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Course Outline
Business Analytics Logic
1.1 The Ubiquity of Data Utilization Opportunities
1.2 Two Examples - Hurricanes and Customer Behavior
1.3 Data Science, Engineering, and Data-Driven Decision Making
1.4 Data Processing and “Big Data”
1.5 From Big Data 1.0 to Big Data 2.0
1.6 Data and Analytics as Strategic Assets
1.7 Business Analytics Logic - Summary
Business Problems and Data Science Solutions
2.1 From Business Problem to Data Exploration
2.2 Supervised and Unsupervised Methods
2.3 Data Exploration and Its Results
2.4 Consequences of Managing Data Science Projects
2.5 Analytical Techniques and Technologies
2.6 Summary
Predictive Modeling - From Correlation to Supervised Segmentation
3.1 Models, Induction, and Forecasting
3.2 Supervised Segmentation
3.3 Visualizing Results
3.4 Trees as Rule Sets
3.5 Probability Estimation
3.6 Case Study Analysis
3.7 Summary
Fitting the Model to Data
4.1 Classification Using Mathematical Functions
4.2 Regression
4.3 Class Probability Estimation and “Logistic” Regression
4.4 Nonlinear Functions
4.5 Neural Networks
4.6 Summary
Overfitting and How to Avoid It
5.1 Generalization
5.2 Overfitting
5.3 Analyzing the Overfitting Problem
5.4 Examples
5.5 Techniques to Avoid Overfitting
5.6 Learning Curves
5.7 Controlling Complexity
5.8 Summary
Similarity, Neighborhood, and Clusters
6.1 Similarity and Distance Metrics
6.2 Nearest Neighbor and Inference Rules
6.3 Key Techniques
6.4 Cluster Analysis
6.5 Applications in Business Problem Solving
When Is a Model Good?
7.1 Classifiers Used in Model Evaluation
7.2 Generalizations Beyond Classification Bounds
7.3 Analytical Frameworks
7.4 Examples of Basic Evaluation Techniques
7.5 Summary
Visualizing the Model
8.1 Using Ranks
8.2 Gain Charts
8.3 ROC Curves (Receiver Operating Characteristics)
8.4 Area Under the ROC Curve
8.5 Cumulative Response
8.6 Examples
8.7 Summary
Evidence and Probabilities
9.1 Example - Customer Targeting
9.2 Probabilistic Evidence Linking
9.3 Applying Bayes' Rules
9.4 Building the Model
9.5 Example of Model Application
9.6 Summary
Representing and Exploring Text
10.1 Why Is Text Important?
10.2 Why Is Working with Text Difficult?
10.3 Representation
10.4 Example
10.5 Entropy and Text
10.6 It’s Not a Bag of Words
10.7 Exploring Information
10.8 Summary
Analytical Engineering - Case Studies
Other Tasks and Techniques
12.1 Co-occurrences and Associations
12.2 Profiling
12.3 Predicting Links
12.4 Information Reduction and Selection
12.5 Bias, Distortion, and Variance
12.6 Case Study Analysis
12.7 Summary
Business Strategy and Data Science
13.1 Redux
13.2 Achieving Competitive Advantage
13.3 Maintaining Advantage
13.4 Resource Acquisition
13.5 New Ideas and Development
13.6 Organizational Maturity
How to Conduct Data Science Project Reviews
Conclusion
Requirements
The Data Science in Business training is designed for several groups of individuals. Firstly, it is aimed at people from the business world. Those who will work with statisticians and data analysts (data scientists, or as sometimes said in Poland, “data masters”). Very often, these individuals will manage projects focused on business data analytics or invest in data science initiatives. In addition to this group, the training is intended for those who will implement data analytics solutions. For these individuals, the focus is on presenting a platform for mutual understanding with business, which is not particularly interested in the details of implementation. Of course, we must not forget the third group—the aspiring data masters.
This training is not about algorithms. It is also not about specific big data systems. These topics are covered in separate trainings, but without knowledge of certain fundamental concepts and principles of data science initiatives, they are doomed to failure from the start. Because technology development is very rapid, it often overshadows the fundamentals on which solutions should be built to allow businesses to benefit effectively.
The training does not require advanced, specialized knowledge in statistics. Of course, one must be aware that by its nature, the material presented during the training has a somewhat technical character. The goal of the training is to enable participants to gain a significant understanding of data science, not just a general overview of the field. Despite this ambitious goal, the mathematical apparatus is limited to the absolutely necessary minimum. Generally speaking, the training includes everything necessary to understand the design and construction of advanced, data science-based solutions to business problems.
35 Hours
Testimonials (1)
large knowledge, work with real data
Tomasz Popiolek - Orange Szkolenia Sp. z o.o.
Course - Data Science w biznesie
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