Data Science Foundation Certification (DSF - DS1016) 

Introduction to data science and its applications

Key roles and responsibilities of a data scientist

Ethical considerations in data science

Data cleaning and handling missing values

Data transformation and feature engineering

Data aggregation and summarization

Descriptive statistics and exploratory data analysis

Probability distributions and hypothesis testing

Correlation and regression analysis

Introduction to supervised and unsupervised learning

Linear and logistic regression

Decision trees and random forests

Model evaluation metrics and techniques

Cross-validation and hyperparameter tuning

Principles of effective data visualization

Visualization libraries in Python (e.g., Matplotlib, Seaborn)

Effective communication of data-driven insights

Tailoring the message to different audiences