Data Science
Week 1-2: Introduction to Data Science
- What is Data Science? Role and Responsibilities of a Data Scientist
- Python Basics for Data Science: Variables, Data Types, Control Structures
- Introduction to Jupyter Notebooks and Pandas for Data Manipulation
- Exploratory Data Analysis (EDA) Techniques: Summary Statistics, Data Visualization
- Python Basics for Data Science: Variables, Data Types, Control Structures
- Introduction to Jupyter Notebooks and Pandas for Data Manipulation
- Exploratory Data Analysis (EDA) Techniques: Summary Statistics, Data Visualization
Week 3-4: Data Preprocessing and Cleaning
- Data Cleaning Techniques: Handling Missing Values, Outliers, and Duplicates
- Feature Engineering: Creating New Features, Handling Categorical Data
- Data Transformation Techniques: Scaling, Normalization, Encoding
- Feature Engineering: Creating New Features, Handling Categorical Data
- Data Transformation Techniques: Scaling, Normalization, Encoding
Week 5-6: Supervised Learning Algorithms
- Introduction to Supervised Learning
- Linear Regression and Regularization Techniques (Ridge, Lasso)
- Classification Algorithms: Logistic Regression, Decision Trees, Random Forests
- Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score
- Linear Regression and Regularization Techniques (Ridge, Lasso)
- Classification Algorithms: Logistic Regression, Decision Trees, Random Forests
- Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score
Week 7-8: Unsupervised Learning Algorithms
- Introduction to Unsupervised Learning
- Clustering Algorithms: K-Means, Hierarchical Clustering
- Dimensionality Reduction Techniques: Principal Component Analysis (PCA), t-Distributed Stochastic
Neighbor Embedding (t-SNE)
- Clustering Algorithms: K-Means, Hierarchical Clustering
- Dimensionality Reduction Techniques: Principal Component Analysis (PCA), t-Distributed Stochastic
Neighbor Embedding (t-SNE)
Week 9-10: Advanced Machine Learning Techniques
- Ensemble Learning: Bagging, Boosting (AdaBoost, Gradient Boosting)
- Support Vector Machines (SVM)
- Hyperparameter Tuning and Model Selection Techniques
- Model Interpretability and Explainability
- Support Vector Machines (SVM)
- Hyperparameter Tuning and Model Selection Techniques
- Model Interpretability and Explainability
Week 11-12: Time Series Analysis and Forecasting
- Introduction to Time Series Data
- Time Series Decomposition: Trend, Seasonality, Residuals
- ARIMA (AutoRegressive Integrated Moving Average) Models
- Forecasting Techniques: Exponential Smoothing, Prophet
- Time Series Decomposition: Trend, Seasonality, Residuals
- ARIMA (AutoRegressive Integrated Moving Average) Models
- Forecasting Techniques: Exponential Smoothing, Prophet
Week 13-14: Big Data Technologies and Tools
- Introduction to Big Data and Distributed Computing
- Apache Hadoop and HDFS
- Apache Spark: RDDs, DataFrames, Spark SQL
- Introduction to Cloud Computing Platforms (AWS, Azure, Google Cloud)
- Apache Hadoop and HDFS
- Apache Spark: RDDs, DataFrames, Spark SQL
- Introduction to Cloud Computing Platforms (AWS, Azure, Google Cloud)
Week 15-16: Capstone Project and Advanced Topics
- Capstone Project: Design and execute an end-to-end data science project incorporating concepts learned throughout the course
- Presentation and Documentation of Capstone Project
- Advanced Topics: Deep Learning for Data Science, Natural Language Processing (NLP), Model Deployment
- Presentation and Documentation of Capstone Project
- Advanced Topics: Deep Learning for Data Science, Natural Language Processing (NLP), Model Deployment
