About Data Science
Data science is the practice of turning raw data into decisions, and this course walks
through it in the order most practitioners learn it. It starts with
Math & Statistics and Python for Data Science,
then Version Control & Notebooks so your work is tracked and
reproducible.
Next comes SQL & Data Sources for getting data out of databases
and APIs, followed by Data Wrangling & Cleaning with Pandas and
Visualization & EDA for understanding what the data is actually
telling you.
Finally, Machine Learning Fundamentals and
Feature Engineering & Evaluation cover building and honestly
testing models, Deep Learning & NLP introduces neural networks and
transformers, and Deployment & MLOps covers getting a model into
production and keeping it healthy: the skills that separate a notebook experiment from
a working data product.
Content
10 modules
01
Math & Statistics
Probability, distributions, hypothesis testing, and the linear algebra and calculus that models are built on.
ProbabilityHypothesis TestingLinear Algebra
02
Python for Data Science
Core Python plus the scientific stack, writing clean, reusable code for analysis.
PythonNumPyFunctions & OOP
03
Version Control & Notebooks
Tracking work with Git and GitHub, and building reproducible analyses in Jupyter with managed environments.
GitJupyterConda / venv
04
SQL & Data Sources
Querying relational databases with joins and window functions, and pulling data from APIs, files, and the web.
SQLWindow FunctionsAPIs
05
Data Wrangling & Cleaning
Turning messy real-world data into analysis-ready tables with Pandas: missing values, outliers, merging, and reshaping.
PandasMissing DataData Quality
06
Visualization & EDA
Exploring datasets, spotting patterns, and communicating findings with clear charts and dashboards.
MatplotlibSeabornPlotly
07
Machine Learning Fundamentals
Supervised and unsupervised learning with scikit-learn: regression, classification, trees, ensembles, and clustering.
scikit-learnRegressionRandom Forest
08
Feature Engineering & Evaluation
Building better inputs and judging models honestly with cross-validation, metrics, and hyperparameter tuning.
Cross-ValidationPrecision / RecallTuning
09
Deep Learning & NLP
Neural networks in PyTorch or TensorFlow, then working with text through embeddings and transformer models.
PyTorchNeural NetworksTransformers
10
Deployment & MLOps
Serving models behind an API, packaging with Docker, and monitoring them for drift once they are live.
DockerFastAPIModel Monitoring
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 role expects. Concretely,
you'll be able to:
Reason with data: apply probability and statistics to design analyses and judge whether a result is signal or noise.
Write analysis code: work fluently in Python, versioned with Git and documented in reproducible notebooks.
Get and clean data: query databases with SQL, pull from APIs, and wrangle messy tables with Pandas.
Tell the story: explore datasets and present findings through clear visualizations.
Build predictive models: train, tune, and compare regression, classification, and clustering models with scikit-learn.
Evaluate honestly: engineer features, use cross-validation, and pick metrics that match the real problem.
Ship and maintain: go deeper with neural networks and NLP, then deploy models behind an API and monitor them in production.