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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

What Is 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.

Recommendation Generation

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

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

Data science professionals typically require an arsenal of data science tools and programming languages to use throughout their careers. These are some of the more popular options being used today:

Popular Data Science Tools

  • Popular Data Science Tools
  • 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 Lifecycle

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.

What Are Data Science Techniques?

There are lots of data science techniques with which data science professionals must be familiar in order to do their jobs. These are some of the 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 Skills

There’s no one-size-fits-all answer to the question What does a data scientist do? So the exact skills and toolboxes that data science professionals need vary from role to role.

While there are some skills and techniques that data scientists will need to learn if they wish to enter into more specialized fields within data science — such as deep learning, neural networks and natural language processing — there are some general proficiencies and a few key soft skills that will set up aspiring and early-career data science professionals for success:

Programming

Using languages like Python and R.

Database Management

Learning and applying SQL to communicate with databases.

Statistics

Having a handle on how to analyze data to solve problems.

Curiosity

Focused on figuring problems out and always learning new things.

Storytelling

The ability to tell stories with data and relay insights.

Communication

Comfortable collaborating with others and communicating problems and solutions clearly.

Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking.Data science is the study of data to extract meaningful insights for business. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.

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