Python Data Science with Pandas: Master 12 Advanced Projects
4.7 (109 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,702 students enrolled

Python Data Science with Pandas: Master 12 Advanced Projects

Work with Pandas, SQL Databases, JSON, Web APIs & more to master your real-world Machine Learning & Finance Projects
Bestseller
4.7 (109 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,702 students enrolled
Created by Alexander Hagmann
Last updated 8/2020
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 14.5 hours on-demand video
  • 19 articles
  • 17 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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
Requirements
  • You should be familiar with Python (Standard Library, Numpy, Matplotlib)
  • You should have worked with Pandas before (at least you should know the basics)
  • A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
  • An internet connection capable of streaming HD videos.
  • Some high school level math skills would be great (not mandatory, but it helps)
Description

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 but you 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 Techniques are required

  • Real-World Data is large, unstructured, nested and unclean -> more advanced Data Manipulation and Data Analysis/Visualization Techniques are 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 JSON files.

  • 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 JSON files.

  • Store (complex and nested) Data in SQL Databases.

  • Work with Pandas and SQL Databases in 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!

Who this course is for:
  • 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
Course content
Expand all 186 lectures 14:48:45
+ Getting Started
6 lectures 43:52
Tips: How to get the most out of this Course (don´t skip!)
05:27
FAQ / Your Questions answered
02:12
How to download and install Anaconda for Python coding
08:08
Jupyter Notebooks - let´s get started
09:29
How to work with Jupyter Notebooks
14:00
+ Project 1: Explanatory Data Analysis & Data Presentation (Movies Dataset)
14 lectures 01:15:52
Project Overview
01:11
Downloads (Project 1)
03:27
Project Brief for Self-Coders
04:19
The best and the worst movies... (Part 1)
08:09
The best and the worst movies... (Part 2)
06:24
Which Movie would you like to see next?
08:12
Are Franchises more successful?
06:05
What are the most successful Franchises?
04:52
The most successful Directors
04:23
The most successful Actors (Part 1)
08:27
The most successful Actors (Part 2)
04:36
Now it´s your turn (Homework)
00:19
+ Project 2: Data Import - Working with APIs and JSON (Movies Dataset)
11 lectures 50:40
Project Overview
01:43
Downloads (Project 2)
00:04
What is JSON?
02:42
Importing Data from JSON files
10:47
JSON and Orientation/Formats
05:53
Working with APIs and JSON (Part 1)
06:42
How to work with your own API-KEY
01:33
Working with APIs and JSON (Part 2)
04:14
Importing and Storing the Movies Dataset (Best Practice)
06:32
Importing and Storing the Movies Dataset (Real World Scenario)
02:35
+ Project 3: Data Cleaning - Tidy up messy Datasets (Movies Dataset)
14 lectures 53:00
Project Overview
01:04
Downloads (Project 3)
00:04
First Steps
02:55
Dropping irrelevant Columns
02:12
How to handle stringified JSON columns (Part 1)
06:39
How to handle stringified JSON columns (Part 2)
03:05
How to flatten nested Columns
07:34
How to clean Numerical Columns (Part 1)
04:58
How to clean Numerical Columns (Part 2)
05:05
How to clean Columns with DateTime Information
02:10
How to clean String / Text Columns
03:17
How to remove Duplicates
03:49
Handling Missing Values & Removing Obervations/Rows
06:06
Final Steps
04:02
+ Project 4: Merging, Cleaning & Transforming Data (Movies Dataset)
8 lectures 17:54
Project Overview
00:45
Downloads (Project 4)
00:04
Getting the Datasets
02:18
Preparing the Data for Merge
02:02
Merging the Data (Left Join)
03:15
Cleaning and Transforming the new "Cast" Column
04:30
Cleaning and Transforming the new "Crew" Column
03:53
Final Steps
01:07
+ Project 5: Working with Pandas and SQL Databases (Movies Dataset)
10 lectures 41:49
Project Overview
00:53
Downloads (Project 5)
00:04
What is a Database / SQL?
04:13
How to create an SQLite Database
03:34
How to load Data from DataFrames into an SQLite Database
07:14
How to load Data from SQLite Databases into DataFrames
03:19
Some simple SQL Queries
06:13
Some more SQL Queries
06:10
Join Queries
04:39
Final Case Study
05:30
+ Project 6: Importing & Concatenating many files (Baby Names Dataset)
10 lectures 31:48
Project Overview
00:38
Downloads (Project 6)
00:04
Getting the Data from the Web
02:56
Importing one File & Understanding the Data Structure (easy case)
04:28
Importing & merging many Files (easy case)
10:26
Final Steps
01:44
Importing one File & Understanding the Data Structure (complex case)
02:24
The glob module
03:51
Importing & merging many Files (complex case)
02:52
Excursus: Saving Memory - Categorical Features
02:25
+ Project 7: Explanatory Data Analysis & Advanced Visualization (Baby Names)
13 lectures 58:37
Project Overview
00:45
Downloads (Project 7)
00:04
First Inspection: The most popular Names in 2018
03:47
Evergreen Names (1880 - 2018)
02:47
Advanced Data Aggregation
05:56
What are the most popular Names of all Times?
02:30
General Trends over Time (1880 - 2018)
04:44
Creating the Features "Popularity" and "Rank"
06:07
Visualizing Name Trends over Time
08:31
Why does a Name´s Popularity suddenly change? (Part 1)
06:53
Why does a Name´s Popularity suddenly change? (Part 2)
05:38
Persistant vs. Spike-Fade Names
04:37
Most Popular Unisex Names
06:18
+ Project 8: Data Preprocessing & Feature Engineering for Machine Learning
12 lectures 59:45
Project Overview
01:05
Downloads (Project 8)
00:04
Data Import and first Inspection
05:53
Data Cleaning and Creating additional Features
05:08
Which Factors influence House Prices?
13:18
Advanced Explanatory Data Analyis with Seaborn
10:00
Feature Engineering - Part 1
03:08
Feature Engineering - Part 2
04:05
Splitting the Data into Train and Test Set
06:50
Training the ML Model (Random Forest)
05:03
Evaluating the Model on the Test Set
02:55
Feature Importance
02:16
+ Project 9: Data Import - Web Scraping, APIs & Wrappers (US Stocks)
6 lectures 16:28
Project Overview
01:01
Downloads (Project 9)
00:04
Web Scraping - the Dow Jones Constituents
03:45
Normalizing Unicode Strings and Getting the Ticker Symbols
04:47
Download and Installation of an API Wrapper Package
02:19
Loading and Saving Historical Stock Prices
04:32