Comprehensive Data Science and AI Course Development
Bestine Okinda
DSAI-course
Setting Up Your Virtual Environment
A virtual environment helps isolate dependencies and manage Python packages efficiently. Follow the steps below based on your operating system:
1. Install Python and pip
Ensure you have Python installed on your system. If you haven't installed it yet:
Windows & macOS: Download and install Python from python.org.
Linux (Debian-based):
After installation, verify python and pip versions
2. Create a virtual environment
Once Python and pip are installed, install
virtualenv by running:Confirm the installation
Choose a name for your environment (e.g.,
DSAI_env) and run:For a specific Python version:
There are different ways to activate virtual environment;
Windows (Command Prompt):
Windows (PowerShell):
macOS & Linux:
Once activated, you will see the environment name in your terminal prompt.
To exit the virtual environment, run:
Now you're ready to install packages and work within an isolated environment for your Data Science and AI projects! 🚀
Module 1 - DSAI 100:Introduction to data science
Day 1: Foundations of Data Science
Introduction to data science concepts, terminology, and applications.
Understanding data types, data sources, and data formats.
Data collection methods and data ethics.
Day 2, 3: Data Acquisition and Preprocessing
Techniques for data acquisition from various sources such as APIs, databases, and web scraping.
Data cleaning and preprocessing methods for handling missing values, outliers, and inconsistencies.
Introduction to data wrangling tools like pandas-profiling and data augmentation techniques.
Day 4, 5: Exploratory Data Analysis (EDA)
Advanced data visualization techniques using libraries like Plotly, Bokeh, and Altair.
Statistical methods for exploratory data analysis, hypothesis testing, and feature importance.
Time series analysis and spatial data visualization.
Day 6, 7, 8: Statistical Modeling and Inference
Introduction to statistical modeling techniques such as linear regression, logistic regression, and time series forecasting.
Model evaluation metrics and validation techniques.
Bayesian statistics and probabilistic programming with libraries like PyMC3.
Day 9, 10: Applied Machine Learning
Overview of supervised and unsupervised learning algorithms.
Ensemble methods: bagging, boosting, and stacking.
Introduction to dimensionality reduction techniques such as PCA and t-SNE.
Hands-on project: Applying machine learning algorithms to real-world datasets, from data preprocessing to model evaluation
Module 2: DSAI 101:Advanced Data Science Techniques
Day 1: Advanced Feature Engineering
Feature engineering for structured and unstructured data, including text and image data.
Feature extraction from time series data and geospatial data.
Automated feature engineering with libraries like FeatureTools and auto-sklearn.
Day 2, 3: Model Optimization and Hyperparameter Tuning
Techniques for model selection, hyperparameter tuning, and model interpretation.Curricula Modules Topics -Grid search, random search, and Bayesian optimization for hyperparameter tuning.
Model interpretability techniques such as SHAP values and LIME.
Day 4, 5: Time Series Analysis and Forecasting
Advanced time series analysis techniques, including ARIMA, SARIMA, and Prophet.
Forecasting methods for hierarchical time series and multivariate time series.
Anomaly detection and intervention analysis in time series data.
Day 6, 7, 8: Unsupervised Learning and Clustering
Clustering algorithms: k-means, hierarchical clustering, DBSCAN.
Dimensionality reduction techniques: t-SNE, UMAP, autoencoders.
Applications of unsupervised learning in anomaly detection, customer segmentation, and recommender systems.
Day 9, 10: Big Data and Scalable Machine Learning
Introduction to distributed computing frameworks: Apache Spark and Dask.
Scalable machine learning algorithms for big data analysis.
Hands-on project: Implementing scalable machine learning pipelines on large-scale datasets using distributed computing
Module 3: DSAI 102:Artificial Intelligence and Deep Learning frameworks.
Day 1: Introduction to Artificial Intelligence and Neural Networks
Fundamentals of artificial intelligence, neural networks, and deep learning.
Activation functions, loss functions, and optimization algorithms.
Introduction to deep learning frameworks: TensorFlow and PyTorch.
Day 2, 3: Convolutional Neural Networks (CNNs)
Understanding CNN architecture, convolutional layers, pooling layers, and fully connected layers.
Transfer learning and fine-tuning pre-trained CNN models.
Advanced CNN architectures: ResNet, Inception, and DenseNet.
Day 4, 5: Recurrent Neural Networks (RNNs) and Sequence Models
Introduction to RNN architecture, long short-term memory(LSTM), and gated recurrent units (GRU).
Sequence-to-sequence models for machine translation and speech recognition.
Attention mechanisms and transformer architectures.
Day 6, 7, 8: Generative Models and Reinforcement Learning
Introduction to generative models: variational autoencoders
Reinforcement learning fundamentals: Markov decision processes, policy gradients, and value iteration.
Deep reinforcement learning algorithms: Deep Q-Networks (DQN), policy gradients (PG), and actor-critic methods.
Day 9, 10: Advanced Deep Learning Applications and Project
Applications of deep learning in computer vision, natural language processing, and reinforcement learning.
Cutting-edge research topics: self-supervised learning, meta-learning, and multi-modal learning.
Capstone project: Students choose a project related to their interests or career goals, applying advanced deep learning techniques to solve a specific problem or explore a research question. They present their project outcomes and findings to the class. (VAEs) and generative adversarial networks (GANs).
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Posted Sep 8, 2025
Developed a comprehensive data science and AI course.
