The course is targeted at professionals who are interested in learning and understanding data science in sufficient depth and breadth. The course will provide an overview of how to pose meaningful data analytics problems for real-life applications. At the end of the course, participants will develop a structured thinking approach to transition from data to data science problem definitions. Python, an open source tool will be introduced in depth. Important and commonly used machine learning and AI algorithms will be described in detail. Laboratory sessions will be conducted as part of each module with the expectation that the participants will be able to apply these algorithms on realistic data.

Learning Objectives :

1. Introduce the participants to the field of ML/AI – background and key concepts 
2. Introduce Python as a programming language for data science
3. Introduce data visualization techniques
4. Introduce the participants to a comprehensive overview of linear algebra, statistics and optimization concepts – critical concepts for the understanding of data science
5. Introduce the participants to in-depth explanation of data science algorithms – supported by hands-on work in Python from an application viewpoint
6. Introduce the participants to real-life applications of data science – a case study approach

Learning Outcomes :

1. Describe a flow process for data science problems (Remembering) 
2. Classify data science problems into standard typology (Comprehension) 
3. Develop Python codes for data science solutions (Application) 
4. Correlate results to the solution approach followed (Analysis) 
5. Assess the solution approach (Evaluation)

After completing this programme, participants will be able to : 

1. Prepare data for statistical modelling
2. Perform exploratory data analysis 
3. Visualize data 
4. Perform predictive modelling 
5. Execute machine learning algorithms 
6. Develop an appreciation for basic machine learning algorithms 
7. Develop their knowledge, skills and competence