Contents
- Introduction to Data Science
- Definition and scope of data science
- The data science lifecycle
- Role of data science in various industries
- Foundations of Data Science
- Basics of statistics and probability
- Linear algebra for data science
- Essential mathematical concepts
- Data Collection and Exploration
- Collecting and accessing data
- Exploratory Data Analysis (EDA)
- Data visualization techniques
- Data Cleaning and Preprocessing
- Dealing with missing data
- Handling outliers
- Data normalization and scaling
- Introduction to Machine Learning
- Basics of machine learning
- Supervised, unsupervised, and semi-supervised learning
- Model training and evaluation
- Regression Analysis
- Linear regression
- Multiple regression
- Polynomial regression
- Classification Algorithms
- Logistic regression
- Decision trees
- Support Vector Machines (SVM)
- Clustering Algorithms
- K-means clustering
- Hierarchical clustering
- DBSCAN
- Dimensionality Reduction Techniques
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Feature engineering
- Introduction to Big Data and Tools
- Overview of big data technologies
- Hadoop and MapReduce
- Apache Spark for distributed computing
- Introduction to Deep Learning
- Basics of neural networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Natural Language Processing (NLP)
- Text processing and tokenization
- Sentiment analysis
- Named Entity Recognition (NER)
- Data Science vs. Data Analytics
- Distinguishing between data science and data analytics
- Overlapping areas and complementary roles
- Choosing the right approach for different scenarios
- Data Science Ethics and Privacy
- Ethical considerations in data science
- Ensuring privacy in data handling
- Responsible data science practices
- Data Science in Action: Case Studies
- Real-world applications of data science
- Case studies from various industries
- Lessons learned from successful projects
- Future Trends in Data Science
- Emerging technologies in data science
- Ethical considerations and challenges
- Opportunities for innovation in data science
