Data Science

Duration: 3 Months

Introduction to Data Science:
- What is Data Science? Overview and real-world applications.
- The Data Science process: Data collection, cleaning, analysis, visualization, and interpretation.
- Tools for Data Science: Python, Jupyter Notebooks, Git.
Python for Data Science:
- Introduction to Python programming.
- Data structures in Python: Lists, dictionaries, tuples.
- Control structures: Loops, conditionals, functions.
- Essential libraries: NumPy (for numerical computations), Pandas (for data manipulation), and Matplotlib (for data visualization).

Data Collection:
- Methods for data collection: APIs, web scraping, and databases.
- Importing datasets from CSV, Excel, and other file formats.
Data Cleaning:
- Handling missing or inconsistent data.
- Data transformations: Normalization, encoding categorical data.
- Dealing with outliers and invalid entries.
Feature Engineering:
- Creating new features from existing data.
- Feature scaling and selection.
- Techniques for dealing with large datasets.
Hands-on Project:
- Collect and clean a real-world dataset (e.g., retail sales, weather data, or public healthcare data).

Exploratory Data Analysis (EDA):
- Descriptive statistics: Mean, median, variance, standard deviation.
- Data visualization techniques: Histograms, box plots, scatter plots, and pair plots.
- Tools: Matplotlib, Seaborn, Plotly.
Visualization for Insights:
- Visual storytelling: Creating effective visualizations to communicate data insights.
- Hands-on project: Perform EDA on a real-world dataset (e.g., financial or social data) and visualize key findings.

Supervised Learning:
- Introduction to machine learning.
- Regression (Linear and Logistic Regression).
- Classification algorithms: Decision Trees, Random Forests, and Support Vector Machines (SVM).
- Model evaluation metrics: Accuracy, Precision, Recall, F1 Score.
Unsupervised Learning:
- Clustering techniques: K-Means, Hierarchical Clustering.
- Dimensionality reduction: Principal Component Analysis (PCA).
- Applications of unsupervised learning (e.g., customer segmentation, market analysis).
Hands-on Project:
- Build a supervised learning model (e.g., predicting housing prices) and an unsupervised learning model (e.g., customer segmentation).

Model Validation and Tuning:
- Cross-validation techniques.
- Hyperparameter tuning: Grid search, Random search.
- Dealing with overfitting and underfitting.
Ensemble Methods:
- Boosting algorithms: AdaBoost, Gradient Boosting.
- Bagging techniques: Random Forest, XGBoost.
Hands-on Project:
- Apply advanced machine learning techniques on a real-world dataset (e.g., healthcare prediction or fraud detection).

Introduction to Time-Series Data:
- Time-series characteristics: Trends, seasonality, and noise.
- Techniques for time-series forecasting: Moving averages, ARIMA, Exponential Smoothing.
Hands-on Project:
- Perform time-series forecasting (e.g., sales, stock market predictions).

Big Data Introduction:
- Working with large datasets.
- Introduction to Hadoop and Spark for distributed data processing.
Data Science in the Cloud:
- Cloud platforms for Data Science: AWS, Google Cloud, Microsoft Azure.
- Hands-on project: Process large datasets using PySpark or other Big Data tools.

Capstone Project:
- Apply data science and machine learning techniques to a real-world problem.
- Examples: Build a recommendation system, create a predictive model, or perform market segmentation analysis.
Model Deployment:
- Introduction to deploying models in production environments.
- Tools: Flask or FastAPI for building APIs, Docker for containerization.
- Hands-on task: Deploy a machine learning model to the cloud (AWS, GCP, or Azure).

Ethical Considerations in Data Science:
- Data privacy, bias in algorithms, fairness, and transparency.
- Regulatory frameworks and ethical challenges.
Future of Data Science:
- Trends like AI integration, AutoML, and edge computing.

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