NobleProg klassrum i Scandic Linköping, Gamla Tanneforsvägen 51, Linköping, sweden, 582 54
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It is all.## Course Outline### Course TitleAdvanced Data Science Techniques### Course DescriptionThis course covers advanced data science techniques, focusing on machine learning, data visualization, and big data processing. Students will learn to apply sophisticated algorithms and tools to analyze and interpret complex datasets. The curriculum includes hands-on projects and case studies to ensure practical application of the concepts learned.### Learning Objectives- Understand and implement advanced machine learning algorithms.- Utilize data visualization tools to communicate insights effectively.- Process and analyze big data using industry-standard tools and frameworks.- Develop and deploy predictive models using real-world datasets.- Collaborate on data science projects, applying best practices in data management and analysis.### Prerequisites- Basic knowledge of Python programming.- Familiarity with fundamental data science concepts.- Experience with basic machine learning algorithms.- Working knowledge of data manipulation libraries such as Pandas and NumPy.### Course Duration8 weeks### Course Schedule#### Week 1: Introduction to Advanced Data Science- Overview of advanced data science techniques.- Setting up the development environment.- Introduction to key tools and frameworks.#### Week 2: Advanced Machine Learning Algorithms- Deep dive into supervised and unsupervised learning.- Implementing algorithms like Support Vector Machines (SVM) and Random Forests.- Evaluating model performance using cross-validation.#### Week 3: Data Visualization- Introduction to data visualization libraries (e.g., Matplotlib, Seaborn).- Creating informative and engaging visualizations.- Best practices for communicating data insights.#### Week 4: Big Data Processing- Overview of big data technologies (e.g., Hadoop, Spark).- Hands-on experience with distributed data processing.- Integrating big data tools with data science workflows.#### Week 5: Predictive Modeling- Building and validating predictive models.- Techniques for feature selection and engineering.- Deploying models in production environments.#### Week 6: Case Studies and Real-World Applications- Analyzing real-world datasets.- Applying advanced data science techniques to solve industry problems.- Collaborative project work and peer reviews.#### Week 7: Advanced Topics in Data Science- Exploring cutting-edge research in data science.- Introduction to deep learning and neural networks.- Ethical considerations in data science.#### Week 8: Final Project and Presentation- Students will work on a comprehensive data science project.- Presenting findings and insights to the class.- Peer and instructor feedback.### Assessment- Participation in class discussions and projects: 20%- Midterm project: 30%- Final project presentation: 50%### Resources- Recommended textbooks: - "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron. - "Data Science from Scratch" by Joel Grus.- Online resources and tutorials.- Access to industry-standard software and datasets.
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Got to know new features in OS which I wasn't aware before.
I thought the training was very thorough and while we covered a lot of material, Martin made ample time for questions and gave good focus to each individual and their different requirements.
I enjoyed the exercices gives a good insight.
He has patient about the questions asked by students. He has knowledge to answer the questions.
The lectures were fine.
I genuinely liked learning a new skill.
