Data Science for Predictive Modeling
( 25 Modules )

Module #1
Introduction to Data Science and Predictive Modeling
Overview of data science, predictive modeling, and its applications

Module #2
Data Science Workflow
Exploring the data science workflow:problem definition, data collection, cleaning, and preprocessing

Module #3
Types of Data
Understanding different types of data:numerical, categorical, and textual data

Module #4
Data Visualization
Introduction to data visualization:importance, types, and best practices

Module #5
Exploratory Data Analysis (EDA)
Using EDA to understand data distribution, correlation, and relationships

Module #6
Data Preprocessing
Handling missing values, outliers, and feature scaling

Module #7
Feature Engineering
Techniques for feature extraction, transformation, and selection

Module #8
Regression Analysis
Introduction to linear regression, simple and multiple regression, and model evaluation

Module #9
Regression Modeling
Building and interpreting regression models, including polynomial and logistic regression

Module #10
Decision Trees and Random Forests
Introduction to decision trees, random forests, and ensemble methods

Module #11
Classification Models
Introduction to classification models, including logistic regression and support vector machines

Module #12
Clustering Analysis
Introduction to clustering algorithms, including k-means and hierarchical clustering

Module #13
Dimensionality Reduction
Techniques for dimensionality reduction, including PCA and t-SNE

Module #14
Model Evaluation and Selection
Metrics and techniques for evaluating and selecting predictive models

Module #15
Overfitting and Underfitting
Understanding overfitting and underfitting, and techniques for regularization

Module #16
Hyperparameter Tuning
Introduction to hyperparameter tuning, including grid search and cross-validation

Module #17
Unsupervised Learning
Introduction to unsupervised learning, including anomaly detection and recommender systems

Module #18
Big Data and Scalability
Working with big data, including distributed computing and Spark

Module #19
Deep Learning for Predictive Modeling
Introduction to deep learning, including neural networks and convolutional neural networks

Module #20
Natural Language Processing (NLP) for Predictive Modeling
Introduction to NLP, including text preprocessing and sentiment analysis

Module #21
Time Series Analysis
Introduction to time series analysis, including forecasting and ARIMA models

Module #22
Ensemble Methods
Introduction to ensemble methods, including bagging, boosting, and stacking

Module #23
Predictive Modeling in Python
Hands-on practice with Python libraries, including scikit-learn and TensorFlow

Module #24
Predictive Modeling in R
Hands-on practice with R libraries, including caret and dplyr

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Data Science for Predictive Modeling career

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Data Science for Predictive Modeling ( 25 Modules )

Data Science for Predictive Modeling
( 25 Modules )