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Data Science Advance
Explore and master the fundamentals of Data Science Advance
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.Chapter 1
Introduction to Advanced Data Science
Chapter 2
Data Wrangling and Feature Engineering
Chapter 3
Advanced Data Visualization Techniques
Advanced data visualization encompasses techniques that extend beyond basic graphical representations, enhancing insights into high-dimensional data and facilitating decision-making. The chapter details various methods including multivariate plots, dimensionality reduction, and interactive visualization tools, underscoring the importance of clarity, accuracy, and efficiency in visual design. Mastering these advanced techniques equips data scientists to effectively communicate complex findings and narratives through data.
Chapter 4
Statistical Inference and Hypothesis Testing
Chapter 5
Supervised Learning – Advanced Algorithms
Chapter 6
Unsupervised Learning – Clustering & Dimensionality Reduction
Chapter 7
Ensemble Methods – Bagging, Boosting, and Stacking
Ensemble methods collectively enhance predictive performance by leveraging the outputs of multiple models. Techniques like Bagging, Boosting, and Stacking each offer distinct strategies to improve model accuracy and increase stability, particularly for complex datasets. Understanding the strengths and weaknesses of these ensemble approaches is crucial for applying them effectively in various domains.
Chapter 8
Deep Learning and Neural Networks
Deep learning has significantly advanced the capabilities of machine learning by mimicking the brain's neural structure through artificial neural networks (ANNs), particularly deep neural networks (DNNs). By utilizing various architectures such as CNNs, RNNs, and GANs, deep learning enables remarkable performance in tasks ranging from image processing to natural language understanding. However, challenges such as overfitting, explainability, and computational demands require careful consideration for ethical and effective application.
Chapter 9
Natural Language Processing (NLP)
Chapter 10
Time Series Analysis and Forecasting
Chapter 11
Recommender Systems
Chapter 12
Model Evaluation and Validation
Chapter 13
Big Data Technologies (Hadoop, Spark)
Chapter 14
Machine Learning Pipelines and Automation
Chapter 15
Cloud Computing in Data Science (AWS,Azure, GCP)
Chapter 16
Ethics and Responsible AI
Chapter 17
Case Studies and Real-World Projects
Chapter 18
Data Science for Business and Decision- Making
Chapter 19
Advanced SQL and NoSQL for Data Science
Chapter 20