what is predictive analytics? course hero

Predictive Analytics is an area of statistics and data analysis that uses data modeling to determine future results of a decision path. By identifying trends and patterns in past and present data and understanding data relationships, data analysts can build models to forecast the effects of different strategies and decisions.

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What is a predictive analytics model?

Predictive analytics uses mathematical modeling tools to generate predictions about an unknown fact, characteristic, or event. “It’s about taking the data that you know exists and building a mathematical model from that data to help you make predictions about somebody [or something] not yet in that data set,” Goulding explains.

What can you do with a degree in predictive analytics?

Predictive analytics is applicable to data analysis, statistics, science, business management and more and the ability to mine data and develop accurate forecasting models is essential for jobs in these and other disciplines.

Why is predictive analytics on top of your it priority list?

That’s why predictive analytics has shot to the top of priority lists for organizations around the world. Predictive analytics is a branch of advanced analytics that makes predictions about future events, behaviors, and outcomes.

Is prescriptive analytics the next frontier of predictive analytics?

Organizations that have successfully implemented predictive analytics see prescriptive analytics as the next frontier. Predictive analytics creates an estimate of what will happen next; prescriptive analytics tells you how to react in the best way possible given the prediction.

What is the meaning of predictive analytics?

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities.

What is predictive analytics explain with example?

Predictive analytics models may be able to identify correlations between sensor readings. For example, if the temperature reading on a machine correlates to the length of time it runs on high power, those two combined readings may put the machine at risk of downtime. Predict future state using sensor values.

What is predictive data analytics and why is it important?

Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers. Improving operations. Many companies use predictive models to forecast inventory and manage resources.

What are the 4 steps in predictive analytics?

But it's not just access to data that helps you make smarter decisions, it's the way you analyze it. That's why it's important to understand the four levels of analytics: descriptive, diagnostic, predictive and prescriptive.

Where is predictive analytics used?

Industry Applications. Predictive analytics is used in insurance, banking, marketing, financial services, telecommunications, retail, travel, healthcare, pharmaceuticals, oil and gas and other industries.

What is predictive analytics in AI?

Predictive Analytics is the use of mathematical and statistical methods, including artificial intelligence and machine learning, to predict the value or status of something of interest.

What is the difference between data analytics and predictive analytics?

Data analytics is 'general' form of Analytics used in businesses to make decisions which are data driven. Predictive analytics is 'specialized' form of Analytics used by businesses to predict future based outcomes. Data Analytics consists of data collection and data analysis in general and could have one or more usage.

Why is predictive analytics important in decision making?

Predictive analytics employs historical and real-time data mining and other techniques to predict outcomes and inform decision-making for businesses and other organizations.

How do you do predictive analytics?

Predictive analytics requires a data-driven culture: 5 steps to startDefine the business result you want to achieve. ... Collect relevant data from all available sources. ... Improve the quality of data using data cleaning techniques. ... Choose predictive analytics solutions or build your own models to test the data.More items...•

What are predictive analytics models?

In short, predictive modeling is a statistical technique using machine learning and data mining to predict and forecast likely future outcomes with the aid of historical and existing data. It works by analyzing current and historical data and projecting what it learns on a model generated to forecast likely outcomes.

What is the best tool for predictive analytics?

In alphabetical order, here are six of the most popular predictive analytics tools to consider.H2O Driverless AI. A relative newcomer to predictive analytics, H2O gained traction with a popular open source offering. ... IBM Watson Studio. ... Microsoft Azure Machine Learning. ... RapidMiner Studio. ... SAP Predictive Analytics. ... SAS.

What are the four different types of analytical methods?

There are four types of analytics, Descriptive, Diagnostic, Predictive, and Prescriptive.

Which is an example of prescriptive analytics?

On social media, TikTok's “For You” feed is one example of prescriptive analytics in action. The company's website explains that a user's interactions on the app, much like lead scoring in sales, are weighted based on indication of interest.

What are examples of predictive analytics in healthcare?

Main Techniques in Predictive AnalyticsData Mining. ... Data Modeling. ... AI. ... Detecting the Early Signs of the Patients' Condition Deterioration. ... Biosensors for ICU Monitoring. ... Risk Scoring for Chronic Illnesses. ... Predictive Care for At-Risk Patients. ... Preventing Patient Suicide and Self-Harm.More items...•

Which is one of the best example prescriptive analytics?

Examples of prescriptive analyticsMarketing and sales. Marketing and sales agencies have access to large amounts of customer data that can help them to determine optimal marketing strategies, such as what types of products pair well together and how to price products. ... Transportation industry. ... Financial markets.

What is predictive analytics?

Predictive analytics uses mathematical modeling tools to generate predictions about an unknown fact, characteristic, or event. “It’s about taking the data that you know exists and building a mathematical model from that data to help you make predictions about somebody [or something] not yet in that data set,” Goulding explains.

