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Glossary Index

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Glossary Index

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Glossary term

Data science

Quick definition

Data science is a multidisciplinary field that brings together mathematics, statistics, programming, computer science, and domain knowledge to extract insights from data and create efficiencies in business tasks.

Key takeaways

 

●      Data science is a vast field covering many topics, including math, computer science, programming, statistics, and business knowledge.

●      Data science involves many processes and applications of data analysis, including data mining, artificial intelligence, predictive analytics, deep learning, and machine learning algorithms.

●      The two main purposes of data science are to gain insights from current data and to create models using that data for testing and predictive analytics.

●      Any company with a digital presence can use data science to improve the customer experience at every stage of the customer journey.


Q: What is data science?

A: When industry professionals talk about data science, the word they often repeat is “multidisciplinary.” Data science is a vast field covering many topics, including math, computer science, programming, statistics, and business knowledge. All of these areas come together to analyze big data in order to gain business insights. Marketers and other business stakeholders can use these insights to perform business tasks better.

Data science involves many processes and applications of data analysis, including data mining, artificial intelligence, predictive analytics, deep learning, and machine learning algorithms.

Q: What’s the difference between data science and business analytics?

A: Business analytics is a subset of the broad field of data science. Business analysts perform tasks like querying, interpreting, summarizing, and presenting stories about data. They may even perform some forecasting. The goal of business analytics is to help businesses implement data-driven decision-making.

Unlike business analytics, data science also includes modeling. Data scientists create models to automate processes — like interacting with customers — to make predictions and perform testing.

Q: What is data science used for?

A: The two main purposes of data science are to gain insights from current data and to create models using that data for testing and predictive analytics.

Other examples of uses for data science include:

●      Data summarization and visualization – representing data graphically using a chart, diagram, or image

●      Causal inference – the process of determining whether or not causal connections exist based on data evidence

●      Pattern recognition – the automated recognition of patterns in data

●      Recommendation enginesusing available data on past behavior to provide content recommendations

●      Natural language processing – using software to automate manipulation of speech and text

●     Business intelligence – using data to gain actionable insights for better business decisions

Q: What does a data scientist do?

A: Data scientists collect, analyze, and interpret data. Usually, before analyzing data, data scientists begin by understanding a problem that needs to be addressed. In business, that problem is usually a process that marketers would like to automate, predict, or understand better. Problems fall into one of two areas: modeling problems and causal inference and experimentation problems.

Modeling problems. To address a modeling problem, data scientists first have to identify the right data, the class of statistical analysis or machine learning techniques they will use, and the metrics they will focus on for optimization. Then, they retrieve the data, clean it up, and summarize it. With this information in hand, data scientists are ready to build models and use them to run experiments. Throughout the experimentation process, data scientists analyze the behavior of the chosen metrics. Finally, they deploy models to address the problem or make predictions about future data. Over time, data scientists will monitor model performance, retrain the models, and test new versions.

Causal inference and experimentation problems. To address a causal inference and experimentation problem, data scientists follow the scientific method. They create hypotheses and design experiments with the data to test their predictions. After running experiments, data scientists analyze the results and form new hypotheses.

Q: Are there different types of data science?

A: Since data science is such a broad field, there are many different areas of focus. Some of the most common focus areas are:

●      Image recognition

●      Natural language processing

●      Predictive modeling

●      Machine learning engineering

●      Causal inference

●      Business Analytics

Q: What are common challenges in data science?

A: The biggest challenges facing data scientists are finding the right data source, getting access to that data, and making sure raw data and unstructured data are transformed into structured data that they can use to do their jobs.

Another major challenge for data scientists is producing a functional data model that is also intuitive for stakeholders. Once they have access to the right data, data scientists can build a model fairly easily. But getting that model into production so that stakeholders can actually use it can be difficult. In fact, there is an entire field dedicated to this practice called “MLOps,” which addresses how teams can operationalize models more effectively with the help of machine learning (ML).

Q: What are the requirements to bring data science into your business?

A: Any company with a digital presence can use data science. All it takes is a set of business-motivated problems, a form of data collection, and access to that data.

Q: What are the benefits of using data science in your business?

A: Data science benefits every stage of the customer journey — awareness, acquisition, purchase, service, and loyalty. Data science especially helps marketers target the right user segments and understand how to market to them best. Once a potential customer is interested, data science can help marketers guide them through the sales funnel by identifying and optimizing the right content to entice them to buy. Data science can even help marketers with customer retention by predicting ways to increase a customer’s lifetime value.

Q: How will data science continue to evolve?

A: The foundations of data science have always been fairly ad hoc. There is an infinite number of tools data scientists can use. In the future, the industry will see a consolidation of those tools, which will reduce the number of choices data scientists have to make — and speed up and standardize processes across the industry.

The data science industry will also see significant growth in the MLOps field, which focuses on deploying and monitoring machine learning models throughout their life cycle. And in the coming years, the industry will dive deeper into the conversation on ethics in data science.

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