Introduction to Data Science


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