Webster University • Webster Groves, MO • George Herbert Walker School of Business & Technology • Mathematics/Computer Science
Introduction to Data Science CSIS-2500
This course serves as an introduction to data science, which incorporates theories and techniques from many areas, such as statistics and data mining, to extract valuable knowledge from data. The course provides an overview of fundamental principles about how data science can provide solutions to business problems, techniques for extracting meaning from data, and general approaches of data analytical thinking.
At the completion of this course students will be able to:
- Explain data, what it represents and how it is organized.
- Explain how to represent data in a variety of common formats
- Discuss predictive modeling, the distinction between model training, evaluation, and use.
- Explain big data and problems it presents and some techniques for dealing with big data challenges.
- Discuss how to visualize data for exploratory and confirmatory analysis
- Discuss data mining and some predictive models, such as decision tree, linear models and probability estimation.
- Discuss and apply model evaluation, accuracy, ranking metrics, and model complexity.
- Explain some of the main applications of data science.
Data Science for Business
Publisher: O'Reilly Media
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- Introduction to Data Science
- The Ubiquity of Data Opportunities
- Data Science, Engineering, and Data-Driven Decision Making
- Data-Analytic Thinking
- From Business Problems to Data Mining Tasks
- The Data Mining Process
- Chapter 1 Introduction: Data-Analytic Thinking
- Chapter 2 Business Problems and Data Science Solutions
- Quiz W1
- From Correlation to Supervised Segmentation
- Models, Induction, and Prediction
- Supervised Segmentation
- Visualizing Segmentations
- Trees as Sets of Rules
- Probability Estimation
- Chapter 3 Introduction to Predictive Modeling: From Correlation to Supervised Segmentation
- Quiz W2
- Fitting Model to Data and Overfitting Avoidance
- Classification via Mathematical Functions
- Regression via Mathematical Functions
- Generalization and Overfitting
- From Holdout Evaluation to Cross-Validation
- Overfitting Avoidance and Complexity Control
- Chapter 4 Fitting a Model to Data
- Chapter 5 Overfitting and Its Avoidance
- Quiz W3
- Similarity, Neighbors, and Clusters
- Similarity and Distance
- Nearest Neighbors for Predictive Modeling
- Nearest-Neighbor Reasoning
- Solving a Business Problem Versus Data Exploration
- Midterm exam
- Chapter 6 Similarity, Neighbors, and Clusters
- Quiz W4
- Model Evaluation and Performance Visualization
- Evaluating Classifiers
- Generalizing Beyond Classification
- A Key Analytical Framework: Expected Value
- Ranking Instead of Classifying
- Profit Curves
- Chapter 7 Decision Analytic Thinking I: What Is a Good Model?
- Chapter 8 Visualizing Model Performance
- Quiz W5
- Probabilistic Reasoning and Text Mining
- Combining Evidence Probabilistically
- Applying Bayes’ Rule to Data Science
- A Model of Evidence “Lift”
- Text Representation
- Beyond Bag of Words
- Chapter 9 Evidence and Probabilities
- Chapter 10 Representing and Mining Text
- Quiz W6
- Toward Analytical Engineering
- Targeting the Best Prospects
- Co-occurrences and Associations
- Profiling: Finding Typical Behavior
- Link Prediction and Social Recommendation
- Data Reduction, Latent Information, and Movie Recommendation
- Chapter 11 Decision Analytic Thinking II: Toward Analytical Engineering
- Chapter 12 Other Data Science Tasks and Techniques
- Quiz W7
- Data Science and Business Strategy
- Achieving Competitive Advantage with Data Science
- Sustaining Competitive Advantage with Data Science
- Attracting and Nurturing Data Scientists and Their Teams
- What Data Can’t Do: Humans in the Loop, Revisited
- Privacy, Ethics, and Mining Data About Individuals
- Final exam
- Chapter 13 Data Science and Business Strategy
- Chapter 14 Conclusion
- Quiz W8
- Final Exam