algorithms, text analysis, open source extensibility, integration with big data, and seamless deployment into applications.