**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