Python Data Science with Pandas- Master 12 Advanced Projects

Python Data Science with Pandas: Master 12 Advanced Projects, available at $94.99, has an average rating of 4.27, with 214 lectures, 15 quizzes, based on 961 reviews, and has 13086 subscribers.

You will learn about Advanced Real-World Data Workflows with Pandas you won′t find in any other Course. Working with Pandas and SQL-Databases in parallel (getting the best out of two worlds) Working with APIs, JSON and Pandas to import large Datasets from the Web Bringing Pandas to its Limits (and beyond) Machine Learning Application: Predicting Real Estate Prices Finance Applications: Backtesting & Forward Testing Investment Strategies + Index Tracking Feature Engineering, Standardization, Dummy Variables and Sampling with Pandas Working with large Datasets (millions of rows/columns) Working with completely messy/unclean Datasets (the standard case in real-world) Handling stringified and nested JSON Data with Pandas Loading Data from Databases (SQL) into Pandas and vice versa Loading JSON Data into Pandas and vice versa Web-Scraping with Pandas Cleaning large & messy Datasets (millions of rows/columns) Working with APIs and Python Wrapper Packages to import large Datasets from the Web Explanatory Data Analysis with large real-world Datasets Advanced Visualizations with Matplotlib and Seaborn This course is ideal for individuals who are Everyone who really want to master large, messy and unclean Datasets. or Everyone who want to improve skills from I can write some Pandas Code to I can master my real-word Data Projects with Pandas or Data Scientists or Machine Learning Professionals or Finance & Investment Professionals or Researchers It is particularly useful for Everyone who really want to master large, messy and unclean Datasets. or Everyone who want to improve skills from I can write some Pandas Code to I can master my real-word Data Projects with Pandas or Data Scientists or Machine Learning Professionals or Finance & Investment Professionals or Researchers.

Enroll now: Python Data Science with Pandas: Master 12 Advanced Projects

Summary

Title: Python Data Science with Pandas: Master 12 Advanced Projects

Price: $94.99

Average Rating: 4.27

Number of Lectures: 214

Number of Quizzes: 15

Number of Published Lectures: 214

Number of Published Quizzes: 13

Number of Curriculum Items: 229

Number of Published Curriculum Objects: 227

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Advanced Real-World Data Workflows with Pandas you won′t find in any other Course.

Working with Pandas and SQL-Databases in parallel (getting the best out of two worlds)

Working with APIs, JSON and Pandas to import large Datasets from the Web

Bringing Pandas to its Limits (and beyond)

Machine Learning Application: Predicting Real Estate Prices

Finance Applications: Backtesting & Forward Testing Investment Strategies + Index Tracking

Feature Engineering, Standardization, Dummy Variables and Sampling with Pandas

Working with large Datasets (millions of rows/columns)

Working with completely messy/unclean Datasets (the standard case in real-world)

Handling stringified and nested JSON Data with Pandas

Loading Data from Databases (SQL) into Pandas and vice versa

Loading JSON Data into Pandas and vice versa

Web-Scraping with Pandas

Cleaning large & messy Datasets (millions of rows/columns)

Working with APIs and Python Wrapper Packages to import large Datasets from the Web

Explanatory Data Analysis with large real-world Datasets

Advanced Visualizations with Matplotlib and Seaborn

Who Should Attend

Everyone who really want to master large, messy and unclean Datasets.

Everyone who want to improve skills from I can write some Pandas Code to I can master my real-word Data Projects with Pandas

Data Scientists

Machine Learning Professionals

Finance & Investment Professionals

Researchers

Target Audiences

Everyone who really want to master large, messy and unclean Datasets.

Everyone who want to improve skills from I can write some Pandas Code to I can master my real-word Data Projects with Pandas

Data Scientists

Machine Learning Professionals

Finance & Investment Professionals

Researchers

***Fully updated and revised in December 2023***

Welcome to the first advanced and project-based Pandas Data Science Course!