Why Is Predictive Analytics Important?

While organizations have recognized the importance of gathering data as a means of looking back on industry trends for years, business teams have only just started scratching the surface of possibility when it comes to predictive analytics.

How to predict cholesterol with a BMI of 31?

In this scenario, when that new patient arrives knowing only that their BMI is 31, a data analyst will be able to predict the patient’s cholesterol by looking at that line and seeing what cholesterol level most closely aligns with other patients who have a BMI of 31.

Why use a linear regression model?

A linear regression model would be useful when a doctor wants to predict a new patient’s cholesterol based only on their body mass index (BMI). In this example, the analyst would know to put the data the doctor gathered from his 5,000 other patients—including each of their BMIs and cholesterol levels—into the linear regression model. They are hoping to predict an unknown based on a predetermined set of quantifiable data.

How does a data analyst use neural networks?

The analyst will pull purchase data and feed it to the neural network, giving the network real examples to learn from. This data will travel through the neural network through various mathematical functions until the output is produced and a product recommendation populates.

What is data analyst?

While data analysts are required to make decisions regarding which mathematical model to use in a given situation, they are not actually the ones crunching the data. Statisticians and programmers develop computer programs that carry out these processes, each of which operates using a different mathematical model.

What is optimal estimation?

Optimal estimation is a modeling technique that is used to make predictions based on observed factors . This model has been used in analytics for over 50 years and has laid the groundwork for many of the other predictive tools used today. According to Goulding, past applications of this method include determining “how to best recalibrate equipment on a manufacturing floor… [and] estimating where a bullet might go when shot,” as well as in other aspects of the defense industry.

What is predictive analytics?

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities.

Why do businesses use predictive analytics?

Businesses use predictive analytics to make inventory management more efficient, helping to meet demand while minimizing stock.

What is predictive modeling?

Predictive modeling can be used to predict a customer’s behavior, such as his or her credit risk. Descriptive modeling. Descriptive modeling describes relationships within a given dataset, and it is primarily used to classify customers or prospects into groups for segmentation purposes.

What are the different types of predictive analytics?

Popular predictive analytics models include classification, clustering, forecast, outliers, and time series , which are described in more detail below. Classification models are categorized under supervised machine learning models. They place data into categories based on conclusions from the historical data.

How does a time series model work?

Time-series models employ a sequence of data points using time as the input parameter. It can take the last year of data, calculate a numerical metric, and use that metric to predict the three to six weeks of data. For example, the model can be used by a hospital to make predictions about emergency room capacity based on the number of patients who showed up in the past six weeks.

What is forecasting model?

Forecast models use metric value prediction, estimating numeric value for new data based on trends from historical data. For example, a call center can use the model to forecast how many calls it will receive per hour. Time series and econometric models would be examples of forecasting models.

What is an outlier model?

Outliers models deal with anomalous data entries in a dataset. For example, insurance companies can use it for fraud detection to flag anomalous data within a list of transactions. Some popular methods for outlier detection include extreme value analysis, probabilistic and statistical modeling, linear regression, proximity-based modeling, and information theory modeling.

What is predictive analytics?

Predictive analytics is the use of data to predict future trends and events. It uses historical data to forecast potential scenarios that can help drive strategic decisions.

Why is predictive analytics important?

Every business needs to keep periodic financial records, and predictive analytics can play a big role in forecasting your organization’s future health. Using historical data from previous financial statements, as well as data from the broader industry, you can project sales, revenue, and expenses to craft a picture of the future and make decisions.

Why is forecasting important?

Forecasting can enable you to make better decisions and formulate data-informed strategies. Here are several examples of predictive analytics in action to inspire you to use it at your organization.

How to predict a lead's likelihood of moving down the funnel from awareness to purchase?

Additionally, historical behavioral data can help you predict a lead’s likelihood of moving down the funnel from awareness to purchase. For instance, you could use a single linear regression model to determine that the number of content offerings a lead engages with predicts—with a statistically significant level of certainty—their likelihood of converting to a customer down the line. With this knowledge, you can plan targeted ads at various points in the customer’s lifecycle.

Why is consumer data important in marketing?

In marketing, consumer data is abundant and leveraged to create content, advertisements, and strategies to better reach potential customers where they are. By examining historical behavioral data and using it to predict what will happen in the future, you engage in predictive analytics.

What is data analytics?

Data analytics —the practice of examining data to answer questions, identify trends, and extract insights—can provide you with the information necessary to strategize and make impactful business decisions .

When is the algorithm triggered?

When the criteria for an upcoming malfunction are met, the algorithm is triggered to alert an employee who can stop the machine and potentially save the company thousands, if not millions, of dollars in damaged product and repair costs. This analysis predicts malfunction scenarios in the moment rather than months or years in advance.

What is predictive analytics?

