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.
Why is data science important?
Data science is important because it combines tools, methods, and technology to generate meaning from data. Modern organizations are inundated with data; there is a proliferation of devices that can automatically collect and store information. 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.
History of data science
While the term data science is not new, the meanings and connotations have changed over time. The word first appeared in the ’60s as an alternative name for statistics. In the late ’90s, computer science professionals formalized the term. A proposed definition for data science saw it as a separate field with three aspects: data design, collection, and analysis. It still took another decade for the term to be used outside of academia.
Future of data science
Artificial intelligence and machine learning innovations have made data processing faster and more efficient. Industry demand has created an ecosystem of courses, degrees, and job positions within the field of data science. Because of the cross-functional skillset and expertise required, data science shows strong projected growth over the coming decades.
What is data science used for?
Data science is used to study data in four main ways.
Descriptive Analysis
Descriptive analysis examines data to gain insight into what has happened or is happening in the data environment. It features data visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives. For example, an airline reservation service may log data such as the number of tickets booked each day. Descriptive analysis reveals booking spikes, booking troughs, and high performance months for this service.
Diagnostic Analysis
Diagnostic analysis is the deep or detailed examination of data to understand why something happened. It features techniques such as drill down, data discovery, data mining and correlation. By performing multiple data operations and transformations on a given data set, you can discover unique patterns in each of these methods. For example, an airline service might drill down into particularly good months to better understand booking spikes.
Because of this, you may find that many of your customers travel to a particular city to attend a monthly sporting event.
Predictive Analytics
Predictive analytics uses historical data to accurately predict data patterns that may occur in the future. It features technologies such as machine learning, prediction, pattern matching, and predictive modeling. In each of these methods, computers are trained to reconstruct causal relationships in data.
For example, an airline service team can use data science to predict airline booking patterns for the next year at the beginning of each year. A computer program or algorithm can analyze historical data and predict a surge in bookings for certain destinations in May. Anticipating customers’ future travel needs, the company could start targeting ads to these cities in February.
Prescriptive Analytics
Prescriptive analytics take predictive data to a new level.
It not only predicts what might happen, but also suggests the best response to that outcome. He can analyze the potential consequences of different choices and recommend the best course of action. He uses graph analytics, modeling, complex event processing, neural networks, and machine learning-based recommendation engines.
Let’s go back to our airline ticket booking example. Prescriptive analytics can take into account past marketing campaigns to maximize the benefits of upcoming booking spikes. A data scientist can predict booking outcomes for different levels of marketing spend across different marketing channels.
These data predictions give airline booking companies more confidence in their marketing decisions.
What is the data science process?
A business problem typically initiates the data science process. A data scientist will work with business stakeholders to understand what business needs. Once the problem has been defined, the data scientist may solve it using the OSEMN data science process:
O – Obtain data
Data can be pre-existing, newly acquired, or a data repository downloadable from the internet. Data scientists can extract data from internal or external databases, company CRM software, web server logs, social media or purchase it from trusted third-party sources.
S – Scrub data
Data scrubbing, or data cleaning, is the process of standardizing the data according to a predetermined format. It includes handling missing data, fixing data errors, and removing any data outliers. Some examples of data scrubbing are:·
- Changing all date values to a common standard format.·
- Fixing spelling mistakes or additional spaces.·
- Fixing mathematical inaccuracies or removing commas from large numbers.
E – Explore data
Data exploration is preliminary data analysis that is used for planning further data modeling strategies. Data scientists gain an initial understanding of the data using descriptive statistics and data visualization tools. Then they explore the data to identify interesting patterns that can be studied or actioned.
M – Model data
Software and machine learning algorithms are used to gain deeper insights, predict outcomes, and prescribe the best course of action. Machine learning techniques like association, classification, and clustering are applied to the training data set. The model might be tested against predetermined test data to assess result accuracy. The data model can be fine-tuned many times to improve result outcomes.
N – Interpret results
Data scientists work together with analysts and businesses to convert data insights into action. They make diagrams, graphs, and charts to represent trends and predictions. Data summarization helps stakeholders understand and implement results effectively.
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.
