Data Science resources

Covers the fundamental concepts of data science, its lifecycle, and its applications.

Explores techniques for data collection, cleaning, and preprocessing for analysis.

Introduces EDA techniques to understand data patterns, distributions, and relationships.

Covers data visualization techniques and tools to present insights effectively.

Explores essential statistical and probability concepts used in data science.

Covers supervised, unsupervised, and reinforcement learning techniques in data science.

Explores techniques for data manipulation and wrangling using popular libraries.

Introduces big data technologies and distributed data processing tools.

Covers the process of data modeling and feature selection for building effective models.

Explores Python libraries and tools used in data science.

Covers NLP concepts, text processing, and sentiment analysis for data science applications.

Explores time series analysis techniques and forecasting models in data science.

Covers neural networks, deep learning models, and applications in data science.

Explores AI techniques and automation in data science pipelines.