Applied statistics - You will use hypothesis testing and probability distributions on real datasets, so you can evaluate analytical results on your own and make more informed business decisions.
Python for data work - You will learn Python in a Data Science setting and work confidently with NumPy and Pandas, which will help you clean, transform, and analyze data much more efficiently.
Effective data exploration - You will run EDA from data cleaning to visualization in Matplotlib, Seaborn, and Plotly, making it easier for you to uncover relationships, anomalies, and useful patterns.
Stronger data preparation - You will practice normalization, standardization, categorical encoding, and train test splitting, so you can prepare datasets correctly and make them ready for reliable modeling.
First ML models - You will build and run your first supervised models, including linear and logistic regression, so you understand when to choose a given algorithm and how to read its results.
Reliable model evaluation - You will learn to choose metrics such as MAE, RMSE, precision, and recall, and apply cross validation with grid search to compare models and reduce overfitting with confidence.
Advanced algorithms - You will work with decision trees, random forests, SVMs, neural networks, and unsupervised methods like k-means, PCA, and Apriori, expanding your practical analytics toolkit.
ML pipeline and deployment - You will see how to connect analysis, modeling, and deployment with cloud tools, Docker, Kubernetes, and MLflow, helping you contribute to end to end ML pipeline projects.
Basic computer skills - You should be comfortable using a computer, installing software, managing files and folders, and working in a browser so you can complete the hands on exercises smoothly.
Elementary math knowledge - You should understand algebraic operations, percentages, averages, and basic charts, because these ideas will be needed when you work with statistics and data analysis.
Basic technical English - You should be able to read simple function names, system messages, and documentation in English, since the libraries, tools, and environments used in the course rely on it.
Readiness for practical work - You should be ready to complete exercises independently and analyze data in practice, because the course includes projects, notebooks, and building your own models.
