Introduction to machine learning algorithms for predictive modeling
Supervised learning techniques such as linear regression, logistic regression, and decision trees
Model evaluation and selection for predictive analytics
Understanding unsupervised learning algorithms for discovering patterns in data
Exploring clustering techniques including k-means, hierarchical clustering, and DBSCAN
Applying unsupervised learning for customer segmentation and market analysis
Introduction to NLP and its applications in text data analysis
Techniques for text preprocessing, sentiment analysis, and text classification
Applying NLP to analyze customer reviews, social media data, and textual data sources
Understanding recommendation systems and their significance in business analytics
Collaborative filtering and content-based recommendation techniques
Building personalized recommendation systems for product recommendations and content suggestions
Advanced time series forecasting methods such as ARIMA, SARIMA, and Prophet
Handling seasonality, trends, and forecasting future values
Applications of time series forecasting in demand planning and financial analysis
Advanced data visualization techniques using tools like Tableau or Power BI
Creating interactive dashboards for exploratory analysis and datadriven insights
Designing visually appealing and interactive visualizations for effective communication
Introduction to big data analytics and its challenges
Technologies and frameworks for processing and analyzing large-scale datasets (e.g., Hadoop, Spark)
Applying big data analytics techniques for handling massive datasets and extracting insights
Understanding network analysis concepts and their applications
Analyzing network data, identifying key influencers, and detecting communities
Leveraging social network analytics for marketing, customer relationship management, and fraud detection
