Understanding the role of data science in today's world. Exploring the data science workflow and methodologies. Introducing key data science tools and languages.
Introduction to Data Science
Collecting data from various sources, including APIs and databases. Cleaning and transforming raw data for analysis. Handling missing data and outliers effectively.
Data Acquisition and Preprocessing
Performing comprehensive exploratory data analysis. Visualizing data with libraries like Matplotlib and Seaborn. Extracting insights and patterns from data.
Data Analysis and Statistics
Creating informative and interactive data visualizations. Building reports and dashboards for data-driven storytelling. Communicating data insights effectively.
Machine Learning Fundamentals
Introduction to machine learning concepts and algorithms. Supervised learning, unsupervised learning, and reinforcement learning. Model selection, training, and evaluation.
Advanced Machine Learning
Statistical analysis for data interpretation.
Hypothesis testing and confidence intervals.
Predictive modeling and regression analysis.
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