10X Your Exploratory Data Analysis Skills With Ai & Python !

Exploratory Data Analysis: From Beginner to Pro with Real Banking Data / Explore, visualize, and analyze Pandas

What you'll learn

Creating stunning visualizations with Python libraries like Matplotlib and Seaborn.

Identifying and handling missing data and outliers.

Cleaning and preprocessing messy banking datasets.

Understanding the fundamentals of exploratory data analysis (EDA).

Applying statistical techniques to uncover insights.

Building interactive dashboards for data presentation.

Using Pandas for data manipulation and analysis.

Analyzing trends, patterns, and relationships in banking data.

Understanding customer segmentation and behavior analysis.

Working with time-series data in banking.

Interpreting and communicating your findings effectively.

Automating repetitive tasks with Python scripts.

Presenting your analysis to stakeholders with confidence.

Preparing data for machine learning models.

Identifying fraud patterns in banking transactions.

Requirements

A willingness to learn and experiment with data.

Familiarity with basic statistics (helpful but not mandatory).

A computer with Python installed (we’ll guide you through the setup).

Basic knowledge of Python programming.

Curiosity and enthusiasm for data analysis.

Description

Are you ready to transform your data analysis skills and unlock the hidden insights in real-world banking data? This course, Exploratory Data Analysis on Real-Life Banking Data Using Python, is your ultimate guide to mastering Exploratory Data Analysis techniques and tools. Whether you're a beginner or an experienced data professional, this course will equip you with the skills to analyze, visualize, and interpret complex datasets with confidence.I’ve spent 6 months researching, testing, and refining the content of this course to ensure it delivers maximum value. Every module is designed to address real-world challenges you’ll face when working with banking data. From cleaning messy datasets to creating stunning visualizations, this course covers it all. By the end of this course, you’ll not only have a deep understanding of Exploratory Data Analysis but also the ability to apply these skills to real-world problems.Why Choose This Course?Unlike other courses that focus on generic datasets, this course dives deep into real-life banking data. You’ll learn how to handle challenges specific to financial datasets, such as missing values, outliers, and complex relationships between variables. This hands-on approach ensures that you’re not just learning theory but also gaining practical experience that you can apply immediately.Here’s what sets this course apart:Real-World Focus: Work with actual banking datasets to solve real problems.Comprehensive Coverage: From data cleaning to advanced visualizations, we cover every step of the Exploratory Data Analysis process.Practical Insights: Learn how to interpret your findings and present them effectively to stakeholders.Expert Guidance: Benefit from my 6 months of research and experience in the field.This course is your gateway to becoming a skilled data analyst. Don’t miss this opportunity to learn from real-world examples and gain practical experience. Enroll now and take the first step toward mastering exploratory data analysis!

Overview

Section 1: Introduction to Exploratory Data Analysis

Lecture 1 Overview of Exploratory Data Analysis

Lecture 2 Conducting Data Overview and Feature Selection

Section 2: Data Overview and Feature Selection

Lecture 3 Techniques for Effective Feature Engineering

Lecture 4 Imputation Techniques for Missing Values

Lecture 5 Automated Value Modification in Multiple Columns

Lecture 6 Evaluating Variables and Detecting Outliers

Lecture 7 Binning Techniques for Variable Categorization

Lecture 8 Comprehensive Data Analysis Techniques

Section 3: Univariate and Bivariate Analysis

Lecture 9 Univariate Analysis of Categorical Variables

Lecture 10 Subsetting Numerical Variables for Detailed Analysis

Lecture 11 Creating and Unstacking Correlation Matrices

Lecture 12 Univariate Analysis of Numerical Variables

Lecture 13 Bivariate Analysis of Numeric Variables

Lecture 14 Analyzing Previous Application Data and Merging with Current Data

Lecture 15 Conducting Univariate and Bivariate Analysis on Merged Dataframe

Lecture 16 Advanced Univariate and Bivariate Analysis Techniques

Python enthusiasts eager to explore data analysis.,Students and graduates in data science, finance, or related fields.,Banking and finance professionals looking to enhance their data skills.,Aspiring data analysts and data scientists.,Anyone passionate about data and analytics.,Entrepreneurs and business owners seeking data-driven insights.,Researchers working with financial datasets.,Professionals transitioning into data-related roles.