Predictive Analytics: intermediate Predictive Analytics (2019)
Expertise:
Technology:
- 18 Courses | 11h 54m 22s
- 6 Books | 32h 4m
- 2 Courses | 1h 21m 41s
- 5 Books | 31h 22m
- 13 Courses | 17h 55m 31s
- 1 Course | 1h 25m 57s
- 7 Books | 48h 58m
- 3 Courses | 4h 19m 49s
- 1 Book | 4h 8m
Discover how to identify and analyze data and use it to predict trends and patterns with Predictive Analytics
GETTING STARTED
Predictive Modeling: Predictive Analytics & Exploratory Data Analysis
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1m 30s
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8m 23s
GETTING STARTED
Predictive Analytics: Case Studies for AI in Agriculture
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1m 51s
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9m 55s
GETTING STARTED
Predictive Analytics: Case Studies on Predictive Analytics for Healthcare
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1m 58s
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11m 42s
COURSES INCLUDED
Process & Applications
Predictive analytics uses techniques, such as statistics and machine learning, to build predictive models, often using big data to test and validate these models. Explore key features of predictive analytics and big data.
8 videos |
32m
Assessment
Badge
Key Statistical Concepts
For any organization, predictive analytics is quickly becoming a key component for organizational success. Discover the application of predictive analytics to various industries, and the process and roles involved.
12 videos |
51m
Assessment
Badge
Data Considerations in Analytics
Predictive analytics involves a wide range of statistical tools and methods that allow an analyst to build a powerful predictive model. Explore the importance of statistics and probability theory in predictive analytics.
12 videos |
55m
Assessment
Badge
Correlation & Regression
Predictive analytics involves widely accepted tools and techniques that enable organizations to make informed decisions regarding potential future events. Examine how correlation and regression are employed in predictive analytics.
8 videos |
29m
Assessment
Badge
Data Collection & Exploration
Most data that organizations collect doesn't offer much value. However, by applying the right techniques, you can extract powerful insights from the stockpile of data. Discover data collection and exploration for best possible prediction.
10 videos |
39m
Assessment
Badge
Data Mining, Data Distributions, & Hypothesis Testing
Purposeful information can be extracted from large data sets to determine what has, could, or should happen. Explore descriptive, predictive, and prescriptive analytics, including data mining, distribution models, and hypothesis testing.
10 videos |
37m
Assessment
Badge
Data Preprocessing
Predictive analytics delivers the greatest value when the data being modeled is relevant to the business goals. Examine the preprocessing phase of data collection to provide the best predictive model.
7 videos |
25m
Assessment
Badge
Data Reduction & Exploratory Data Analysis (EDA)
With predictive analytics, relevant data should be stored for easy retrieval and kept up-to-date, with attributes selected contingent on their predictive potential. Explore data reduction and graphic tools for exploratory data analysis.
11 videos |
40m
Assessment
Badge
K-Nearest Neighbor (k-NN) & Artificial Neural Networks
Choosing the appropriate technique to deliver confident predictions can be challenging for analysts. Examine algorithms used for predictive analytics, including the K-Nearest Neighbor (k-NN) algorithm and artificial neural network modeling.
9 videos |
38m
Assessment
Badge
A/B Testing, Bayesian Networks, & Support Vector Machine
At the core of predictive analytics lie the models used to make predictions after the data has been collected and preprocessed. Explore predictive techniques, including A/B testing, Bayesian Networks, and the support vector machine (SVM).
11 videos |
41m
Assessment
Badge
Clustering Techniques
The key to meaningful analysis is the ability to choose the right methods that provide the greatest predictive power. Discover how data clustering, such as K-Means, hierarchical, and DBSCAN, is used to combine similar subsets of data.
10 videos |
35m
Assessment
Badge
Linear & Logistic Regression
Regression modeling investigates relationships between dependent and independent variables and is heavily relied upon for predictive analytics and data mining applications. Explore both the linear and logistic regression models.
10 videos |
41m
Assessment
Badge
Text Mining & Social Network Analysis
Text mining facilitates social network analysis, giving analysts the ability to capture people's sentiments about various topics. Examine how text mining and social network analysis can greatly impact many diverse areas.