Predictive analytics is the process of using data analytics to make predictions based on data. This process uses data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events. The term “predictive analytics” describes the application of a statistical or machine learning technique ...

Why do companies use predictive analytics?

Companies also use predictive analytics to create more accurate forecasts, such as forecasting the demand for electricity on the electrical grid. These forecasts enable resource planning (for example, scheduling of various power plants), to be done more effectively.

Why is historical data used in predictive analytics?

Typically, historical data is used to build a mathematical model that captures important trends. That predictive model is then used on current data to predict what will happen next, or to suggest actions to take for optimal outcomes. Predictive analytics has received a lot of attention in recent years due to advances in supporting technology, ...

What is predictive modeling?

Predictive modeling uses mathematical and computational methods to predict an event or outcome. These models forecast an outcome at some future state or time based upon changes to the model inputs.

How does predictive modeling help companies?

Data-driven predictive models can help companies solve long-standing problems in new ways. Equipment manufacturers, for example, can find it hard to innovate in hardware alone. Product developers can add predictive capabilities to existing solutions to increase value to the customer.

What are some examples of time series regression models?

Examples include time-series regression models for predicting airline traffic volume or predicting fuel efficiency based on a linear regression model of engine speed versus load, and remaining useful life estimation models for prognostics.

What Are the Benefits of Predictive Analytics?

For starters, predictive tools enable company leaders to move away from gut instinct and assumptions. Relatedly, they cut down on time-intensive decision-making processes and eliminate guesswork.

How does predictive analytics help companies?

Not surprisingly, having a predictive analytics strategy can also help companies achieve better marketplace performance. One study showed that 63% of business representatives cited their data analytics programs as creating a competitive advantage.

What Are Some Examples of Predictive Analytics Platforms?

For example, Board offers automated predictive modeling features to speed utilization. SAP Analytics Cloud has tailored fast-start solutions that allow people to start analyzing data in days rather than weeks.

Why do marketers use predictive analytics?

Marketing teams often depend on predictive analytics to assess how a campaign or new product launch will perform. One company claims 85% accuracy with technology predicting which actors will generate the most profits if placed in lead roles.

What is predictive product?

Predictive products give details about future events, helping people plan for them. In contrast, prescriptive tools offer solutions based on what the data shows. Users often depend on them to solve known problems.

Why do retailers use predictive tools?

Retailers rely on predictive tools to understand likely sales trends, as well as preventing product depletion or overstock issues. They know factors such as weather and preferences can make sales fluctuate, and want details on the elements that drive or decrease consumer demand.

Why is high quality data important?

No matter when or how a company’s decision-makers want to start using algorithms to predict the future, high quality data is essential for success . If a company feeds incorrect or duplicate information into a powerful tool, the results may not have the accuracy and dependability a business leader needs to feel confident.

Things You Should Know

Each predictive analytics course in this article will have different prerequisites. I will specify them in detail in each course section below. However, all require you to have basic knowledge in the following:

1. Predictive Analytics for Business

This Nanodegree program from Udacity aims toward students who want to apply predictive analytics to solve real-world business problems without delving too deeply into the technicals.

2. Predictive Analytics using Python

The second course will be far more technical than the first. This program by the University of Edinburgh will guide you through the entire process of building fully-functional predictive analytics models using Python.

4. Introduction to Predictive Modeling

This Coursera course from the University of Minnesota will lead you through concepts, processes, techniques, and real-world predictive modeling applications. If you are looking for a tutorial to test the waters, this course will serve that purpose.

5. Predictive Modeling and Machine Learning with MATLAB

Designed for MATLAB users, this course by MathWorks will guide students on how to use MATLAB to build, train, and optimize predictive models to analyze data.

Coursera Plus

Since I have recommended several Coursera courses in this article, you may be interested in more than one course. If that is the case, I recommend subscribing to Coursera Plus instead.

Other Alternatives

Below are other predictive analytics courses that you may want to consider. However, I did not include them in the list for specific reasons explained below.

What is Predictive Analytics?

Predictive Analytics is an area of statistics and data analysis that uses data modeling to determine future results of a decision path. By identifying trends and patterns in past and present data and understanding data relationships, data analysts can build models to forecast the effects of different strategies and decisions.

Online Courses in Predictive Analytics

Predictive analytics is applicable to data analysis, statistics, science, business management and more and the ability to mine data and develop accurate forecasting models is essential for jobs in these and other disciplines.

Jobs in Predictive Analytics

Applied predictive modeling is a key part of many data science and data analysis job roles. At the time of this writing, Indeed.com listed over 2,000 job openings that included predictive analytics in their requirements.

Explore a Career in Predictive Analytics

Enroll in a data science MicroMasters program and get in-depth training in data mining, data modeling and predictive analytics. Adding these key skills to your CV will get you on a path to an exciting career in big data, machine learning, data analytics, BI or a related field.