Course Curriculum
Detailed breakdown of topics and learning materials
What is Data Science, why it matters, real-world applications in business, healthcare, finance, and environment. Data Science for predictions and business decision-making. Extracting knowledge from structured and unstructured data using statistics, machine learning, and data mining.
Introduction to Data Science
What is Data Science, why it is important, real-world applications in business, healthcare, finance, and environmental conservation. Data Science for predictions and business decision-making. Extracting knowledge from structured and unstructured data.
The 7 stages: Business Understanding, Data Understanding, Data Preparation (handling missing values, removing outliers, feature creation), Exploratory Data Analysis (scatter plots, heatmaps), Model Selection, Model Evaluation, and Model Deployment. Includes House Price Prediction case study.
Data Science Life Cycle
The 7-stage Data Science lifecycle: Business Understanding, Data Understanding, Data Preparation, Exploratory Data Analysis, Model Selection, Model Evaluation, and Model Deployment. Includes a House Price Prediction case study walking through the full pipeline.
Relationship between AI, ML, DL, and Data Science. AI as the umbrella field, ML as a subset, DL as a subset of ML. How ML fits into Data Science. Real-life ML applications: Amazon, Netflix, YouTube, Instagram.
AI, ML, DL and Data Science
Relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Data Science. AI as the umbrella field, ML as a subset, DL as a subset of ML. Real-life examples from Amazon, Netflix, YouTube, Instagram. Roles and responsibilities of a Data Scientist.
The 7 key responsibilities: Data Acquisition, Data Cleaning, Exploratory Data Analysis, Model Development, Model Evaluation, Deployment, and Communication of results.
No learning materials available for this topic yet.
What is an Algorithm, what is a Model, Training Data vs Testing Data, Model Building Process. Learning types: Supervised Learning and Unsupervised Learning.
Machine Learning Fundamentals
What is an Algorithm vs a Model. Training Data vs Testing Data. The model building process. Types of Machine Learning: Supervised Learning (classification, regression) and Unsupervised Learning (clustering, association). Key supervised learning algorithms: SVM, Decision Tree, Random Forest, SVR.
Definition and types of supervised learning: Classification and Regression. Algorithms covered: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Tree, Random Forest, Support Vector Regression (SVR).
No learning materials available for this topic yet.
Definition, regression terminologies (Cost Function, Error Minimization, Prediction Equation), Mean Squared Error (MSE). Real-life example: House Price Prediction. Use cases: sales prediction, risk prediction, price estimation.
Linear Regression & Classification
Linear Regression: cost function, error minimization, MSE, and house price prediction example. Use cases in sales, risk, and price estimation. Types of regression. Classification algorithms: Naive Bayes, Decision Tree. Exercise: California House Price Prediction Model.
What is Classification, types of classification, Naive Bayes Algorithm, Decision Tree Classification. Applications: spam filtering, sentiment analysis, category prediction.
No learning materials available for this topic yet.
What is Unsupervised Learning, clustering concepts, types of clustering, association analysis. Algorithms: K-Means Clustering, Hierarchical Clustering.
Unsupervised Learning & K-Means Clustering
Unsupervised learning concepts: clustering and association. K-Means Clustering: how it works, cluster formation steps, choosing optimal k with the Elbow Method. Use cases: customer segmentation, market research, anomaly detection. Exercises: Decision Tree and K-Means models.
How K-Means works, cluster formation, choosing the number of clusters (k) using the Elbow Method, distance-based grouping. Use cases: customer segmentation, market research, image compression, anomaly detection.
No learning materials available for this topic yet.
Hands-on projects: California House Price Prediction Model, Decision Tree Model Building, K-Means Clustering Model. Exercises reinforce concepts from each module.
Advanced Data Science - Review Session
Comprehensive review covering all 11 modules: Introduction to Data Science, Life Cycle, AI/ML/DL fundamentals, Data Scientist roles, ML basics, Supervised Learning, Linear Regression, Classification, Unsupervised Learning, K-Means Clustering, and practical exercises.
Meet Your Instructors
Learn from industry experts and experienced professionals
Mireille Jabbour
Primary Instructor
Artificial Intelligence
As a PhD candidate in Information and Communication Sciences, I am committed to innovating in Artificial Intelligence with a focus on Computer Vision. My research explores elderly...