11 videos |
48m
Assessment
Badge
Time Series Modeling
Time series modeling is a common forecasting method, such as making stock market predictions. It has made its way into many varied applications, including inventory management and healthcare. Explore the features of time series modeling.
8 videos |
33m
Assessment
Badge
Machine Learning, Propensity Score, & Segmentation Modeling
Both supervised and unsupervised machine learning techniques are at the forefront of the predictive analytics and data mining industry. Discover machine learning features and tools, propensity scoring, and segmentation modeling.
12 videos |
46m
Assessment
Badge
Random Forests & Uplift Models
Nestled within machine learning are ensemble techniques, enabling the combination of multiple models to reduce prediction error and improve forecasting ability. Examine machine learning methods, including random forests and uplift models.
8 videos |
33m
Assessment
Badge
Model Life Cycle Management
Analysts must continuously manage analytical models, such as monitoring performance over time and interacting with various stakeholders. Explore the operational decision-making stages of model life cycle management.
7 videos |
31m
Assessment
Badge
Model Development, Validation, & Evaluation
Analytic model management ensures that models are not only superior to alternatives, but they also meet or exceed current business needs. Examine the process of building, validating, and evaluating a predictive analytics model.
13 videos |
53m
Assessment
Badge
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COURSES INCLUDED
Predictive Modeling: Predictive Analytics & Exploratory Data Analysis
Explore the machine learning predictive analytics, exploratory data analytics, and different types of data sets and variables in this 9-video course. Discover how to implement predictive models and manage missing values and outliers by using Python frameworks. Key concepts covered in this course include predictive analytics, a branch of advanced analytics, and its process flow, and learning how analytical base tables can be used to build and score analytical models. Next, you will discover business problems that can be resolved by using predictive modeling; how to build predictive models with the Python framework; and learn the essential features of exploratory data analysis. Then learn about data sets, collections of data corresponding to the content of a single database or a single statistical data matrix, and then learn the variables of the different types of data sets including univariate, bivariate, and multivariate data and analytical approaches that can be implemented with them. Finally, you will learn about methods that can be used to manage missing values and outliers in data sets.
9 videos |
40m
Assessment
Badge
Predictive Modeling: Implementing Predictive Models Using Visualizations
Explore how to work with machine learning feature selection, general classes of feature selection algorithms, and predictive modeling best practices. In this 12-video course, learners discover how to implement predictive models with scatter plots, boxplots, and crosstabs by using Python. Key concepts examined here include the benefits of feature selection and the general classes of feature selection algorithms; the different types of predictive models that can be implemented and associated features; and how to implement scatterplots and the capability of scatterplots in facilitating predictions. Next, you will learn about Pearson's correlation measures and the possible ranges for Pearson's correlation; learn to recognize the anatomy of a boxplot, a visual representation of the statistical five-number summary of a given data set; and observe how to create and interpret boxplots with Python. Then see how to implement crosstabs to visualize categorical variables; learn statistical concepts that are used for predictive modeling; and learn tree-based methods used to implement regression and classification. Finally, you will learn best practices for implementing predictive modeling.
12 videos |
41m
Assessment
Badge
COURSES INCLUDED
Predictive Analytics: Case Studies for AI in Agriculture
Population growth, climate change, and volatile commodity prices risk factors are putting a strain on the agricultural system nowadays. Using artificial intelligence (AI) in agriculture can potentially help mitigate this strain in areas such as yield prediction and disease detection in agriculture. In this course, explore a study that uses machine learning (ML) models for agricultural use cases. Next, explore a specific case study that attempts to predict the yield of maize and soybean crops on various American farms. Finally, examine a study that uses machine learning for pest detection. Upon completion, you'll be able to gather and analyze academic papers on machine learning in agriculture, identify problem categories and solution constructs, and recall common recurring themes in research.
10 videos |
58m
Assessment
Badge
Predictive Analytics: Performing Classification Using Machine Learning
Species identification is an important step in many agricultural processes. With supply chain globalization and strict grading norms imposed by importing nations on the composition of consignments, machine learning (ML) techniques geared towards classification are becoming increasingly relevant in agriculture. In this course, learn how to perform exploratory data analysis on data about beans. Next, discover how to visualize the data to get a sense of the relationships between the attributes, perform logistic regression to classify the beans, and explore the result metrics of the model. Finally, practice performing feature selection and using other types of machine learning models. Upon completion, you'll be able to analyze agricultural data, recognize relationships between attributes, and classify data based on type.
