About AI & Data Scientist
AI and data science is the craft of turning raw data into decisions, predictions, and
intelligent products, and this course walks through it in the order most practitioners
actually learn it. It starts with Math & Statistics Foundations,
then Python for Data Science and
Version Control & Notebooks so you can write, track, and share
real analysis.
Next comes SQL & Data Collection: querying databases, calling
APIs, and gathering data from the web. From there,
Data Cleaning & Exploratory Analysis covers wrangling messy data
and finding patterns, while Data Visualization & Storytelling
covers presenting results so people act on them.
Machine Learning Fundamentals covers supervised and unsupervised
models, evaluation, and avoiding overfitting, and Deep Learning
introduces neural networks for images, sequences, and beyond.
Finally, NLP, LLMs & Generative AI covers embeddings, prompting,
fine-tuning, and retrieval-augmented generation, and
MLOps & Deployment covers serving, monitoring, and governing
models in production: the skills that separate a notebook experiment from a system
people can rely on.
Content
10 modules
01
Math & Statistics Foundations
The maths behind every model: descriptive statistics, probability, hypothesis testing, linear algebra, and calculus essentials.
ProbabilityLinear AlgebraHypothesis Testing
02
Python for Data Science
Writing clean Python for data work, then using NumPy and pandas to load, reshape, and analyse datasets.
PythonNumPypandas
03
Version Control & Notebooks
Tracking experiments with Git and GitHub, and working in Jupyter notebooks and virtual environments.
GitJupyterVirtual Environments
04
SQL & Data Collection
Querying relational databases with SQL, pulling data from APIs, and scraping the web responsibly.
SQLREST APIsWeb Scraping
05
Data Cleaning & Exploratory Analysis
Handling missing values, outliers, and messy formats, then exploring distributions and relationships before modelling.
Missing DataFeature EngineeringEDA
06
Data Visualization & Storytelling
Turning analysis into clear charts and dashboards, and communicating findings to non-technical audiences.
MatplotlibSeabornDashboards
07
Machine Learning Fundamentals
Supervised and unsupervised learning, model evaluation, cross-validation, and avoiding overfitting.
scikit-learnRegressionClustering
08
Deep Learning
Building and training neural networks, including CNNs for images and sequence models, with PyTorch or TensorFlow.
PyTorchCNNsTransfer Learning
09
NLP, LLMs & Generative AI
Working with text and large language models: embeddings, prompting, fine-tuning, and retrieval-augmented generation.
TransformersEmbeddingsRAG
10
MLOps & Deployment
Shipping models to production: serving them behind APIs, containerizing, monitoring drift, and responsible AI practices.
DockerModel ServingMonitoring
What You Can Do After This Course
By the end of the curriculum you'll have worked through 5 hands-on projects and
covered the core of what a junior to mid-level data science or AI role expects. Concretely,
you'll be able to:
Reason with data: apply probability and statistics to design sound analyses and interpret results honestly.
Wrangle and query data: write Python and SQL to collect, clean, and reshape data from files, databases, and APIs.
Explore and communicate: run exploratory analysis and present findings through clear visualizations and dashboards.
Build predictive models: train, tune, and evaluate machine learning models with scikit-learn and proper validation.
Work with neural networks: build deep learning models in PyTorch or TensorFlow and reuse pretrained ones through transfer learning.
Use LLMs and generative AI: work with embeddings, prompts, fine-tuning, and RAG to build language-powered features.
Ship and monitor models: containerize and serve models, track drift in production, and apply responsible AI practices.