Course Material: IBM Data Science
Course Material: IBM Data Science
Course Outline
| Module | Topic | Subtopics |
|---|---|---|
| Module 1 | Introduction to Data Science | – What is Data Science? – The Data Science Process – Applications of Data Science – Key Skills for Data Scientists |
| Module 2 | Data Science Tools | – Python for Data Science – Jupyter Notebooks – IBM Watson Studio – R for Data Science |
| Module 3 | Data Collection and Cleaning | – Data Sources (CSV, Excel, APIs) – Handling Missing Data – Data Transformation and Feature Engineering |
| Module 4 | Exploratory Data Analysis (EDA) | – Understanding Data Distribution – Visualizing Data with Matplotlib and Seaborn – Descriptive Statistics |
| Module 5 | Data Visualization | – Types of Visualizations (Bar, Line, Scatter, etc.) – Creating Dashboards with Power BI and Tableau – Best Practices |
| Module 6 | Statistical Methods for Data Science | – Probability and Statistics – Hypothesis Testing – Regression Analysis – Sampling Methods |
| Module 7 | Introduction to Machine Learning | – Supervised vs. Unsupervised Learning – Key Machine Learning Algorithms – Training and Testing Data Sets |
| Module 8 | Supervised Learning Algorithms | – Linear and Logistic Regression – Decision Trees and Random Forests – K-Nearest Neighbors – Support Vector Machines |
| Module 9 | Unsupervised Learning Algorithms | – Clustering Techniques (K-Means, Hierarchical) – Principal Component Analysis (PCA) – Association Rules |
| Module 10 | Deep Learning and Neural Networks | – Neural Network Architecture – Activation Functions – Convolutional Neural Networks (CNNs) – Recurrent Neural Networks (RNNs) |
| Module 11 | Natural Language Processing (NLP) | – Text Preprocessing (Tokenization, Lemmatization) – Sentiment Analysis – Named Entity Recognition (NER) |
| Module 12 | Model Evaluation and Validation | – Cross-Validation Techniques – Performance Metrics (Accuracy, Precision, Recall, F1-Score) – Overfitting and Underfitting |
| Module 13 | Data Science Workflow with IBM Watson | – Using IBM Watson Studio – Data Preprocessing with Watson – Building and Deploying Models with Watson – Visualizing Results |
| Module 14 | Capstone Project and Real-World Applications | – Designing a Data Science Project – End-to-End Project with Data Science Tools – Deploying a Model into Production |
| Module 15 | Ethics and Privacy in Data Science | – Ethical Considerations in Data Science – Data Privacy and Security Concerns – Bias in Machine Learning Models |
Elaboration on the Most Important Topics
Module 1: Introduction to Data Science
Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights from structured and unstructured data. In this module, students will get an overview of the data science field, its process, and applications across various industries.
Key Topics:
-
What is Data Science?: Data science combines aspects of statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists collect, process, and analyze large amounts of data to help organizations make data-driven decisions.
OR