13 videos |
1h 21m
Assessment
Badge
Predictive Analytics: Applying Clustering to Soil Features & Conditions
The question of what crops ought to be planted per growing season for a given patch of land is extremely important. An important related question is which type of crop fits most easily with the soil and climatic conditions. Machine learning (ML) models like clustering can help answer this question using data from other farms. In this course, work with soil data consisting of field climate conditions. Next, learn how to use charts to view univariate information and the relationships between attributes. Finally, discover how to perform k-means and agglomerative clustering on data. Upon completion, you'll be able to apply clustering to data, identify links between clusters identified by ML algorithms and the crops cultivated in them, and differentiate k-means and agglomerative clustering.
12 videos |
1h 24m
Assessment
Badge
Predictive Analytics: Performing Prediction Using Regression
In agriculture, accurately assessing crop yield in advance can help farmers effectively plan ahead, allocate labor and capital, and plan for crop transportation logistics. Machine learning (ML) can be used to account for the many factors that drive yields. In this course, work with data consisting of blueberry plant information and climate factors to predict yield. Next, learn how to visualize univariate relationships and bivariate correlations and perform linear regression. Finally, practice performing feature selection for the regression model and view the score of importance and model on a subset for different data attributes. Upon completion, you'll be able to use regression techniques to predict agricultural yields, identify real-world and statistical relationships in the data, and differentiate between various regression models.
8 videos |
54m
Assessment
Badge
Predictive Analytics: Case Studies for Operations
Nowadays, the domains of operations management and supply chain management are growing ever more complex. Operations management is the management of labor, resources, and processes for goods manufacturing for customers. Supply chain management encompasses the timely procurement of materials for factories and the safe and timely transportation of finished products to the end customer. In this course, explore a case study about leveraging machine learning models for identifying reliable suppliers. Next, examine a study that attempts to identify failures of induction motors using machine learning models. Finally, discover how analytics can be used to predict order lead time and demand in an aluminum firm. Upon completion, you'll be able to outline use cases for the application of AI to operations and supply chain management.
11 videos |
1h 36m
Assessment
Badge
Predictive Analytics: Identifying Machine Failures
Machines are the building blocks of manufacturing facilities, and facilities have grown larger and more complex over time, resulting in the increased effects of a single machine failure. Artificial intelligence (AI) and machine learning (ML) models are commonly used to quickly get ahead of and diagnose machine failures. In this course, learn how to create an Azure Machine Learning workspace for building models that predict machine failures. Next, practice performing preprocessing tasks on machine failure prediction data. Finally, discover how to perform machine failure prediction using logistic regression. Upon completion, you'll be able to detect machine failures using machine learning methods.
11 videos |
1h 10m
Assessment
Badge
Predictive Analytics: Using SMOTE, Model Explanations, & Hyperparameter Tuning
Machine learning (ML) models can struggle with training themselves to identify failures if the dataset's number of machine failures is too low. This is a common problem that occurs when predicting very rare occurrences. Thankfully, oversampling techniques exist to mitigate such issues. In this course, learn how to use SMOTE, a widely used technique to make datasets more balanced. Next, explore model explanations, a feature of Azure Machine Learning. Finally, practice performing hyperparameter tuning by trying different model configurations to see which yields the best performance. Upon completion, you'll be able to improve the performance of a failure detection model, generate records of minority classes, and perform hyperparameter tuning.
11 videos |
1h 15m
Assessment
Badge
Predictive Analytics: Case Studies for Cybersecurity
Cybersecurity is the protection of user software from maliciously-intentioned agents and parties. Cyberattacks commonly focus on critical physical infrastructures like power plants, oil refineries, and gas pipelines. For geopolitical reasons, the cybersecurity of such installations is increasingly important. In this course, explore the use of classification models when modeling cyberattacks and the evaluation metrics for classification models. Next, examine a case study where machine learning and cybersecurity attempt to detect intrusions in a gas pipeline. Finally, investigate a case study where machine learning models are used to detect and cope with malware. Upon completion, you'll be able to identify the need for AI in cybersecurity and outline the appropriate use of evaluation metrics for classification models.
