Skills you’ll Learn
EDA concepts
EDA in python
Visualization tools
Module
1. Introduction to Exploratory Data Analysis (EDA)
- What is EDA?
- Importance of EDA in data science and machine learning.
- Overview of the EDA workflow.
2. Data Collection and Preparation
- Loading datasets from different sources (CSV, Excel, databases).
- Handling missing values (removal, imputation).
- Identifying and treating outliers.
- Data type conversions and feature transformations.
3. Summary Statistics and Data Insights
- Understanding central tendency (mean, median, mode).
- Measuring data dispersion (variance, standard deviation, percentiles).
- Correlation and covariance.
- Detecting skewness and kurtosis.
4. Data Visualization Techniques
- Introduction to data visualization and best practices.
- Univariate analysis:
- Histograms, box plots, bar charts, and KDE plots.
- Bivariate and multivariate analysis:
- Scatter plots, pair plots, heatmaps.
- Categorical vs. numerical data representation.
5. Feature Engineering and Transformation
- Encoding categorical variables (One-Hot Encoding, Label Encoding).
- Scaling and normalization techniques.
- Creating new features from existing data.
6. Case Study: Real-World Data Exploration
- Hands-on EDA using a real dataset (e.g., Titanic, Iris, or a custom dataset).
- Step-by-step implementation in Python with Pandas, Matplotlib, and Seaborn.
- Insights and decision-making from EDA results.
7. Tools and Libraries for EDA
- Overview of Pandas, NumPy, Matplotlib, Seaborn.
- Introduction to advanced tools (e.g., Sweetviz, Pandas Profiling).
8. Conclusion and Best Practices
- Summary of key EDA techniques.
- Common pitfalls in EDA.
- Next steps: Preparing data for machine learning.
Description
The Basics of Exploratory Data Analysis course shall imbibe in you the knowledge on working with Data Manipulation techniques with DPLYR and its functions to reduce the arduous task. The course shall then continue with Data Visualization techniques using the GGPLOT2 grammar package and different plots and layers. You will learn the statistics involved with the subject and the science supporting Data Science strategies. In the later part of this course, a case study on the Pokemon Dataset would be fun for you to apply these concepts and understand the subject as a whole. You can refer to the attached study materials at any point after enrolling in the course and take up the quiz at the end to test your knowledge and understand your gains.
Dahiru –
“This ‘Basics of Exploratory Data Analysis’ course was exactly what I needed to get started with data analysis. The concepts were explained clearly and concisely, and the practical examples helped me understand how to apply them to real-world datasets. I now feel much more confident in my ability to explore and understand data.”
Akeem –
“This course, ‘Basics of Exploratory Data Analysis’, was fantastic for building a solid foundation in data exploration! The concepts were explained clearly and concisely, and the practical examples really helped solidify my understanding. I especially appreciated the focus on using different visualization techniques to uncover insights. It’s a great starting point for anyone looking to get into data analysis.”
Chika –
“This course, ‘Basics of Exploratory Data Analysis,’ was a fantastic introduction to the field! The explanations were clear and concise, making complex topics easy to understand. I especially appreciated the practical exercises that allowed me to apply the concepts I was learning. I now feel confident in my ability to explore and analyze data effectively.”
Johnson –
“This course, ‘Basics of Exploratory Data Analysis’, was excellent for building a solid foundation. The concepts were explained clearly, and the practical exercises were incredibly helpful in reinforcing my understanding. I now feel much more confident in my ability to analyze datasets and draw meaningful insights. It’s a really well-structured course for anyone starting out in data analysis.”