Course Outline
Data science is a set of fundamental principles that support the extraction of information and knowledge form data. It involves concepts for extraction of knowledge via the analysis of data using techniques from various fields such as statistics, machine learning and data mining. This two-day workshop concentrates on the fundamental concepts of data
Upcoming Meetings
Modules
Basic methodology
- CRISP DM – the journey from business understanding to model deployment)
Data Understanding and Engineering
- Data integration
- Data transformation
Exploratory Data Analysis (EDA)
- Descriptive analytics and statistics
- Basic statistical inference and measures of uncertainty and irregular cardinality
Data pre-processing
- Data normalization
- Data cleaning
- Data reduction (PCA,ICA, and more)
- Outlier detection and analysis
- Data imputation
- Binning and discretization methods
- Matrix decomposition algorithms - PCA & ICA
Predictive analytics & classification: Supervised Learning
- Linear and logistic regression
- Decision trees
- Ensemble models (Random Forest, Bagging and Boosting)
- SVM (support vector machine)
- KNN (K-nearest neighbors)
- Neural networks
Clustering (from k-means to hierarchical clustering) Unsupervised Leaning
- K-Means
- K-Medoids
- Hierarchical clustering
- Density-based methods
Introduction to Deep Learning and Reinforcement Learning
Prerequisites
- People with engineering/scientific academic background and with soft skills in programing and statistics
Upcoming Meetings
The course will present basic concepts and algorithms require to communicate in a data-driven environment"Download Full Syllabus