Data Science Online

Professional Certification

Unlock the transformative potential of data and AI in addressing real-world challenges, to enable more informed and impactful decision-making.

In an era where data fuels innovation and decision-making across sectors, this advanced Masterclass provides an immersive exploration of data analysis, manipulation, modeling, visualization, and AI technologies, with a focus on real-world use cases.

Discover techniques to dissect and interpret datasets of varying complexities, while applying these skills to real business problems.

Masterclass Expert: Professor Murat Kristal

Dates: 14 - 18 October 2024

Location: Live, Online

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.