CRISP-ML(Q)-Data Pre-processing Using Python(2025)

Gain a strong foundation in data science project management with this comprehensive program! Learn a structured approach to data science projects, essential for aspiring data scientists. This course emphasizes the crucial role of understanding the business problem and aligning project objectives with clear success criteria. We'll explore success metrics encompassing business impact, machine learning performance, and economic value.

You'll discover the importance of the Project Charter – the foundational document for any successful data science initiative. Understand different data types and the four measures of data, alongside effective data collection mechanisms to acquire the right data for analysis.

Delve into primary data collection techniques, including detailed explanations of surveys and experiments. Master Exploratory Data Analysis (EDA), also known as Descriptive Analytics. We'll focus on the four moments of business, utilizing compelling graphical representations like univariate, bivariate, and multivariate plots. Learn to interpret and create effective box plots, histograms, scatter plots, and Q-Q plots to uncover valuable insights.

A core focus is on data preprocessing techniques using Python. This critical step ensures your data is optimally prepared for model building. We'll cover essential techniques such as outlier analysis, imputation techniques, and scaling techniques, applying them to practical, real-world datasets. This program equips you with the skills to effectively manage data and build robust data science solutions.