Learn Data Science from Scratch

Introduction

Unraveling the Data Science Universe: An Introduction

• Introduction
• What is data science
• Data science: A fusion of fields
• History and evolution of data science as a field
• The data science process
• A day in the life of a data scientist
• How data science is shaping our world
• Differences between Artificial Intelligence, big data, and data science
• Conclusion
• Points to remember
• Questions

Essential Python Libraries and Tools for Data Science

• Introduction
• Setting up your developer environment
• Basics of NumPy
• Pandas for data manipulation
• Matplotlib, seaborn, and Plotly for data visualization
• Jupyter Notebook essentials
• Scikit-learn: Key to streamlined Machine Learning
• Conclusion
• Points to remember
• Questions

Statistics and Probability Essentials for Data Science

• Introduction
• Probability theory
• Basic probability concepts
• Conditional probability and Bayes’ theorem
• Discrete and continuous random variables
• Expectation, variance, and covariance of random variables
• Distributions and sampling
• Central limit theorem
• Sampling techniques
• Hypothesis testing
• Conclusion
• Points to remember
• Questions

Data Mining Expedition: Web Scraping and Data Collection Techniques

• Introduction
• Sources of data
• Web scraping with Beautiful Soup and Requests
• APIs and Python libraries for data collection
• Ethical considerations during data collection
• Conclusion
• Points to remember
• Questions

Painting with Data: Exploration and Visualization

• Introduction
• Exploratory data analysis
• Descriptive statistics
• Data visualization with Matplotlib, seaborn, and Plotly
• Discovering trends and relationships
• Conclusion
• Points to remember
• Questions

Data Alchemy: Cleaning and Preprocessing Raw Data

• Introduction
• Handling missing data
• Data transformation and normalization
• Addressing duplication and data inconsistencies
• Feature engineering and selection
• Encoding categorical features
• Conclusion
• Points to remember
• Questions

Machine Learning Magic: An Introduction to Predictive Modeling

• Introduction
• Supervised and unsupervised learning
• Essential algorithms and model selection
• Training, testing, and evaluation'
• Overfitting and underfitting
• Conclusion
• Points to remember
• Questions

Exploring Regression: Linear, Logistic, and Advanced Methods

• Introduction
• Linear regression
• Logistic regression
• Harnessing regularization: Techniques to rein in your model
• Conclusion
• Points to remember
• Questions

Unveiling Patterns with k-Nearest Neighbors and Naïve Bayes

• Introduction
• Understanding the k-Nearest Neighbors algorithm
• Naïve Bayes classifier
• Hyperparameter tuning
• Conclusion
• Points to remember
• Questions

Exploring Tree-Based Models: Decision Trees to Gradient Boosting

• Introduction
• Decision trees
• Entropy and information gain
• Tree pruning and optimization
• The power of ensemble methods in machine learning
• Conclusion
• Points to remember
• Questions

Support Vector Machines: Simplifying Complexity

• Introduction
• Introduction to support vector machines
• Understanding kernel methods
• SVM for classification and regression roles
• Real-world SVM: From preprocessing to evaluation
• Balancing the bias-variance trade-off in SVM
• Conclusion
• Points to remember
• Questions

Dimensionality Reduction: From PCA to Advanced Methods

• Introduction
• Understanding the problem of high dimensionality
• Principal component analysis
• Visualizing high-dimensional data
• Exploring beyond PCA: t-SNE and UMAP
• Conclusion
• Points to remember
• Questions

Unlocking Unsupervised Learning

• Introduction
• K-means clustering
• Hierarchical clustering
• Understanding DBSCAN: A comprehensive guide
• DBSCAN and other density-based methods
• Cluster evaluation and validation
• Conclusion
• Points to remember
• Questions

The Essence of Neural Networks and Deep Learning

• Introduction
• Deep learning: Beyond conventional machine learning
• Deep learning as artificial intelligence’s game changer
• Data and processing power
• Introduction to deep learning libraries
• The intricate web of artificial neural networks
• Importance of data and feature engineering in deep learning
• Feature crafting versus self-learning
• Overfitting: A deep learning perspective
• Convolutional neural networks
• Recurrent neural networks 
• Long short-term memory networks
• Conclusion
• Points to remember
• Questions

Word Play: Text Analytics and Natural Language Processing

• Introduction
• Text processing and tokenization
• The transformation journey: From text to features
• Decoding emotions: Sentiment analysis and text classification
• Topic modeling and entity recognition
• Conclusion
• Points to remember
• Questions

Crafting Recommender Systems

• Introduction
• Introduction to collaborative filtering
• User-based collaborative filtering
• Decoding item-based collaborative filtering
• Measuring similarities in recommender systems
• Sparsity and scalability in collaborative filtering
• Building your first collaborative filtering systems in Python
• Personalized proposals: Understanding content-based filtering
• Building content based recommendations in Python
• Matrix factorization and SVD in recommender system
• Synergy in recommendation: Hybrid systems
• Crafting a hybrid recommender with Python: Step-by-step guide
• Conclusion
• Points to remember
• Questions

Data Storage Mastery: Databases and Efficient Data Management

• Introduction
• Exploring database types: Relational and NoSQL databases
• Diversifying your data storage: NoSQL databases
• Python meets SQL: Mastering database interaction
• Navigating databses in Python: SQLAlchemy, SQLite3, PyMango
• Python data format handling: CSV, JSON, XML, Parquet, Excel
• Unpacking serialization: Moving and storing data efficiently
• Data warehouses and data lakes: A comprehensive guide
• Conclusion
• Points to remember
• Questions

Data Science in Action: A Comprehensive End-to-end Project

• Introduction
• Defining a data science problem
• Data collection and preparation
• From selection to evaluation: Charting the model’s journey
• Communication of results
• Deployment, monitoring and maintenance of a model
• Conclusion
• Points to remember