This Industry-oriented data science curriculum is designed to equip students with the skills and knowledge needed to apply data science techniques in real-world industry settings. This is Add-On course which can be taken after completing Data Science 101 or along with Data Science 101. Here are some key components we intend to discuss:

• Machine Learning and AI:

• Supervised and Unsupervised Learning: Algorithms and techniques for both types of learning.
• Deep Learning: Neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
• Natural Language Processing (NLP): Techniques for processing and analyzing human language.

• Domain-Specific Applications:
  • Lifesciences: Data science applications in medical imaging, patient data analysis, Predictive modelling for clinical trials outcome.

• Project-Based Learning:
  • Hands on projects that simulate real-world industry problems.
  • Capstone projects that require students to apply their skills to solve complex data science problems.

• Ethics and Legal Considerations:
  • Understanding of data privacy, ethical considerations, and legal compliance in data handling.

For each topic, we will discuss in detail, practise with synthetically made industry data, assignments will be given and feedback will be shared. Test will be conducted. Additional help will be provided to prepare on vendor certification for example, Microsoft Data Scientist DP 100 exam.