Data Science Training
A Data Science course typically covers a wide range of topics essential for analyzing and interpreting complex data. It includes foundational knowledge in statistics, programming, and machine learning, as well as practical skills for working with real-world data.
Data Science Course Syllabus
- Overview of Data Science and its Applications
- The Data Science Workflow: Data Collection, Cleaning, Exploration, Modeling, and Deployment
- Key Roles and Skills in Data Science
- Introduction to Data Science Tools and Environments (e.g., Jupyter Notebooks, RStudio, etc.)
- Understanding Different Types of Data (Structured, Unstructured, Semi-structured)
- Data Sources: Databases, APIs, Web Scraping, Public Datasets
- Techniques for Data Collection and Data Integration
- Tools for Data Collection (e.g., SQL, Python Libraries, Web Scraping Tools)
- Introduction to Data Quality and Cleaning Techniques
- Handling Missing Values and Outliers
- Data Transformation and Normalization
- Feature Engineering and Selection
- Working with Categorical and Numerical Data
- Data Wrangling with Python (Pandas) and R (dplyr, tidyr)
- Importance of EDA in Data Science
- Data Visualization Techniques and Tools (e.g., Matplotlib, Seaborn, ggplot2)
- Descriptive Statistics: Mean, Median, Mode, Variance, and Standard Deviation
- Correlation and Covariance Analysis
- Identifying Patterns and Trends in Data
- Creating and Interpreting Visualizations: Histograms, Box Plots, Scatter Plots
- Introduction to Probability and Statistics
- Probability Distributions: Normal, Binomial, Poisson, etc.
- Hypothesis Testing and Confidence Intervals
- Regression Analysis: Simple and Multiple Linear Regression
- ANOVA and Chi-Square Tests
- Statistical Modeling and Interpretation
- Introduction to Machine Learning and its Types: Supervised, Unsupervised, Reinforcement Learning
- Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC
- Introduction to Model Validation Techniques: Cross-Validation, Train-Test Split
- Overfitting and Underfitting: Concepts and Solutions
- Regression Algorithms: Linear Regression, Polynomial Regression
- Classification Algorithms: Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Neural Networks and Deep Learning Basics
- Model Tuning and Hyperparameter Optimization
- Introduction to Unsupervised Learning
- Clustering Algorithms: K-Means, Hierarchical Clustering, DBSCAN
- Dimensionality Reduction Techniques: PCA (Principal Component Analysis), t-SNE
- Association Rules and Market Basket Analysis
- Introduction to Neural Networks and Deep Learning
- Understanding Perceptrons, Activation Functions, and Architecture
- Building and Training Deep Neural Networks with TensorFlow/Keras or PyTorch
- Convolutional Neural Networks (CNNs) for Image Analysis
- Recurrent Neural Networks (RNNs) and LSTM for Sequence Data
- Transfer Learning and Pre-trained Models
- Case Studies and Real-World Data Science Applications
- Building End-to-End Data Science Projects
- Implementing and Deploying Machine Learning Models
- Collaborating with Stakeholders and Communicating Results
- Ethical Considerations and Data Privacy Issues
- Advanced Machine Learning Techniques: Ensemble Methods, Reinforcement Learning
- Natural Language Processing (NLP) and Text Analytics
- Time Series Analysis and Forecasting
- Automation and MLOps (Machine Learning Operations)
- Emerging Trends in Data Science and AI
