Advanced Analytics Techniques
( 24 Modules )

Module #1
Introduction to Advanced Analytics
Overview of advanced analytics, its applications, and importance in business decision-making

Module #2
Data Preparation for Advanced Analytics
Data preprocessing, data cleaning, and data transformation techniques for advanced analytics

Module #3
Regression Analysis
Simple and multiple linear regression, non-linear regression, and regression diagnostics

Module #4
Time Series Analysis
Time series components, trend analysis, seasonality, and forecasting models

Module #5
ARIMA Modeling
AutoRegressive Integrated Moving Average (ARIMA) modeling for time series forecasting

Module #6
Exponential Smoothing
Exponential smoothing techniques for time series forecasting, including SES, DES, and TES

Module #7
Machine Learning Fundamentals
Introduction to machine learning, supervised and unsupervised learning, and model evaluation metrics

Module #8
Decision Trees and Random Forest
Decision tree algorithm, decision tree optimization, and random forest ensemble learning

Module #9
Support Vector Machines
Support vector machine algorithm, kernel functions, and SVM optimization techniques

Module #10
Clustering Analysis
K-means clustering, hierarchical clustering, and density-based clustering algorithms

Module #11
Principal Component Analysis
Dimensionality reduction using principal component analysis

Module #12
Text Analytics
Text preprocessing, text representation, and text classification techniques

Module #13
Natural Language Processing
Introduction to NLP, tokenization, stemming, and named entity recognition

Module #14
Deep Learning Fundamentals
Introduction to deep learning, neural networks, and deep learning architecture

Module #15
Convolutional Neural Networks
Convolutional neural networks for image recognition and object detection

Module #16
Recurrent Neural Networks
Recurrent neural networks for sequential data and time series forecasting

Module #17
Gradient Boosting Machines
Gradient boosting machines for regression and classification problems

Module #18
Ensemble Methods
Ensemble learning techniques, including bagging, boosting, and stacking

Module #19
Feature Engineering
Feature selection, feature extraction, and feature creation techniques

Module #20
Model Selection and Hyperparameter Tuning
Model selection, hyperparameter tuning, and cross-validation techniques

Module #21
Model Deployment and Monitoring
Model deployment, model monitoring, and model maintenance strategies

Module #22
Big Data Analytics
Big data analytics using Hadoop, Spark, and NoSQL databases

Module #23
Real-World Applications of Advanced Analytics
Case studies and applications of advanced analytics in various industries

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Advanced Analytics Techniques career

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Advanced Analytics Techniques ( 24 Modules )

Advanced Analytics Techniques
( 24 Modules )