Data Science & Machine Learning

• Explain the data science life cycle
• Explain Big Data and data analysis
• Explain methods for data preparations

Skills and abilities
Upon completion of this course, students will be able to:

• Apply unsupervised and supervised machine learning algorithms for problem-solving
• Apply fundamental concepts in statistics and probability theory, including key concepts like probability distributions, statistical significance, hypothesis testing and regression
• Use programming languages for data science/data analysis
• Perform data extraction from text
• Use exploratory data analysis (EDA) to describe the data using summary statistics and visualisation techniques

• Interpret and analyse the results of a data extraction process, as well as evaluate the effects of choices made during the process
  

The course covers the process of data science, i.e. a multidisciplinary approach to find, extract and discover patterns in data through a fusion of analytical methods, domain expertise and technology. In this context, the fields of data mining, forecasting, machine learning, predictive analytics, statistics and text analytics are covered.

Within the framework of the iterative data science process, business understanding is treated with problem identification for the specification of key variables that are to function as model goals and identification of relevant data sources. It also includes the formulation of questions that define business goals and that can be quantified by computer science technicians.

To control data quality, the acquisition of raw data, data processing (ETL), examination of data and modeling are included. To facilitate the development of model(s) and to find the model that best answers the initial questions, so called feature engineering is used, when raw data is extracted and distinctive features are created.

Finally, the evaluation of modeling and analysis, presentation of results and commissioning are discussed.

The Swedish grades U–VG.