10 videos |
1h 23m
Assessment
Badge
Predictive Analytics: Identifying Network Attacks
In cybersecurity, it's important to determine whether a user interaction or action represents an attack, followed by discerning the specific attack type and signature. Machine learning (ML) models and managed ML solutions like Microsoft Azure Machine Learning can help with this. In this course, learn how to create an Azure Machine Learning workspace, read in data, and categorize all of the different types of attacks. Next, discover how to train a random forest classification model using the scikit-learn library and test it on the in-sample validation data. Finally, practice performing multiclass classification to identify the specific type of attack. Upon completion, you'll be able to detect intrusions using data, train and evaluate classification models, and perform multiclass classification.
18 videos |
2h 3m
Assessment
Badge
Predictive Analytics: Case Studies for Marketing & Retail
Artificial intelligence (AI) can play a part in many marketing tasks, and if used skillfully, can have a significant impact on revenues and profits. Every action on social media is recorded and AI is well-suited to process this large volume of data. AI can also be used to help with demand forecasting, product recommendations, and fraud detection. In this course, you will investigate significant ways to apply AI in marketing and in retail. Explore the Weibo case study, which focuses on data from marketing and advertising posts on social media and the attempts to predict likes, comments, and shares of each post before it is sent out. Then, analyze the applications of market basket analysis (MBA) via case study to determine how to perform customer segmentation using clustering and MBA through association rules mining. Finally, discover how to predict out-of-stock events in a store.
9 videos |
55m
Assessment
Badge
Predictive Analytics: Predicting Sales & Customer Lifetime Value
In recent years, retailing has changed from a fragmented space into a winner-takes-all sector, in which a key differentiating factor is the ability to tightly predict demand and measure customer lifetime value. Begin this course by attempting to predict the sales for each week in a Walmart store. You will explore and visualize your data, creating an Azure machine learning workspace and a hosted Python notebook to write code. Then, perform regression analysis to predict the sales after one-hot encoding the requisite explanatory variables. You will apply different models as well, including ridge regression, K-nearest neighbors, decision trees, random forests, and extra tree regressors. Next, predict the customer lifetime value using regression analysis, and perform cross-validation and feature selection on the model in order to improve its performance. Finally, experiment with feature selection, including recursive feature elimination, lasso regularization, and linear SVR.
14 videos |
1h 41m
Assessment
Badge
Predictive Analytics: Customer Segmentation & Market Basket Analysis
Two central goals of marketing are reducing customer acquisition costs and increasing customer lifetime value. Customer segmentation is an important step towards both of these goals - by learning more about present and prospective customers, marketing practitioners can focus on tailoring strategies to acquire and retain these different types of customers more effectively. Explore the regency, frequency, and monetary value (RFM) framework of customer interactions by performing K-means clustering and using the silhouette score to pick the optimal number of clusters. Next, switch to two alternative clustering techniques, known as agglomerative clustering and DBScan. Finally, perform market basket analysis, also known as affinity analysis, to predict what items that customers will purchase together, such as bread and jam. Use the a priori algorithm for computing frequent itemsets, and the calculation and implications of metrics such as support, confidence, lift, and conviction.
16 videos |
1h 52m
Assessment
Badge
Predictive Analytics: Predicting Responses to Marketing Campaigns
While growth has become a primary driver of business valuations and key performance indicators for executives, a renewed emphasis on profitability has led to greater attention being placed on cost-effective, tailored strategies for customer acquisition. This makes predicting the likely response of a prospect to a marketing campaign an increasingly important skill. Discover how to predict the purchase intention of shoppers on e-commerce websites, beginning with cleaning the data and then exploring it for patterns using heatmaps and correlation analysis. Next, perform classification analysis to predict whether a shopper will purchase something. Then, explore different evaluation metrics such as the confusion matrix, recall, precision, and the F1-score. Finally, you will predict whether a specific user responded to a marketing campaign and use feature selection to optimize the number of explanatory variables used in the process.
