Advanced EDA with Python

In this course, you will learn about exploratory data analysis techniques in Python, including EDA for data preparation.




Hours per week


Live Sessions

Nov 20, 2023

Next Date


The objective of this course is to provide participants with the knowledge and skills to effectively clean, explore, visualize, build predictive models, and communicate insights from large datasets using Python. Participants will learn how to clean and preprocess data, ensuring data quality and removing irrelevant information for analysis. They will explore advanced techniques in exploratory data analysis to gain a deep understanding of datasets and uncover patterns and trends. Participants will also learn how to visualize complex data using advanced visualization techniques in Python, enabling them to effectively communicate insights and make data-driven decisions. Additionally, participants will gain practical experience in building predictive models using PyCaret, a Python library for automated machine learning. Finally, participants will learn how to communicate their findings effectively through data storytelling, presenting insights in a compelling and impactful manner. By the end of the course, participants will be equipped with the skills to effectively clean, explore, visualize, build predictive models, and communicate insights from large datasets using Python, enabling them to derive valuable insights and make data-driven decisions in various domains.


  • Basic knowledge of programming fundamentals.
  • Familiarity with Python programming language.
  • Understanding of basic data manipulation concepts.
  • Prior exposure to data analysis concepts and techniques.
  • Access to a computer with Python installed and the necessary libraries (such as Pandas, NumPy, Matplotlib, and PyCaret) for data cleaning, preprocessing, exploratory data analysis, data visualization, and building predictive models.
  • Familiarity with Jupyter Notebook or any other Python development environment.
  • Willingness to learn and actively participate in hands-on exercises and projects.
  • Basic understanding of statistical concepts (e.g., descriptive statistics, correlation) is helpful but not mandatory.
  • Curiosity and eagerness to effectively communicate data insights through storytelling.


Data Cleaning and Preprocessing

Learn to Prepare data for analysis by addressing various data quality issues. Apply techniques for handling missing data, including imputation methods and data removal strategies. Identifying outliers and understanding their impact on analysis.standardize data for better analysis by using data normalization and scaling techniques. Encoding categorical variables using techniques like one-hot encoding and label encoding. Feature engineering to create new features that enhance model performance. Explore methods to handle data inconsistencies and ensure data quality

Exploratory Data Analysis

Generate summary statistics and key metrics to gain insights into data distribution. Bivariate analysis techniques will be covered to explore relationships between two variables and identify correlations and associations. Multivariate analysis will be used to investigate relationships among multiple variables, leveraging techniques like scatter plots and heatmaps. Data profiling will be introduced to discover patterns, trends, and anomalies within the dataset, facilitating deeper insights and decision-making.

Data Visualization

Learn the principles of data visualization and explore popular Python libraries like Matplotlib and Seaborn to create static visualizations. Explore Interactive visualizations using libraries like Plotly to build dynamic and engaging plots. Geospatial visualization for mapping geographical data using GeoPandas and Folium. And to visualize time series data and identify temporal patterns to extract valuable information from time-based datasets.

Predictive Models

Introductiont to the fundamental predictive models like: linear regression, decision trees.They will understand the fundamentals of these algorithms and their applications in predictive modeling. Model evaluation techniques will be explored to assess model performance using metrics like accuracy, precision, recall, and F1-score. Use cross-validation to validate model performance and prevent overfitting. The topic will conclude with hyperparameter tuning to optimize model performance by fine-tuning hyperparameters using techniques like grid search and random search.

Data Storytelling

Apply the art of presenting data analysis results effectively to different audiences to communicate insights clearly and persuasively. Create interactive dashboards using Streamlit to present insights dynamically, making it easier for stakeholders to interact with the data. Storyboarding will be introduced as a method to structure a data-driven narrative and convey key messages effectively. Ethical considerations in data storytelling, including data privacy and confidentiality, will also be addressed, ensuring responsible data communication and analysis.


Mentor to be defined.
Our alumni works in:

Learn all you can. No extra fees, no commissions, no surprises.

— We’re an hybrid learning platform with live-cohorts. Learn everything you want by acquiring a membership.