Overview:
Predictive analytics is the practice of extracting insights from the existing data set with the help data mining, statistical modeling and machine learning techniques and using it to predict unobserved/unknown events
Pre-Requisite:
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….)
Audience:
- 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.
Course Curriculum
| Quick Overview | |||
| Data Sources Details | 00:00:00 | ||
| Minding Data Details | 00:00:00 | ||
| Recommender systems Details | 00:00:00 | ||
| Target Marketing Details | 00:00:00 | ||
| Datatypes | |||
| Structured vs unstructured Details | 00:00:00 | ||
| Static vs streamed Details | 00:00:00 | ||
| Attitudinal, behavioural and demographic data Details | 00:00:00 | ||
| Data-driven vs user-driven analytics Details | 00:00:00 | ||
| Data validity Details | 00:00:00 | ||
| Volume, velocity and variety of data Details | 00:00:00 | ||
| Models | |||
| Building models Details | 00:00:00 | ||
| Statistical Models Details | 00:00:00 | ||
| Machine learning Details | 00:00:00 | ||
| Data Classification | |||
| Clustering Details | 00:00:00 | ||
| kGroups, k-means, nearest neighbours Details | 00:00:00 | ||
| Ant colonies, birds flocking Details | 00:00:00 | ||
| Predictive Models | |||
| Decision trees Details | 00:00:00 | ||
| Support vector machine Details | 00:00:00 | ||
| Naive Bayes classification Details | 00:00:00 | ||
| Neural networks Details | 00:00:00 | ||
| Markov Model Details | 00:00:00 | ||
| Regression Details | 00:00:00 | ||
| Ensemble methods Details | 00:00:00 | ||
| ROI | |||
| Benefit/Cost ratio Details | 00:00:00 | ||
| Cost of software Details | 00:00:00 | ||
| Cost of development Details | 00:00:00 | ||
| Potential benefits Details | 00:00:00 | ||
| Building Models | |||
| Data Preparation (MapReduce) Details | 00:00:00 | ||
| Data cleansing Details | 00:00:00 | ||
| Choosing methods Details | 00:00:00 | ||
| Developing model Details | 00:00:00 | ||
| Testing Model Details | 00:00:00 | ||
| Model evaluation Details | 00:00:00 | ||
| Model deployment and integration Details | 00:00:00 | ||
| Overview of Open Source and commercial software | |||
| Selection of R-project package Details | 00:00:00 | ||
| Python libraries Details | 00:00:00 | ||
| Hadoop and Mahout Details | 00:00:00 | ||
| Selected Apache projects related to Big Data and Analytics Details | 00:00:00 | ||
| Selected commercial solution Details | 00:00:00 | ||
| Integration with existing software and data sources Details | 00:00:00 | ||
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