13 videos |
1h 16m
Assessment
Badge
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COURSES INCLUDED
Applying Predictive Analytics
This 13-video course explores machine learning predictive analytics, and how its application can drive revenues, reduce costs, and provide a competitive advantage to businesses. Learners will observe the predictive modeling process and how to apply tools and techniques for performing predictive analytics, and how to use historical data to identify trends and patterns to forecast future events. First, you will learn about the predicative modeling process, the statistical concepts for predictive modeling, and regression techniques. This course uses two examples to demonstrate commonly used methods of predictive analytics, by examining decision trees and SVMs (support vector machines). Next, you will learn about survival analysis, market basket analysis, and how to apply data for cluster models. You will learn about random forests in predictive analytics, and you will examine probabilistic graphical models. Learn about classification models, and how to organize data into groups based on predicting the class of the data points. Finally, you will explore some best practices for predictive modeling.
14 videos |
1h 25m
Assessment
Badge
COURSES INCLUDED
Predictive Analytics: Case Studies on Predictive Analytics for Healthcare
Healthcare aims to improve the health of individuals, but generally, healthcare systems tend to be extremely strained. Using artificial intelligence (AI) with healthcare could potentially mitigate this strain on the system. In this course, examine how AI is used in healthcare, how to evaluate classification models, and the metrics that are significant in models used in disease diagnosis. Next, discover the importance of a model's recall or sensitivity and the computation of the ROC curve and AUC metrics. Finally, explore the process of compiling the datasets, training, and evaluating models from research papers that take a general look at the application of AI in disease and specific ailment diagnosis. Upon completion, you'll be able to identify healthcare use cases for AI and its limitations.
11 videos |
1h 27m
Assessment
Badge
Predictive Analytics: Detecting Kidney Disease Using AI
Nowadays, diseases such as Alzheimer's, heart disease, and diabetes are becoming ever more prevalent across the world. Use this course to get hands-on experience building a pipeline to diagnose chronic kidney disease using Azure Machine Learning designer. Explore the different features of Azure Machine Learning, its interface, and how components and resources come together to build a pipeline. Next, learn how to build a pipeline to create a dataset, implement various data cleaning tasks, and work with the cleaned dataset to build a logistic regression model to detect kidney disease. Finally, examine how models can be trained and evaluated for performance and deploy your pipeline. Upon completion, you'll be able to build and deploy a disease diagnosis Azure Machine Learning pipeline.
17 videos |
1h 47m
Assessment
Badge
Predictive Analytics: Identifying Tumors with Deep Learning Models
Azure Machine Learning designer allows you to create machine learning models using no-code, drag-and-drop pipelines. Use this course to build pre-trained neural network models that detect diseases from image scans using Azure Machine Learning designer. Learn how to set up data for model training, validation, and testing and how to feed that data into a pipeline that employs a DenseNet model. Next, discover how a model can be configured and substitute a pipeline's DenseNet model with a ResNet model. Finally, explore how a model's training metrics can be analyzed to understand what tweaks need to be applied to build a more reliable model. Upon completion, you'll be able to build DenseNet and ResNet models that can identify tumors from chest scan images.
10 videos |
1h 4m
Assessment
Badge
EARN A DIGITAL BADGE WHEN YOU COMPLETE THESE COURSES
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.BOOKS INCLUDED
Book
Data Mining: Practical Machine Learning Tools and Techniques, Fourth EditionTeaching readers everything they need to know to get going, this comprehensive resource offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations.
13h 23m
By Christopher J. Pal, Eibe Frank, Ian H. Witten, Mark A. Hall
Book
Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second EditionBased on the authors' practical experience in implementing data analysis and data mining, this proven go-to guide focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects.
3h 20m
By Glenn J. Myatt, Wayne P. Johnson
Book
A/B Testing: The Most Powerful Way to Turn Clicks into CustomersA guide to delivering a better user experience through A/B testing, this handy book outlines a simple way to test several different versions of a web page with live traffic, and then measure the effect each version has on visitors.
1h 51m
By Dan Siroker, Pete Koomen
Book
Working with Text: Tools, Techniques and Approaches for Text MiningProviding cross-disciplinary perspectives on text mining and its applications, this book offers focused studies describing text mining on historical texts, automated indexing using constrained vocabularies, and the use of natural language processing to explore the climate science literature.