This Course starts where many other courses end: You can write some Pandas code butyou are still struggling with real-world Projects because

Real-World Data is typically not provided in a single or a few text/excel files -> more advanced Data Importing Techniquesare required

Real-World Data is large, unstructured, nested and unclean -> more advanced Data Manipulation and Data Analysis/Visualization Techniquesare required

many easy-to-use Pandas methods work best with relatively small and clean Datasets -> real-world Datasets require more General Code (incorporating other Libraries/Modules)

No matter if you need excellent Pandas skills for Data Analysis, Machine Learning or Finance purposes, this is the right Course for you to get your skills to Expert Level! Master your real-world Projects!

This Course covers the full Data Workflow A-Z:

Import (complex and nested) Data from JSONfiles.

Import (complex and nested) Data from the Web with Web APIs, JSON and Wrapper Packages.

Import (complex and nested) Data from SQL Databases.

Store (complex and nested) Data in JSONfiles.

Store (complex and nested) Data in SQL Databases.

Work with Pandas and SQL Databasesin parallel (getting the best of both worlds).

Efficiently import and merge Data from many text/CSV files.

Clean large and messy Datasets with more General Code.

Clean, handle and flatten nested and stringified Data in DataFrames.

Know how to handle and normalize Unicode strings.

Merge and Concatenate many Datasets efficiently.

Scale and Automate data merging.

Explanatory Data Analysis and Data Presentation with advanced Visualization Tools (advanced Matplotlib & Seaborn).

Test the Performance Limits of Pandas with advanced Data Aggregations and Grouping.

Data Preprocessing and Feature Engineering for Machine Learning with simple Pandas code.

Use your Data 1: Train and test Machine Learning Models on preprocessed Data and analyze the results.

Use your Data 2: Backtesting and Forward Testing of Investment Strategies (Finance & Investment Stack).

Use your Data 3: Index Tracking(Finance & Investment Stack).

Use your Data 4: Present your Data with Python in a nicely looking HTML format (Website Quality).

and many more

I am Alexander Hagmann, Finance Professional and Data Scientist (> 7 Years Industry Experience) and best-selling Instructor for Pandas, (Financial) Data Science and Finance with Python. Looking forward to seeing you in this Course!

Course Curriculum

Chapter 1: Getting Started

Lecture 1: Course Overview (don′t skip!)

Lecture 2: Tips: How to get the most out of this Course (don′t skip!)

Lecture 3: FAQ / Your Questions answered

Lecture 4: How to download and install Anaconda for Python coding

Lecture 5: Jupyter Notebooks – let′s get started

Lecture 6: How to work with Jupyter Notebooks

Chapter 2: Project 1: Explanatory Data Analysis & Data Presentation (Movies Dataset)

Lecture 1: Project Overview

Lecture 2: Downloads (Project 1)

Lecture 3: Project Brief for Self-Coders

Lecture 4: Data Import from csv file and first Inspection

Lecture 5: The best and the worst movies (Part 1)

Lecture 6: The best and the worst movies (Part 2)

Lecture 7: Which Movie would you like to see next?

Lecture 8: What are the most common Words in Movie Titles, Taglines and Overviews?

Lecture 9: Are Franchises more successful?

Lecture 10: What are the most successful Franchises?

Lecture 11: The most successful Directors

Lecture 12: The most successful Actors (Part 1)

Lecture 13: The most successful Actors (Part 2)

Lecture 14: Now it′s your turn (Homework)

Chapter 3: Excursus: How to avoid and debug Coding Errors (incl. ChatGPT)

Lecture 1: Introduction

Lecture 2: Test your debugging skills!

