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Masterclass overview
Module 1: Foundations of Data Science
Mathematics for Data Science: Embrace fundamental mathematical concepts such as linear algebra, calculus, and statistics, which serve as the backbone of data analysis and modeling.
Data Ethics and Privacy: Navigate the ethical considerations of data collection and analysis, complying with privacy regulations while upholding responsible data handling principles.
Module 2: Data Manipulation and Analysis
Data Manipulation with NumPy and Pandas: Master the use of NumPy arrays and Pandas DataFrames for efficient data manipulation, tackling challenges like cleaning, preprocessing, and handling missing data.
Exploratory Data Analysis (EDA): Implement techniques to comprehensively explore complex datasets, identifying valuable patterns, trends, and correlations.
Python Libraries for Data Science: Survey essential Python libraries tailored for data analysis, strategically selecting the most suitable ones for specific tasks.
Module 3: Data Cleaning and Data Wrangling Techniques
Data Cleaning Strategies: Learn strategies to identify and mitigate data quality issues, employing advanced techniques for data imputation and validation.
Data Wrangling with Pandas: Elevate data transformation skills using Pandas, including merging, joining, grouping, and aggregating datasets.
Data Cleaning and Wrangling Best Practices: Construct a robust data cleaning workflow, leveraging automation and scripting through real-world case studies.
Module 4: Data Modeling and Machine Learning
Introduction to Machine Learning: Navigate the machine learning workflow, differentiate between supervised, unsupervised, and reinforcement learning, and select appropriate algorithms.
Supervised Learning: Deep dive into supervised learning algorithms like regression and classification, and learn to evaluate models using performance metrics.
Unsupervised Learning and Model Deployment: Unravel unsupervised learning techniques, and demystify the deployment of machine learning models while adhering to best practices.
Module 5: Dynamic Data Visualisation
Data Visualisation Principles: Internalise the importance of data visualisation, create visualisations aligned with effective design principles, and select appropriate chart types.
Interactive Data Visualisations: Infuse interactivity into visualisations for enhanced engagement, and master dynamic dashboard creation for comprehensive data exploration.
Storytelling with Data: Elevate data communication through narrative-driven visualisations, employ storytelling techniques, and strategically present dynamic visualisations to stakeholders.
Meet our Leader
Dr. Murat Kristal: Program Director, MBA in Technology Leadership; Associate Professor of Operations Management and Information Systems; Special Advisor, AI & Business Analytics, the Schulich School of Business.
Dr. Murat Kristal is an Associate Professor of Operations Management at the Schulich School of Business at York University in Toronto, Canada. He received his Ph.D. from the University of North Carolina at Chapel Hill in 2005. Dr. Kristal is the founding director of Master of Business Analytics and Master of Management in Artificial Intelligence Programs at Schulich.
His expertise in innovation, ideation and research is evident through his distinct ability to connect business problems with analytical and AI solutions.
Dr. Kristal has published in top Operations Management Journals throughout his career, and he works with various companies in North America and Europe to help them achieve their analytics, AI, and digital transformation goals.
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