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Course Outline
Business data analytics logic
1.1 Widespread possibilities of using data
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 data analytics as strategic assets
1.7 Data analytics logic - summary
Business problems and solutions using data science
2.1 From business problem to data mining
2.2 Supervised and unsupervised methods
2.3 Data mining 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 Visualization of results
3.4 Trees as sets of rules
3.5 Probability estimation
3.6 Case study
3.7 Summary
Fitting the model to the 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 Analysis of the overfitting problem
5.4 Examples
5.5 Techniques to avoid overfitting
5.6 Learning curves
5.7 Complexity Control
5.8 Summary
Similarity, neighborhood and clusters
6.1 Similarity and distance measure
6.2 Nearest neighborhood and inference rules
6.3 Key techniques
6.4 Cluster analysis
6.5 Applications to solving business problems
When is a model good?
7.1 Classifiers used in model evaluation
7.2 Generalizations beyond the limits of classification
7.3 Analytical framework
7.4 Examples of the application of basic evaluation techniques
7.5 Summary
Model visualization
8.1 Application of ranks
8.2 Profit curves
8.3 ROC (Receiver Operating Characteristics) curves and graphs
8.4 Area under the ROC curve
8.5 Cumulative Response
8.6 Examples
8.7 Summary
Evidence and probabilities
9.1 Example - customer focus
9.2 Probabilistic evidence combinations
9.3 Application of Bayes' rules
9.4 Building a model
9.5 Example of using the model
9.6 Summary
Representing and mining text
10.1 Why is the text important?
10.2 Why is working with text difficult?
10.3 Representation
10.4 Example
10.5 Entropy and text
10.6 This is not a bag of words
10.7 Message mining
10.8 Summary
Analytical engineering - case studies
Other tasks and techniques
12.1 Co-occurrences and associations
12.2 Profiling
12.3 Forecasting connections
12.4 Information reduction and selection
12.5 Misstatements, Misstatements and Variance
12.6 Case analysis
12.7 Summary
Business strategy and data science
13.1 Redux
13.2 Achieving a competitive advantage
13.3 Maintaining the Advantage
13.4 Resource Acquisition
13.5 New ideas and developments
13.6 Maturity of the organization
How to conduct data science project reviews
End
Requirements
Data Science business training is addressed to several groups of people. Firstly, it is addressed to people from the business itself. Those who will work with statisticians and data analysts (data scientists, or as they are sometimes called in Poland, "data masters"). Very often, these people will manage projects focused on business data analytics or will invest in data science ventures. In addition to this group, the training is intended for those who will implement solutions focused on data analytics. In the case of these people, it is about presenting a platform for mutual understanding with business that is not very interested in the details of the implementation itself. Of course, we should not forget about the third group. About those who aspire to become a data champion.
The training is not algorithm training. It is also not training in specific big data systems. Separate training is devoted to these topics, but without knowledge of certain fundamental concepts and principled principles, data science projects are doomed to failure. Because the development of technology is very fast, it often obscures the foundations on which solutions that the business can use effectively should be built.
The training does not require sophisticated, specialized knowledge in the field of statistics. Of course, you should be aware that, by its very nature, the material presented during the training is somewhat technical in nature. The goal of the training is to enable participants to gain a significant understanding of data science , not just a general introduction to the field. Despite this rather ambitious goal, the mathematical apparatus is limited to the absolutely necessary minimum. Broadly speaking, the training covers everything you need to understand to design and build 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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