Lecture 3: Major reasons for Coding Errors

Lecture 4: The most commonly made Errors at a glance

Lecture 5: Omitting cells, changing the sequence and more

Lecture 6: IndexErrors

Lecture 7: Indentation Errors

Lecture 8: Misuse of function names and keywords

Lecture 9: TypeErrors and ValueErrors

Lecture 10: **NEW** Debugging Pandas Errors with ChatGPT

Lecture 11: Getting help on StackOverflow.com

Lecture 12: How to traceback more complex Errors

Lecture 13: Problems with the Python Installation

Lecture 14: External Factors and Issues

Lecture 15: Errors related to the course content (Transcription Errors)

Lecture 16: Summary and Debugging Flow-Chart

Lecture 17: **NEW** The Debugging Flow-Chart with ChatGPT

Chapter 4: Project 2: Data Import – Working with APIs and JSON (Movies Dataset)

Lecture 1: Project Overview

Lecture 2: What is JSON?

Lecture 3: Downloads (Project 2)

Lecture 4: Project Brief for Self-Coders

Lecture 5: Importing Data from JSON files

Lecture 6: JSON and Orientation/Formats

Lecture 7: What is an API? – The Movie Database API

Lecture 8: Working with APIs and JSON (Part 1)

Lecture 9: How to work with your own API-KEY

Lecture 10: Working with APIs and JSON (Part 2)

Lecture 11: Importing and Storing the Movies Dataset (Best Practice)

Lecture 12: Importing and Storing the Movies Dataset (Real World Scenario)

Chapter 5: Project 3: Data Cleaning – Tidy up messy Datasets (Movies Dataset)

Lecture 1: Project Overview

Lecture 2: Downloads (Project 3)

Lecture 3: Project Brief for Self-Coders

Lecture 4: First Steps

Lecture 5: Dropping irrelevant Columns

Lecture 6: How to handle stringified JSON columns (Part 1)

Lecture 7: How to handle stringified JSON columns (Part 2)

Lecture 8: How to flatten nested Columns

Lecture 9: How to clean Numerical Columns (Part 1)

Lecture 10: How to clean Numerical Columns (Part 2)

Lecture 11: How to clean Columns with DateTime Information

Lecture 12: How to clean String / Text Columns

Lecture 13: How to remove Duplicates

Lecture 14: Handling Missing Values & Removing Obervations/Rows

Lecture 15: Final Steps

Chapter 6: Project 4: Merging, Cleaning & Transforming Data (Movies Dataset)

Lecture 1: Project Overview

Lecture 2: Downloads (Project 4)

Lecture 3: Project Brief for Self-Coders

Lecture 4: Getting the Datasets

Lecture 5: Preparing the Data for Merge

Lecture 6: Merging the Data (Left Join)

Lecture 7: Cleaning and Transforming the new Cast Column

Lecture 8: Cleaning and Transforming the new Crew Column

Lecture 9: Final Steps

Chapter 7: Project 5: Working with Pandas and SQL Databases (Movies Dataset)

Lecture 1: Project Overview

Lecture 2: What is a Database / SQL?

Lecture 3: Downloads (Project 5)

Lecture 4: Project Brief for Self-Coders

Lecture 5: How to create an SQLite Database

Lecture 6: How to load Data from DataFrames into an SQLite Database

Lecture 7: How to load Data from SQLite Databases into DataFrames

Lecture 8: Some simple SQL Queries

Lecture 9: Some more SQL Queries

Lecture 10: Join Queries

Lecture 11: Final Case Study

Chapter 8: Project 6: Importing & Concatenating many files (Baby Names Dataset)

Lecture 1: Project Overview

Lecture 2: Downloads (Project 6)

Lecture 3: Project Brief for Self-Coders (Part 1)

Lecture 4: Getting the Data from the Web

Lecture 5: Importing one File & Understanding the Data Structure (easy case)

Lecture 6: Importing & merging many Files (easy case)

Lecture 7: Final Steps

Lecture 8: Project Brief for Self-Coders (Part 2)

Instructors

Alexander Hagmann
Data Scientist | Finance Professional | Entrepreneur

Rating Distribution

1 stars: 15 votes

2 stars: 20 votes

3 stars: 75 votes

4 stars: 279 votes

5 stars: 572 votes

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