Python Data Analysis: Real World Applications

Learn the basics of python, how to manipulate and visualize data, and how to train and evaluate machine learning models

What you'll learn

The Basics of Python Programming

How to Work with Datasets

How to Visualize Data

Machine Learning and Statistical Modeling

Data Preprocessing and Feature Engineering

Training and Evaluating Machine Learning Models

Requirements

No programming experience needed. You'll learn everything you need to know in this course

Description

Welcome to Python Data Analysis: Real World Applications. I am Zaviir Berry, your instructor for this comprehensive course. I hold a degree in Electrical and Computer Engineering from Rochester Institute of Technology where I specialized in artificial intelligence and its applications in analyzing live brain wave data to classify human motor functions. Since graduating in 2021, I have been working as a Software Engineer at a Fortune 100 company.Throughout this course, you will: gain a solid understanding of the basics of Python programminglearn how to work with datasetsvisualize dataperform machine learning and statistical modeling techniquesWe will delve into the essential components of model development, including: data preprocessingfeature engineeringmodel trainingevaluationUpon completion of this course, participants will have acquired the skills necessary to effectively forecast insurance claim amounts and predict financial market trends using advanced machine learning techniques. They will be able to utilize patient characteristics, such as age, gender, Body Mass Index (BMI), and blood pressure, to make accurate predictions of insurance claim amounts. Additionally, they will be able to predict the closing price of the S&P 500 for the next day with a high degree of accuracy. The course also includes a comprehensive data preprocessing component, which enables participants to effectively prepare data for use in various machine learning techniques, including Linear and Logistic Regression. Furthermore, participants will be able to interpret the results of their models through the application of various evaluation metrics, such as accuracy, precision, and recall, which will allow them to make informed decisions based on their predictions.

Overview

Section 1: Introduction

Lecture 1 Course Introduction

Section 2: Introduction to Python Programming

Lecture 2 Introduction To Python Programming

Lecture 3 Google Colaboratory

Lecture 4 Let's Begin

Lecture 5 Data Types

Lecture 6 Operators and Expressions

Lecture 7 Conditional Statements

Lecture 8 Loops

Lecture 9 Functions

Section 3: Working with Data in Python

Lecture 10 NumPy

Lecture 11 Pandas

Lecture 12 Data Frames

Lecture 13 Importing Data from CSV

Lecture 14 Introduce the Dataset

Lecture 15 Filtering

Lecture 16 GroupBy

Lecture 17 Sorting

Section 4: Visualizing Data in Python

Lecture 18 Matplotlib

Lecture 19 Seaborn

Lecture 20 Plotly

Lecture 21 Examples

Section 5: Handling Missing Data

Lecture 22 dropna

Lecture 23 fill

Lecture 24 interpolate

Section 6: Handling Categorical Data

Lecture 25 Handling Categorical Data

Lecture 26 Label Encoding

Lecture 27 One-Hot Encoding

Lecture 28 Dummy Encoding

Lecture 29 Binary Encoding

Lecture 30 Count Encoding

Section 7: Handling Outliers

Lecture 31 Using Statistical Methods

Lecture 32 Using Visualization Tools

Lecture 33 Removing Outliers

Lecture 34 Transforming Outliers

Lecture 35 Clipping

Lecture 36 Winsorizing

Section 8: Scaling Data

Lecture 37 Scaling

Section 9: Introduction to Machine Learning

Lecture 38 The concept of machine learning

Lecture 39 scikit-learn

Lecture 40 train_test_split

Section 10: Training and Evaluating Models

Lecture 41 Linear Regression

Lecture 42 Logistic Regression

Lecture 43 Decision Tree

Lecture 44 Random Forest

Lecture 45 Support Vector Machines

Lecture 46 Clustering

Section 11: Final Project

Lecture 47 Final Project Introduction

Lecture 48 Final Project Solution

Section 12: Conclusion

Lecture 49 Conclusion

Beginner Python developers who are curious about data analysis and machine learning