The subject has two teaching sessions every week. Each Session is divided into Two parts: part one is predominantly teacher led, the teacher explains the new contents and theory; part two, the students work on exercises to consolidate the knowledge they have learnt. Every two sessions, individual or group evaluations are carried out by means of written tests, individual or group activities and collection of exercises carried out at home, etc.

The following table relates the learning outcomes to the areas and the content taught to achieve them:
LO1. Use Python to read and transform data into different formats: Develop solutions on your own using standard libraries such as Numpy, Pandas, Matplotlib or SciPy.
LO2. Generate basic statistics and metrics using data on disk: Retrieve data from disk storage, load it into an appropriate format, and clean and preprocess the data as needed.
Calculate basic statistics (e.g., mean, median, standard deviation) and relevant metrics (e.g., average, percentage) based on the prepared data, and present the findings.
LO3. Work with computing tasks distributed over a cluster: Set up a computing cluster environment, including selecting appropriate hardware, configuring software frameworks and establishing network communication between cluster nodes.
Develop and execute computing tasks, including tasks like data parallelism, task distribution, fault tolerance, and resource management.
LO4. Convert data from various sources in storage or querying formats: Identify diverse data sources such as files and databases and implement procedures to extract data from these sources while handling any format-specific challenges.
Develop conversion processes to transform and standardize data from different sources into a common format or structure, ensuring data quality, consistency, and compatibility for downstream analysis or storage.
LO5. Prepare data for statistical analysis, visualization and machine learning: Identify and address missing values, outliers, and inconsistencies in the dataset by applying techniques such as imputation, scaling, and encoding categorical variables to ensure the data is ready for analysis and modeling.
Create new relevant features, and select informative variables, optimizing the dataset for statistical analysis, visualization, and machine learning model training, while preserving the integrity of the data's information.
LO6. Present data in the form of effective visuals: Identify the most appropriate types of data visualizations (e.g., bar charts, scatter plots, heatmaps) based on the nature of the data and the insights to be conveyed, taking into consideration factors like data distribution, relationships, and patterns.
Design and create compelling visualizations by choosing suitable colors, labels, and titles, ensuring clarity, accuracy, and aesthetic appeal, and then integrate these visuals into reports or presentations to effectively communicate data-driven insights to stakeholders.