6h 19m
By Emma L. Tonkin, Gregory J.L. Tourte (eds)
Book
Machine Learning: Algorithms and ApplicationsExplaining the concepts of machine learning algorithms, this practical book describes the application areas of each algorithm discussed, and uses simple, practical examples to help readers understand each algorithm.
2h 6m
By Eihab Bashier Mohammed Bashier, Mohssen Mohammed, Muhammad Badruddin Khan
Book
Text Mining in Practice with RWith the help of numerous real-world examples and case studies, this book takes a practical, hands-on approach to teaching you a reliable, cost-effective method to mining the vast, untold riches buried within all forms of text using R.
5h 5m
By Ted Kwartler
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BOOKS INCLUDED
Book
Applied Analytics through Case Studies Using SAS and R: Implementing Predictive Models and Machine Learning TechniquesIncluding industrial case studies of various domains, this book will help you examine business problems using a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language.
4h 46m
By Deepti Gupta
Book
Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Third EditionWritten for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling.
6h 12m
By Kattamuri S. Sarma
Book
Machine Learning with Spark and Python: Essential Techniques for Predictive Analysis, Second EditionDesigned specifically for those without a specialized math or statistics background, this book simplifies machine learning by focusing on two algorithm families that effectively predict outcomes, and by showing you how to apply them using the Python programming language.
5h 19m
By Michael Bowles
Book
Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second EditionProviding exercises at the end of each chapter, this book offers the theory behind, programming steps for, and examples of predictive modeling with SAS Enterprise Miner.
7h 11m
By Kattamuri S. Sarma
Book
Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second EditionProviding simple yet insightful quantitative techniques, this book contains essays offering detailed background, discussion, and a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.
7h 54m
By Bruce Ratner
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BOOKS INCLUDED
Book
Fundamentals of Predictive Analytics with JMP, Second EditionGoing beyond the theoretical foundation, this step-by-step book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis.
5h 22m
By B. D. McCullough, Ron Klimberg
Book
Predictive Analytics for Dummies, 2nd EditionProviding tips on outlining business goals and approaches, this friendly guide will help you discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data.
7h 12m
By Anasse Bari, Mohamed Chaouchi, Tommy Jung
Book
Effective CRM using Predictive AnalyticsIncluding numerous real-world case studies, this step-by-step book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques.
5h 5m
By Antonios Chorianopoulos
Book
Predictive Analytics and Data Mining: Concepts and Practice with RapidMinerWhether you are brand new to data mining or working on your tenth project, this easy-to-understand book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.
6h 56m
By Bala Deshpande, Vijay Kotu
Book
Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and MethodsHelping managers make the most of data technologies in their business area, this easy to read, in-depth guide provides readers with a solid understanding of predictive analytics, and how it should be applied to improve business decision-making and operational efficiency.
5h 51m
By Steven Finlay
Book
Data Mining and Predictive Analytics, Second EditionApplying a unified "white box" approach to data mining methods and models, this detailed second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis.
11h 17m
By Chantal D. Larose, Daniel T. Larose
Book
Applied Predictive Analytics: Principles and Techniques for the Professional Data AnalystClearly explaining the theory behind predictive analytics, this guide teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling.
7h 15m
By Dean Abbott
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BOOKS INCLUDED
Book
Data Mining and Predictive Analytics for Business Decisions: A Case Study ApproachThis book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis.
4h 8m
By Andres Fortino
SKILL BENCHMARKS INCLUDED
Predictive Analytics in Agriculture Literacy (Beginner Level)
The Predictive Analytics in Agriculture Literacy (Beginner Level) benchmark measures your ability to identify the need for artificial intelligence (AI) and predictive analytics in agriculture. You will be evaluated on your skills in identifying various use cases where one can apply predictive analytics. A learner who scores high on this benchmark demonstrates that they have a good understanding of predictive analytics needs and various use cases in agriculture.
8m
| 8 questions
Predictive Analytics in Cybersecurity Literacy (Beginner Level)
The Predictive Analytics in Cybersecurity Literacy (Beginner Level) benchmark measures your knowledge of identifying the need for artificial intelligence (AI) in cybersecurity. You will be evaluated on your ability to recognize the uses and limitations of AI, classification models, and various evaluation metrics used in cybersecurity. A learner who scores high on this benchmark demonstrates that they have an awareness of why and how predictive analytics and AI are used in cybersecurity.
