Introduction to Data Science (DM1)

Databases, NoSQL and Big Data

This course introduces the field of data science, showing what it covers from exploring data and creating visualizations to applying basic machine learning methods, and interpreting and evaluating results for practical use.

No programming skills are required; the hands-on part runs on a cloud platform (BigML) or on popular desktop tools like RapidMiner and Weka. The course covers data preparation, basic model evaluation, and a practical NLP example.

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The course:

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• Introduction and key concepts (data science vs. machine learning vs. artificial intelligence vs. data mining)

• Typical workflow for analytical projects and stages of data analytics

• Data, data types, and data quality

• Exploratory data analysis and data visualization

• Tools for data science and common choices
1. Local desktop tools (on a local computer)
2. R and Python languages (for Python, introduction to core libraries)
3. Cloud platforms

• Data preparation
1. Selection
2. Data cleaning
3. Data transformation (value grouping, discretization, derived columns, …)
4. Sampling

• Machine learning techniques
1. Linear regression
2. Classification tasks – logistic regression, decision trees, neural networks, Bayesian approaches
3. Clustering
4. Association rules
5. Anomaly detection

• Interpreting results and model evaluation

• Natural language processing with a practical example

• State of the art in data science, machine learning, and artificial intelligence

Assumed knowledge:

Basic computer user skills and basic statistics.

Schedule:

2 days (9:00 AM - 5:00 PM )

Language: