Data Science

1: Introduction to Data Science

Overview of data science and its applications
Introduction to the data science workflow
Ethical considerations in data science

2: Data Acquisition and Data Wrangling

Data sources and formats
Collecting data from APIs and web scraping
Data cleaning and preprocessing
Dealing with missing values and outliers

3: Exploratory Data Analysis (EDA)

Descriptive statistics and data visualisation
Summarising and visualising data distributions
Detecting patterns and relationships in data

4: Statistical Analysis and Hypothesis Testing

Introduction to statistical concepts and methods
Sampling and statistical inference
Hypothesis testing and p-values
Performing t-tests and chi-square tests

5: Supervised Learning - Regression

Introduction to supervised learning
Linear regression and multiple regression
Model evaluation and interpretation
Feature selection and regularisation techniques

6: Supervised Learning - Classification

Logistic regression
Decision trees and random forests
Evaluating classification models
Handling imbalanced datasets

7: Unsupervised Learning

Clustering algorithms (K-means, hierarchical clustering)
Dimensionality reduction techniques (PCA)
Association rule mining (Apriori algorithm)

8: Natural Language Processing (NLP)

Text preprocessing and tokenization
Text classification and sentiment analysis
Topic modeling with LDA

9: Introduction to Deep Learning

Neural networks and deep learning basics
Building and training deep learning models
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)

10: Data Visualization and Communication

Principles of effective data visualisation
Data visualisation libraries (Matplotlib, Seaborn)
Creating interactive visualisations (Plotly, Tableau)
Communicating data insights effectively

Courses

Note: This syllabus provides a general outline for a Data Science course. The instructor can further customise it based on the students' needs and the course objectives. Additional topics like time series analysis, advanced machine learning algorithms, big data processing with tools like Spark, or deploying machine learning models can be included if desired.