Introduction to Data Science (E-DAT501)

Concepts covered in this course would provide relevant theoretical and hands-on programming knowledge. Every topic is demonstrated using easy to understand real world examples. The following topics would be covered during the course duration:

• Topic 1: Data Science Overview
• Topic 2: Working with Jupyter Notebooks
• Topic 3: Crash-Course on Python
• Topic 4: Numerical Computation
• Topic 5: Data Pre-Processing
• Topic 6: Feature Engineering
• Topic 7: Data Visualization
• Topic 8: TimeSeries
• Topic 9: Machine Learning (ML)
• Supervised Learning
• Unsupervised Learning

The ability to retrieve and analyze data has become one of the most important challenges in almost all disciplines, sectors and industries. In this course, students will be familiarized with basic tools and processes used in computer science ("data science"). Students work through the entire data flow, from retrieval, purification and modeling, to storing the data. The work is performed using the Python stack, which includes: IPython, NumPy, Pandas, Matplotlib and Jupyter Notebooks. Students learn to structure their work using the CRISP-DM and Data Science process ("Ask, Get, Explore, Model, Communicate and Visualize"). After completing the course, the student will develop the following competence:

Knowledge

• Develop programs to load, analyze, clean, transform, merge, reshape and save data.
• Compare regular Python, NumPy, and Panda data structures and use one of these for a defined task.
• Use Jupyter notebook for investigative computing.
• Develop algorithms for machine learning or data mining.

Skills

• Hands-on experience in python and data science.
• Data analysis by CRiSP-DM and Data Science Processes.
• Create good visualizations of data analysis using matplotlib.
• Optimize data analysis using available structure and methods.
• Evaluate, communicate and defend results from data analysis.

General Competence

• Develop an intuitive understanding about various data science tools and approaches
• Solve machine learning and data analysis tasks using a structured approach.

Course teacher(s)

Head of Department:

Course teacher:

Course coordinator:

A total of 12 lectures will cover the course syllabus

• Every lecture would be covered through bite-size recorded videos.
• Three hands-on programming assignments will be presented to the students
• Assignment 1: Topics 1-3
• Assignment 2: Topics 4-7
• Assignment 3: Topic 8 & 9
• Students will work individually and have 2 weeks for each assignment after their release
• Three live session would be conducted to discuss the solutions for each assignment