Predictive Analytics with R and Python provides a comprehensive exploration of statistical methods and machine learning techniques essential for data-driven decision-making. The course emphasizes hands-on projects, enabling participants to apply theoretical concepts to real-world scenarios, enhancing their analytical skills and practical knowledge. Participants will engage in collaborative learning, culminating in a final project that showcases their ability to derive insights from complex datasets.
The curriculum is structured to cover a diverse range of topics, ensuring a robust understanding of predictive modeling. By the end of the program, learners will have the expertise to utilize R and Python for data analysis, model building, and result interpretation. This course not only prepares individuals for immediate application in the workforce but also encourages contributions to Cademix Magazine, fostering a culture of knowledge sharing and professional development.
Introduction to Predictive Analytics: Concepts and Applications
Data Preprocessing Techniques in R and Python
Exploratory Data Analysis (EDA) with Visualization Tools
Regression Analysis: Linear and Non-Linear Models
Classification Techniques: Decision Trees, Random Forests, and SVM
Time Series Forecasting Methods
Model Evaluation Metrics and Validation Techniques
Feature Engineering and Selection Strategies
Implementing Machine Learning Algorithms in R and Python
Final Project: Building a Predictive Model on a Real Dataset