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 science and engineering. The goal for this course is 2-fold: (1) to be able to approach business problems using analytical data- approach and (2) to competently interact on the topic of data science for business analytics. Enterprises nowadays adopt a data-driven decision-making (DDD) methods which refer to the practice of basing decisions on the analysis of data, rather than purely on intuition. Moreover, data science is an essential component for companies in the high-tech domain which are engaged in the development of data products and data services.
The course will present basic concepts and algorithms require to communicate in a data-driven environment.
Additional information is provided in appendices to extend the learning experience after the course has been completed.
This course is intended for IT developers, digital marketers, CTO and business analysts taking their first steps with data science, data mining and machine learning and provides them with the skills required for becoming a productive data scientist in that environment. The curriculum includes topics such as data mining algorithms and techniques. The course is suitable for people planning to engage in data science and big data analytics projects.
This course is designed for people with engineering/scientific academic background and with soft skills in programming and statistics. The course doesn’t not include programming tasks.
Business Intelligence (BI) and Analytics
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- 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 normalization
- Data cleaning
- Data reduction (PCA,ICA, and more)
- Outlier detection and analysis
- Data imputation
- Binning and discretization methods
- Matrix decomposition algorithms – PCA and ICA
Predictive analytics and 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
- Hierarchical clustering
- Density-based methods
Introduction to Deep Learning and Reinforcement Learning