Course Overview
This course is designed to equip participants with the skills and knowledge needed to perform effective Exploratory Data Analysis (EDA) using Python. EDA is a critical step in the data science process, allowing data scientists to understand the underlying patterns, detect anomalies, and extract valuable insights from datasets before applying any machine learning models. Through hands-on exercises and real-world examples, participants will learn how to use Python libraries such as Pandas, Matplotlib, Seaborn, and others to clean, visualize, and interpret data, ultimately making informed decisions based on their findings.
Course Duration
10 Days
Who Should Attend
- Data Scientists and Analysts
- Aspiring Data Scientists
- Researchers and Academicians
- Business Analysts and Professionals
- Students in Data Science and Analytics fields
- IT professionals looking to transition into data science
Course Objectives
By the end of this course, participants will be able to:
- Understand the fundamental concepts and importance of Exploratory Data Analysis (EDA) in data science.
- Use Python and its libraries to load, clean, and preprocess datasets.
- Identify and handle missing data, outliers, and data inconsistencies.
- Perform univariate, bivariate, and multivariate analysis to discover patterns and relationships.
- Create insightful visualizations to represent data distributions, correlations, and trends.
- Apply statistical techniques to summarize and interpret data.
- Detect and understand underlying data patterns, including anomalies and correlations.
- Communicate EDA findings effectively through well-structured reports and visualizations.
- Work with various types of data, including categorical, numerical, and time series data.
- Prepare data for further analysis, including feature engineering and selection.
Course Outline:
Module 1: Introduction to Exploratory Data Analysis
- Understanding the EDA process
- Importance of EDA in data science
- Python libraries for EDA (NumPy, Pandas, Matplotlib, Seaborn)
Module 2: Data Import and Exploration
- Loading data from various sources (CSV, Excel, databases)
- Basic data exploration using Pandas
- Understanding data types and structures
Module 3: Data Cleaning and Preprocessing
- Handling missing values (imputation, deletion)
- Outlier detection and treatment
- Data normalization and standardization
- Feature engineering
Module 4: Univariate Analysis
- Summary statistics (mean, median, mode, quartiles, etc.)
- Data distribution analysis (histograms, box plots, density plots)
- Categorical data exploration (frequency tables, bar charts)
Module 5: Bivariate Analysis
- Correlation analysis (Pearson, Spearman)
- Scatter plots
- Grouped data analysis (pivot tables, crosstabulations)
- Hypothesis testing (t-test, chi-square test)
Module 6: Multivariate Analysis
- Pair plots and correlation matrices
- Dimensionality reduction techniques (PCA)
- Clustering (k-means, hierarchical clustering)
Module 7: Data Visualization
- Creating effective visualizations (line charts, bar charts, scatter plots, heatmaps)
- Customizing plots for clarity and aesthetics
- Interactive visualizations (using libraries like Plotly)
Module 8: Time Series Analysis
- Exploring time series data
- Time series decomposition (trend, seasonality, residuals)
- Forecasting techniques (ARIMA, exponential smoothing)
Module 9: Case Studies and Real-World Applications
- Analyzing various datasets (e.g., customer data, financial data, social media data)
- Applying EDA to solve real-world problems
- Developing data-driven insights and recommendations
Module 10: Advanced Topics (Optional)
- Geospatial data analysis
- Text mining and NLP for EDA
- Machine learning integration with EDA