8m
| 8 questions
Predictive Analytics in Cybersecurity Competency (Intermediate Level)
The Predictive Analytics in Cybersecurity Competency (Intermediate Level) benchmark measures your ability to apply machine learning and predictive analytics techniques to identify and prevent cyber attacks. You will be evaluated on your skills in identifying and testing network cyber attack data, visualizing data, setting up data for machine learning, and preprocessing and transforming data before applying machine learning techniques, classification models, and feature selection. A learner who scores high on this benchmark demonstrates that they have experience in applying machine learning and predictive analytics for cybersecurity with minimal supervision.
15m
| 15 questions
Predictive Analytics in Agriculture Competency (Intermediate Level)
The Predictive Analytics in Agriculture Competency (Intermediate Level) benchmark measures your ability to identify how to perform classification using machine learning on agriculture data. You will be evaluated on your skills in applying clustering to soil features and conditions and performing prediction using regression. A learner who scores high on this benchmark demonstrates that they have experience performing predictive analytics in agriculture.
21m
| 21 questions
Predictive Analytics in Operations Competency (Intermediate Level)
The Predictive Analytics in Operations Competency (Intermediate Level) benchmark measures your ability to apply suitable machine learning algorithms and perform predictive analytics for various use cases in the operations field. You will be evaluated on your skills in viewing important model attributes, comparing and evaluating model performance, and optimizing the models. A learner who scores high on this benchmark demonstrates that they have experience in performing predictive analytics and performance tuning of models in operations.
18m
| 18 questions
Predictive Analytics in Marketing and Retail Literacy (Beginner Level)
The Predictive Analytics in Marketing and Retail Literacy (Beginner Level) benchmark measures your ability to identify the need for artificial intelligence (AI) and predictive analytics in the marketing and retail sector. You will be evaluated on your skills in recognizing various case studies where one can apply predictive analytics. A learner who scores high on this benchmark demonstrates that they have a good understanding of predictive analytics needs and various use cases in marketing and retail.
7m
| 7 questions
Predictive Analytics in Marketing and Retail Competency (Intermediate Level)
The Predictive Analytics in Marketing and Retail Competency (Intermediate Level) benchmark measures your ability to apply machine learning algorithms and perform predictive analytics to predict sales and customer lifetime value. You will be evaluated on your skills in predicting responses to marketing campaigns and performing market basket analysis. A learner who scores high on this benchmark demonstrates that they have experience in applying predictive analytics in the marketing and retail domain with minimal supervision.
24m
| 24 questions
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SKILL BENCHMARKS INCLUDED
Predictive Analytics in Healthcare Literacy (Beginner Level)
The Predictive Analytics in Healthcare Literacy (Beginner Level) benchmark measures your ability to identify the need for artificial intelligence (AI) and predictive analytics in the healthcare domain. You will be evaluated on your skills in identifying various use cases where one can apply predictive analytics. A learner who scores high on this benchmark demonstrates that they have a good understanding of predictive analytics needs and various use cases in healthcare.
9m
| 9 questions
Predictive Analytics in Healthcare Competency (Intermediate Level)
The Predictive Analytics in Healthcare Competency (Intermediate Level) benchmark measures your ability to identify and apply predictive analytics in the healthcare domain. You will be evaluated on your skills in using predictive analytics for various use cases, such as detecting kidney diseases and identifying tumors with deep learning models. A learner who scores high on this benchmark demonstrates that they have experience performing predictive analytics on data in the healthcare domain.
16m
| 16 questions
Predictive Analytics in Operations Literacy (Beginner Level)
The Predictive Analytics in Operations Literacy (Beginner Level) benchmark measures your ability to identify various mathematical and machine learning (ML) models and artificial intelligence (AI) used in operations. You will be evaluated on your skills in applying ML models for various tasks, such as selecting reliable suppliers, detecting faulty motors, and optimizing a supply chain. A learner who scores high on this benchmark demonstrates that they have an awareness and understanding of why and how predictive analytics and AI is used in operations.
9m
| 9 questions
