If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.
It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.
It is not aimed at people configuring the solution, those people will benefit from the big picture though.
- Data Sources
- Minding Data
- Recommender systems
- Target Marketing
- Structured vs unstructured
- Static vs streamed
- Attitudinal, behavioural and demographic data
- Data-driven vs user-driven analytics
- data validity
- Volume, velocity and variety of data
- Building models
- Statistical Models
- Machine learning
- Clustering
- kGroups, k-means, the nearest neighbours
- Ant colonies, birds flocking
- Decision trees
- Support vector machine
- Naive Bayes classification
- Neural networks
- Markov Model
- Regression
- Ensemble methods
- Benefit/Cost ratio
- Cost of software
- Cost of development
- Potential benefits
- Data Preparation (MapReduce)
- Data cleansing
- Choosing methods
- Developing model
- Testing Model
- Model evaluation
- Model deployment and integration
- Selection of R-project package
- Python libraries
- Hadoop and Mahout
- Selected Apache projects related to Big Data and Analytics
- Selected commercial solution
- Integration with existing software and data sources
- Understanding of traditional data management and analysis methods like SQL, data warehouses, business intelligence, OLAP, etc...
- Understanding of basic statistics and probability (mean, variance, probability, conditional probability, etc....)
21 hours (usually 3 days including breaks)
