PRED-ANA.AP1
Predictive analytics: Data Mining, Machine Learning, and Data Science for Practitioners
Discover how to make the most of data with our Predictive Analytics, Data Mining, and Machine Learning course.
- Practice in 10 Hands-On Labs — nothing to install
- 12 Interactive Lessons and 101 topics mapped to the official exam objectives
- 213 Practice Test Questions
Intermediate Self-paced · 1 year access 4.6/5 (61 Reviews)
10 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
12Interactive Lessons
101Topics
10LiveLab
213Practice Test Questions
25Videos
105Flashcards
105Glossary of terms
01 / Skills you'll get
What you will be able to do
This Predictive Analytics course teaches practitioners how data can work magic in decision-making, We’ll guide you through essential topics like data preprocessing, algorithms basics, and big data, all while keeping it fun and engaging. You’ll explore powerful tools for text analytics and sentiment analysis, learn how to create decision trees, and even get hands-on with popular techniques like k-means clustering. Use what you learn to turn data into real-world results that dazzle and impress.
- Learn various methods for discovering patterns and insights in data
- Gain the ability to create and validate models that predict future outcomes
- Apply popular algorithms, such as k-nearest Neighbor, Naive Bayes, and linear regression
- Classify tasks by creating and analyzing decision trees
- Use k-means clustering and understand its applications
- Exploring text analytics and sentiment analysis to extract insights from textual data
- Learn how to assess model performance and make improvements
- Develop skills to visually present data and analyze results effectively
- Gain practical experience with tools like KNIME and Python for data analysis
Course Highlights
-
12 Structured Lessons Comprehensive coverage of core course objectives
-
10 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
-
213 Practice Questions Assessment tests with detailed answer rationales
-
1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
12 Interactive Lessons · 101 topics01 Introduction 2 topics +
- About This eBook
- Foreword
02 Introduction to Analytics 9 topics +
- What’s in a Name?
- Why the Sudden Popularity of Analytics and Data Science?
- The Application Areas of Analytics
- The Main Challenges of Analytics
- A Longitudinal View of Analytics
- A Simple Taxonomy for Analytics
- The Cutting Edge of Analytics: IBM Watson
- Summary
- References
03 Introduction to Predictive Analytics and Data Mining 8 topics · 2 LiveLab +
- What Is Data Mining?
- What Data Mining Is Not
- The Most Common Data Mining Applications
- What Kinds of Patterns Can Data Mining Discover?
- Popular Data Mining Tools
- The Dark Side of Data Mining: Privacy Concerns
- Summary
- References
2 LiveLab in this lesson — see the labs panel →
04 Standardized Processes for Predictive Analytics 8 topics +
- The Knowledge Discovery in Databases (KDD) Process
- Cross-Industry Standard Process for Data Mining (CRISP-DM)
- SEMMA
- SEMMA Versus CRISP-DM
- Six Sigma for Data Mining
- Which Methodology Is Best?
- Summary
- References
05 Data and Methods for Predictive Analytics 13 topics · 1 LiveLab +
- The Nature of Data in Data Analytics
- Preprocessing of Data for Analytics
- Data Mining Methods
- Prediction
- Classification
- Decision Trees
- Cluster Analysis for Data Mining
- k-Means Clustering Algorithm
- Association
- Apriori Algorithm
- Data Mining and Predictive Analytics Misconceptions and Realities
- Summary
- References
1 LiveLab in this lesson — see the labs panel →
06 Algorithms for Predictive Analytics 10 topics · 4 LiveLab +
- Naive Bayes
- Nearest Neighbor
- Similarity Measure: The Distance Metric
- Artificial Neural Networks
- Support Vector Machines
- Linear Regression
- Logistic Regression
- Time-Series Forecasting
- Summary
- References
4 LiveLab in this lesson — see the labs panel →
07 Advanced Topics in Predictive Modeling 6 topics · 1 LiveLab +
- Model Ensembles
- Bias–Variance Trade-off in Predictive Analytics
- Imbalanced Data Problems in Predictive Analytics
- Explainability of Machine Learning Models for Predictive Analytics
- Summary
- References
1 LiveLab in this lesson — see the labs panel →
08 Text Analytics, Topic Modeling, and Sentiment Analysis 8 topics · 2 LiveLab +
- Natural Language Processing
- Text Mining Applications
- The Text Mining Process
- Text Mining Tools
- Topic Modeling
- Sentiment Analysis
- Summary
- References
2 LiveLab in this lesson — see the labs panel →
09 Big Data for Predictive Analytics 10 topics +
- Where Does Big Data Come From?
