Data Wrangling - Learn to manipulate and clean data using libraries like Pandas (Python) or dplyr (R) to prepare datasets for analysis.
Feature Engineering - Develop techniques to create new features that enhance machine learning model performance, crucial for improving predictive accuracy.
Machine Learning Algorithms - Master algorithms such as regression, classification, clustering, and neural networks to build robust predictive models.
Data Visualization - Use tools like Matplotlib, Seaborn, and Plotly (Python) or ggplot2 (R) to create insightful visualizations that communicate data findings effectively.
Natural Language Processing (NLP) - Learn to process and analyze textual data using techniques like tokenization, sentiment analysis, and language modeling to extract meaningful insights.