Big Data Analytics (Specialisation studies)

This course introduces the core concepts and workflow of analytics and ML (Machine Learning) through a hands-on approach. You learn how an analytics process is implemented from problem definition, data understanding, and preprocessing to modelling, interpretation, visualization, and the practical use of results. The course focuses especially on predictive time series problems and forecasting with machine learning for regression. You also learn how to validate forecasting results by calculating and interpreting forecasting errors. Through real-life examples and practical exercises, the course builds a strong foundation for further studies in analytics, covering analytics systems, Python development, feature engineering, time series forecasting, visualization, and evaluation.

This course gives you a practical and structured introduction to predictive machine learning with static data. You learn how to move from data acquisition and preprocessing to model selection, parameter tuning, evaluation, and comparison of results. The course covers core approaches for both classification and regression, including linear models, neural networks, and numerical methods for representing and processing text. You also develop an understanding of how to build optimized ML solutions that are robust, scalable, and ready for production-oriented use.

This course focuses on the principles of data visualization as a way to turn analysis into insight, communication, and action. You learn to design effective and user-centered visualizations by combining core ideas from data visualization with interface and interaction design. Topics include visual perception, data and chart types, dashboards, interaction basics, and geospatial visualization. The course has a strong practical emphasis on producing clear and meaningful graphical material, while also helping you understand how visual analytics can support communication, storytelling, and data-informed decision-making in organisations.

This course introduces the concepts and methods behind modern deep learning and foundation models. You learn the principles of gradient descent and backpropagation, and you gain hands-on experience in building neural networks from scratch as well as fine-tuning pre-trained models. The course also develops your ability to handle large-scale data programmatically for training and testing deep learning systems. You learn how DL (Deep Learning) can be applied to demanding analytics tasks and how model outputs can be integrated into the design of intelligent services. 

This course provides an overview of how ML and big data workflows are supported by cloud computing, scalable infrastructure, and modern data engineering practices. You are introduced to descriptive and predictive modelling in smaller-scale settings and then learn how similar approaches can be adapted to large-scale data environments. The course covers the analytical process from data-related requirement handling and domain understanding to modelling, tooling, and verification of results. It also highlights how computing resources and engineering choices affect performance, scalability, and the deployment of analytics solutions.

This course introduces service design as a strategic and human-centered approach to innovation, business transformation, and sustainable value creation. You learn the main theories, methods, and tools of service design, and develop the ability to analyse needs, transform processes, and create service solutions in collaboration with users, partners, and interdisciplinary teams. The course also strengthens your skills in strategic planning, service design leadership, and critical communication, with a strong emphasis on sustainability, innovation, and future-oriented thinking.

• Basic syntax and data types (None, bool, int, float, str, list, tuple, dict).
• Understanding the concept of variables and variable scope.
• Control structures (if-else, for and while loops).
• Ability to write simple functions and use them in programs.
• Basic Python libraries like math, random, as well as understanding and utilizing classes from the datetime module.

• Ability to handle basic string and list methods for manipulation. For example:

  For strings:

  • upper(): Converts all the characters in a string to uppercase.
  • lower(): Converts all the characters in a string to lowercase.
  • strip(): Removes leading and trailing whitespace from a string.
  • split(): Splits a string into a list where each word is a list item.
  • replace(): Replaces a specified phrase with another specified phrase.

  For lists:

  • append(): Adds an element at the end of the list.
  • insert(): Adds an element at the specified position.
  • remove(): Removes the first item with the specified value.
  • pop(): Removes the element at the specified position.
  • sort(): Sorts the list.
• Reading from and writing to files.
• Handling errors and exceptions using try/except blocks.
• An understanding of Python's logging system.

• Handle regular expressions for pattern matching in strings.
• Understand object-oriented programming: classes, objects, methods.
• Understand and use list comprehensions and lambda functions.
• Understand Python's memory management and optimization techniques.

Familiarity with the following Python libraries would be beneficial:

• NumPy: For numerical computations.
• Pandas: For data manipulation and analysis.
• Matplotlib: For data visualization.

Your code writing style should be clear, concise, and efficient. Avoid excessive code repetitions by adhering to the DRY (Don't Repeat Yourself) principle. This means that information is not duplicated, and you use appropriate structures to encapsulate repeated code. This not only reduces redundancy but also improves readability, maintainability, and scalability of the code. Code you produce should be self-explanatory, with meaningful variable, function, and class names. Comments should be used to explain the 'why' rather than the 'what' or 'how'. This promotes consistency and makes the code easier to understand and debug. Remember, code is read more often than it is written, so strive for clarity and simplicity.

Being comfortable with basic Linux terminal commands is essential to interact with Linux-based systems efficiently. These skills are indispensable for software and data engineers, whom our program is targeting. Knowledge of these terminal commands ensures a deeper understanding of computer systems and equips you with the tools needed for professional development in technology fields.

• man: Display the user manual of a command
• ssh: Secure shell remote login.
• ls: List directory contents.
• cd: Change the current directory.
• pwd: Print the name of the current directory
• cp: Copy files and directories.
• mv: Move or rename files and directories.
• rm: Remove files and directories.
• cat: Concatenate and display file content.
• echo: Display a line of text.
• head: Output the first part of files.
• tail: Output the last part of files.
• grep: Search for a specific pattern within files.
• find: Search for files in a directory hierarchy.
• chmod: Change the permissions of files or directories.
• chown: Change the owner and group of files or directories.
• df: Report file system disk space usage.
• du: Estimate file and directory space usage.
• tar: Archive files.
• gzip: Compress or expand files.
• ps: Report a snapshot of the current processes.
• top: Display Linux tasks.
• kill: Send a signal to a process.
• curl: Transfer data to or from a server.
• scp: Securely copy files between a local host and a remote host or between two remote hosts.
• rsync: Only transfers the changes made rather than transferring all the files again.

You should be able to use these Linux commands comfortably and understand their options and parameters. You should also understand the concept of Linux file permissions and know how to use pipes (|) and redirection (>, >>, <).