- The Vs That Define Big Data
- Fundamental Concepts of Big Data
- The Business Problems That Big Data Analytics Addresses
- Big Data Technologies
- Data Scientists
- Big Data and Stream Analytics
- Data Stream Mining
- Summary
- References
10 Deep Learning and Cognitive Computing 10 topics +
- Introduction to Deep Learning
- Basics of “Shallow” Neural Networks
- Elements of an Artificial Neural Network
- Deep Neural Networks
- Convolutional Neural Networks
- Recurrent Networks and Long Short-Term Memory Networks
- Computer Frameworks for Implementation of Deep Learning
- Cognitive Computing
- Summary
- References
11 Appendix A: KNIME and the Landscape of Tools for Business Analytics and Data Science 8 topics +
- Project Constraints: Time and Money
- The Learning Curve
- The KNIME Community
- Correctness and Flexibility
- Extensive Coverage of Data Science Techniques
- Data Science in the Enterprise
- Summary and Conclusions
- Acknowledgment
12 Appendix B: Videos 9 topics +
- Introduction to Predictive Analytics
- Introduction to Predictive Analytics and Data Mining
- The Data Mining Process
- Data and Methods in Data Mining
- Data Mining Algorithms
- Text Analytics and Text Mining
- Big Data Analytics
- Predictive Analytics Best Practices
- Summary
Hands-On Labs Our edge
10 LiveLabs- Creating a Decision Tree in Python
- Creating a Decision Tree in KNIME
- Running k-Means Clustering Algorithm in KNIME
- Using the k-Nearest Neighbor Algorithm
- Using ANN and SVM for Prediction Type Analytics Problems
- Implementing Linear Regression in Python
- Implementing Linear Regression Model in KNIME
- Showcasing Better Practices With a Customer Churn Analysis
- Performing Topic Modeling
- Performing Sentiment Analysis
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
What is predictive analytics in data mining?+
Predictive analytics in data mining involves using statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. It helps uncover patterns and trends to make informed predictions.
What is the difference between predictive analytics, data mining, and machine learning?+
Data mining is the process of discovering patterns and knowledge from large amounts of data.
Predictive analytics use ML and statistical algorithms to predict and plan for future outcomes.
Machine learning involves using algorithms to extract and transform information into an understandable structure for further use.
What industries can I work in with the skills gained from this course?+
With skills in predictive analytics, data mining, and machine learning, you can work in various industries, including finance, healthcare, marketing, retail, manufacturing, and technology.
Who is this course for?+
This predictive business analytics course is ideal for anyone interested in data analytics, business intelligence, or machine learning.
Can I transition into a data science career after this course?+
Yes, this predictive analytics training course provides a solid foundation in data science principles, making it possible to transition into a data science career.
How will this course enhance my career prospects?+
This predictive analytics and machine learning course equips you with in-demand skills in analytics and data science, making you a more competitive candidate in the job market and opening doors to new opportunities.
Do I need any prior knowledge to take this course?+
No prior knowledge is required! Our best predictive analytics course is designed for both beginners and practitioners.
How does predictive analytics benefit businesses?+
Predictive analytics helps businesses forecast trends, understand customer behavior, optimize operations, and make data-driven decisions. This leads to better efficiency, higher profits, and a competitive advantage in the market.
Transform Data into Decisions
Develop the skills to analyze trends, predict outcomes, and drive informed decision-making.
- 1 year of full access
- 10 LiveLab included
- Certificate of completion