Python for data analysis - You will learn to work confidently with lists, dictionaries, and NumPy arrays, so you can prepare data faster for analysis, reporting, and machine learning tasks used in everyday work.
Practical business scripts - You will see how to build functions and classes that support ML projects, then use them to automate sales data analysis and streamline repetitive tasks in a practical business context.
Choosing the right algorithm - You will understand when to use supervised, unsupervised, or other learning approaches, so you can match methods more accurately to customer behavior prediction and business goals.
Hands-on scikit-learn - You will practice building models with scikit-learn from data preparation through training and testing, so you can run a complete machine learning workflow in Python on your own.
Core data science libraries - You will gain practical skill in NumPy, Pandas, Matplotlib, and SciPy, making it easier to combine data wrangling, statistical analysis, and visualization in one workflow.
Model quality evaluation - You will learn model evaluation methods and testing good practices, so you can tell whether a model truly performs well instead of relying on results that only look convincing.
Regression and tree models - You will learn how to use linear and polynomial regression as well as decision trees and Random Forest to predict numeric values and compare the effectiveness of different model types.
Clear analysis and visuals - You will create charts and examine data correlations, helping you understand patterns in datasets and present model outcomes in a way that is clear for both your team and business users.
Basic computer skills - You should be comfortable using a computer, installing software, managing files, and launching applications so you can complete the hands-on exercises without difficulty.
Basic logic and math - You should understand simple algebra, the idea of a variable, percentages, and relationships between values so you can analyze data more easily and interpret model results.
Experience with data tables - You should have basic experience working with data tables, for example in Excel or a similar tool, so you can understand records, columns, and simple data operations quickly.
Readiness to write code - You should be ready to write and revise simple code on your own, because the training includes practical exercises and active work with examples in Python.
