###### CCNA 200-301 Pearson uCertify Network Simulator

ISBN: 978-1-61691-837-8Cisco 200-301-SIMULATOR.AB1

(PRED-ANA.AP1) / ISBN : 978-1-64459-326-4

This course includes

Lessons

TestPrep

Hands-On Labs

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61
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Predictive analytics is all about foreseeing the future and making smarter and faster business decisions. Business analytics is often characterized by three levels/echelons representing the hierarchical nature of the term—descriptive, predictive, and prescriptive. Organizations usually start with descriptive analytics, then move into predictive analytics, and finally reach prescriptive analytics. Learn predictive analytics with uCertify's course Predictive analytics: Data Mining, Machine Learning, and Data Science for Practitioners. The course has well descriptive interactive lessons containing pre and post-assessment questions, knowledge checks, quizzes, flashcards, and glossary terms to get a detailed understanding of predictive analytics.

Get the support you need. Enroll in our Instructor-Led Course.

12+ Interactive Lessons | 134+ Exercises | 135+ Quizzes | 105+ Flashcards | 105+ Glossary of terms

66+ Pre Assessment Questions | 66+ Post Assessment Questions |

10+ LiveLab | 10+ Video tutorials | 01:15+ Hours

45+ Videos | 08:49+ Hours

1

- About This eBook
- Foreword

2

- 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

3

- 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

4

- 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

5

- 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

6

- Naive Bayes
- Nearest Neighbor
- Similarity Measure: The Distance Metric
- Artificial Neural Networks
- Support Vector Machines
- Linear Regression
- Logistic Regression
- Time-Series Forecasting
- Summary
- References

7

- 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

8

- Natural Language Processing
- Text Mining Applications
- The Text Mining Process
- Text Mining Tools
- Topic Modeling
- Sentiment Analysis
- Summary
- References

9

- 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

- 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

A

- 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

B

- 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

3

- Creating a Decision Tree in Python
- Creating a Decision Tree in KNIME

5

- Running k-Means Clustering Algorithm in KNIME

6

- 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

7

- Showcasing Better Practices With a Customer Churn Analysis

8

- Performing Topic Modeling
- Performing Sentiment Analysis