Data Analysis Outline
Programming for Data Science is a subject we’ve designed to explore the various programming components of data science.
Keywords
data science, data analysis, programming, dataidea
Data Analysis Outline
Week 1: Introduction to Data Analysis
- Understanding the role of data analysis in decision-making
- Introduction to Python for data analysis (Numpy and Pandas)
- Exploring data types, data structures, and data manipulation
Week 2: Introduction to Data Cleaning and Preprocessing
- Data quality assurance
- Identifying and handling missing data
- Dealing with outliers and other data anomalies
Week 3: Introduction to Data Visualization
- Basic plotting techniques using Matplotlib
- Extracting insights from data distributions and relationships
- Performing EDA using Pandas and visualizations
Week 4: Introduction to Machine Learning
- Overview of machine learning concepts
- Supervised vs. unsupervised learning
- Hands-on exercises with Scikit-Learn for classification and regression
Week 5: Statistical Analysis
- Overview of machine learning concepts
- Descriptive statistics and summary metrics
- Hypothesis testing and p-values
- Implementing statistical analysis in Python using SciPy
Week 6: Data Analysis Practice
- Data Preprocessing (numpy, pandas etc.)
- Data Visualization (matplotlib, pandas etc.)
- Exploratory Data Analysis (pandas, matplotlib)
- Machine Learning Modeling (sci-kit learn, pandas, numpy)
Week 7: Data Wrangling and Feature Engineering
- Feature scaling and engineering for model improvement
- Data normalization and standardization
- Handling categorical data and encoding techniques
Week 8: Model Evaluation and Validation
- Evaluating machine learning models
- Cross-validation and hyperparameter tuning
- Model selection and performance metrics
Week 9: Time Series Analysis
- Evaluating machine learning models
- Understanding time series data
- Time series visualization and decomposition
- Forecasting techniques with Python
Week 10: Capstone Project
- Applying learned concepts to a real-world dataset
- Data analysis, visualization, and modeling
- Presenting findings and insights
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