Advanced Data Science Methods And Algorithms

Advanced Data Science Methods And Algorithms
Published 2/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Learn Advanced Data Science Methods and Algorithms with Pandas and Python

What you’ll learn

Knowledge about Advanced Data Science methods, algorithms, theory, best practices, and tasks

Deep hands-on knowledge of Advanced Data Science and know how to handle Data Science tasks with confidence

Advanced ensemble models such as the XGBoost models for prediction and classification

Detailed and deep Master knowledge of Regression, Regression analysis, Prediction, Classification, and Supervised Learning

Hands-on knowledge of Scikit-learn, Matplotlib, Seaborn, and some other Python libraries

Advanced knowledge of A.I. prediction/classification models and automatic model creation

Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources

Master the Python 3 programming language for Data Handling

Master Pandas 2 and 3 for Advanced Data Handling

Requirements

The four ways of counting (+-*/)

Some Experience with Data Science, Data Analysis, or Machine Learning

Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection

Programming experience is not needed and you will be taught everything you need

The course only uses costless software

Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included
Description

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Setup of the Anaconda Cloud Notebook

Lecture 3 Download and installation of the Anaconda Distribution (optional)

Lecture 4 The Conda Package Management System (optional)

Section 2: Master Python for Data Handling

Lecture 5 Overview of Python for Data Handling

Lecture 6 Python Integers

Lecture 7 Python Floats

Lecture 8 Python Strings

Lecture 9 Python String Methods

Lecture 10 Python Strings and DateTime Objects

Lecture 11 Overview of Python Native Data Storage Structures

Lecture 12 Python Set

Lecture 13 Python Tuple

Lecture 14 Python Dictionary

Lecture 15 Python List

Lecture 16 Overview of Python Data Transformers and Functions

Lecture 17 Python While-loop

Lecture 18 Python For-loop

Lecture 19 Python Logic Operators and conditional code branching

Lecture 20 Python Functions I: Some theory

Lecture 21 Python Functions II: create your own functions

Lecture 22 Python Object Oriented Programming I: Some theory

Lecture 23 Python Object Oriented Programming II: create your own custom objects

Lecture 24 Python Object Oriented Programming III: Files and Tables

Lecture 25 Python Object Oriented Programming IV: Recap and More

Section 3: Master Pandas for Data Handling

Lecture 26 Master Pandas for Data Handling: Overview

Lecture 27 Pandas theory and terminology

Lecture 28 Creating a Pandas DataFrame from scratch

Lecture 29 Pandas File Handling: Overview

Lecture 30 Pandas File Handling: The .csv file format

Lecture 31 Pandas File Handling: The .xlsx file format

Lecture 32 Pandas File Handling: SQL-database files and Pandas DataFrame

Lecture 33 Pandas Operations & Techniques: Overview

Lecture 34 Pandas Operations & Techniques: Object Inspection

Lecture 35 Pandas Operations & Techniques: DataFrame Inspection

Lecture 36 Pandas Operations & Techniques: Column Selections

Lecture 37 Pandas Operations & Techniques: Row Selections

Lecture 38 Pandas Operations & Techniques: Conditional Selections

Lecture 39 Pandas Operations & Techniques: Scalers and Standardization

Lecture 40 Pandas Operations & Techniques: Concatenate DataFrames

Lecture 41 Pandas Operations & Techniques: Joining DataFrames

Lecture 42 Pandas Operations & Techniques: Merging DataFrames

Lecture 43 Pandas Operations & Techniques: Transpose & Pivot Functions

Lecture 44 Pandas Data Preparation I: Overview & workflow

Lecture 45 Pandas Data Preparation II: Edit DataFrame labels

Lecture 46 Pandas Data Preparation III: Duplicates

Lecture 47 Pandas Data Preparation IV: Missing Data & Imputation

Lecture 48 Pandas Data Preparation V: Data Binnings [Extra Video]

Lecture 49 Pandas Data Preparation VI: Indicator Features [Extra Video]

Lecture 50 Pandas Data Description I: Overview

Lecture 51 Pandas Data Description II: Sorting and Ranking

Lecture 52 Pandas Data Description III: Descriptive Statistics

Lecture 53 Pandas Data Description IV: Crosstabulations & Groupings

Lecture 54 Pandas Data Visualization I: Overview

Lecture 55 Pandas Data Visualization II: Histograms

Lecture 56 Pandas Data Visualization III: Boxplots

Lecture 57 Pandas Data Visualization IV: Scatterplots

Lecture 59 Pandas Data Visualization VI: Line plots

Section 4: Advanced Models for Regression and Supervised Learning

Lecture 60 Overview

Lecture 62 Feedforward Multi-Layer Perceptrons for Prediction

Lecture 63 Decision Tree Regression model

Lecture 64 Random Forest Regression

Lecture 65 Voting Regression

Lecture 66 eXtreme Gradient Boosting Regression (XGBoost)

Section 5: Advanced Models for Classification and Supervised Learning

Lecture 67 Overview

Lecture 69 Feedforward Multi-Layer Perceptrons for Classification

Lecture 70 Decision Tree Classifier

Lecture 71 Random Forest Classifier

Lecture 72 Voting Classifier

Lecture 73 eXtreme Gradient Boosting Classifier (XGBoost)

Anyone who wants to learn Advanced Data Science Methods and Algorithms,Anyone who wants to learn Python programming and to reach the intermediate level of Python programming knowledge as required by many Udemy courses!,Anyone who wants to master Pandas for Data Handling!,Anyone who knows Data Science or Machine Learning and want to learn Data Handling skills that work as a force multiplier with the skills you already know!,Anyone who wants to study at the University level and want to learn Advanced Data Science, Machine Learning, and Data Handling skills that they will have use for in their entire career!