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Finance & Accounting Finance Data Science

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
Rating: 4.6 out of 54.6 (247 ratings)
3,272 students
Created by Alexander Hagmann
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

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

16 sections • 195 lectures • 15h 33m total length

  • Preview04:36
  • 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 Overview
    01:11
  • Downloads (Project 1)
    03:27
  • Project Brief for Self-Coders
    04:19
  • Preview09:27
  • 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
  • Preview06:01
  • 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 Overview
    01:43
  • What is JSON?
    02:42
  • Downloads (Project 2)
    00:04
  • Project Brief for Self-Coders
    03:45
  • Importing Data from JSON files
    10:47
  • JSON and Orientation/Formats
    05:53
  • Preview07:55
  • 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 Overview
    01:04
  • Downloads (Project 3)
    00:04
  • Project Brief for Self-Coders
    06:36
  • 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 Overview
    00:45
  • Downloads (Project 4)
    00:04
  • Project Brief for Self-Coders
    03:17
  • 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 Overview
    00:53
  • What is a Database / SQL?
    04:13
  • Downloads (Project 5)
    00:04
  • Project Brief for Self-Coders
    03:45
  • 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 Overview
    00:38
  • Downloads (Project 6)
    00:04
  • Project Brief for Self-Coders (Part 1)
    02:02
  • 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
  • Project Brief for Self-Coders (Part 2)
    02:39
  • 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 Overview
    00:45
  • Downloads (Project 7)
    00:04
  • Project Brief for Self-Coders
    08:11
  • 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 Overview
    01:05
  • Downloads (Project 8)
    00:04
  • Project Brief for Self-Coders
    06:09
  • 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 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

Instructor

Alexander Hagmann
Data Scientist | Finance Professional | Entrepreneur
Alexander Hagmann
  • 4.7 Instructor Rating
  • 3,532 Reviews
  • 35,499 Students
  • 8 Courses

Alexander is a Data Scientist and Finance Professional with more than 10 years of experience in the Finance and Investment Industry.

He is also a Bestselling Udemy Instructor for

- Data Analysis/Manipulation with Pandas

- (Financial) Data Science

- Python for Business and Finance

- Algorithmic Trading

Alexander started his career in the traditional Finance sector and moved step-by-step into Data-driven and Artificial Intelligence-driven Finance roles. He is currently working on cutting-edge Fintech projects and creates solutions for Algorithmic Trading and Robo Investing. And Alexander is excited to share his knowledge with others here on Udemy. Students who completed his courses work in the largest and most popular tech and finance companies all over the world.

Alexander´s courses have one thing in common: Content and concepts are practical and real-world proven. The clear focus is on acquiring skills and understanding concepts rather than memorizing things.   

Alexander holds a Master´s degree in Finance and passed all three CFA Exams (he is currently no active member of the CFA Institute).   

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