Overview

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In this course, you'll gain essential skills to transform raw data into actionable insights, covering the full data science lifecycle, from preparation to advanced machine learning techniques. By focusing on modern models and ethical considerations, you'll be prepared to make informed data-driven decisions in real-world scenarios. This course emphasizes hands-on learning with practical examples and real-world applications to enhance your understanding of data science. You'll learn how to apply machine learning techniques to real-life problems and refine your coding and statistical skills. What makes this course unique is its balance of theory and practice, combining foundational concepts with modern advancements in data science, including ethical issues related to AI. You'll work on actionable case studies that allow you to immediately apply what you learn. This course is perfect for aspiring data scientists who have basic programming or math skills. It is ideal for beginners looking to build a strong foundation in data science. Prior knowledge of Python will be helpful but not necessary.

Syllabus

Data Science Terminology

In this section, we define core data science terminology, explain the three domains of data science, and introduce basic Python syntax for data tasks.

Types of Data

In this section, we explore structured versus unstructured data, quantitative versus qualitative data, and the four levels of data for effective analysis and modeling.

The Five Steps of Data Science

In this section, we explore the five steps of data science, focusing on problem definition, data preprocessing with pandas, and effective data visualization and communication.

Basic Mathematics

In this section, we explore fundamental mathematical concepts including symbols, logarithms, set theory, and matrix operations, essential for data science modeling and analysis.

Impossible or Improbable A Gentle Introduction to Probability

In this section, we explore probability's core principles, compare frequentist and Bayesian approaches, and apply probability rules to model uncertain real-world events.

Advanced Probability

In this section, we examine advanced probability concepts like Bayes' theorem and random variables, focusing on their application in predictive modeling and decision-making processes.

What Are the Chances? An Introduction to Statistics

In this section, we explore unbiased data sampling, measures of center and variation, z-scores, and the empirical rule to analyze and interpret data effectively.

Advanced Statistics

In this section, we explore hypothesis testing, confidence intervals, and the central limit theorem to make population inferences from sample data. Key concepts include point estimates and sampling distributions for data-driven decision-making.

Communicating Data

In this section, we explore methods for communicating data effectively, focusing on identifying misleading visualizations, understanding correlation versus causation, and creating clear, insightful visuals for diverse audiences.

How to Tell if Your Toaster is Learning - Machine Learning Essentials

In this section, we explore machine learning fundamentals, including regression, classification, and model evaluation.

Predictions Don't Grow on Trees or Do They

In this section, we explore naive Bayes, decision trees, and PCA for real data analysis and prediction.

Introduction to Transfer Learning and Pre-Trained Models

In this section, we explore transfer learning and pre-trained models, focusing on their application in ML tasks. Key concepts include BERT, GPT, and adapting models for computer vision and NLP.

Mitigating Algorithmic Bias and Tackling Model and Data Drift

In this section, we explore algorithmic bias mitigation, model and data drift handling, and strategies for building fair and robust machine learning systems.

AI Governance

In this section, we explore structured approaches to data, ML, and architectural governance to drive digital transformation, ensure compliance, and unlock value through effective management and control.

Navigating Real-World Data Science Case Studies in Action

In this section, we analyze the COMPAS dataset for bias detection and implement text embeddings using OpenAI models. We focus on feature standardization, encoding, and practical data science applications.