Description
Course Syllabus:
Day 1: Introduction to Data Wrangling
- Overview of Data Science and Data Wrangling
- Introduction to data formats: CSV, Excel, JSON, SQL
- Introduction to Python for Data Wrangling
- Working with Pandas for data manipulation
Day 2: Data Importing and Exporting
- Importing Data from various sources (CSV, Excel, JSON, SQL)
- Exporting data back to different formats
- Handling large datasets efficiently
Day 3: Data Cleaning: Handling Missing Data
- Identifying missing or null values
- Techniques for handling missing data: Imputation, deletion, interpolation
- Understanding outliers and anomalies in data
Day 4: Data Transformation and Manipulation
- Working with Pandas data structures: Series, DataFrame
- Data filtering, sorting, and indexing
- Aggregation, grouping, and merging datasets
Day 5: Data Exploration and Feature Engineering
- Descriptive statistics and summarizing data
- Identifying trends, patterns, and correlations in data
- Feature engineering: Creating new variables
Day 6: Introduction to Data Visualization
- Principles of effective data visualization
- Introduction to Matplotlib for static plots
- Creating line, bar, histogram, and scatter plots
Day 7: Advanced Data Visualization Techniques
- Advanced Matplotlib plots: Subplots, annotations, and color palettes
- Introduction to Seaborn: Creating beautiful statistical graphics
- Heatmaps, boxplots, pairplots, and violin plots
Day 8: Interactive Visualizations
- Introduction to Plotly for interactive visualizations
- Creating interactive plots: Line, bar, pie charts
- Customizing Plotly charts with color, size, and hover effects
Day 9: Visualizing Complex Datasets
- Handling multi-dimensional data
- Visualizing time-series data
- Geographic data visualization: Choropleth maps
Day 10: Final Project and Presentation
- End-to-end data wrangling and visualization project
- Presenting insights and creating interactive dashboards
- Sharing visualizations using platforms like Jupyter Notebook or Tableau
