Python Data Science with Pandas: Master 12 Advanced Projects
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
- Preview04:36
- 05:27Tips: How to get the most out of this Course (don´t skip!)
- 02:12FAQ / Your Questions answered
- 08:08How to download and install Anaconda for Python coding
- 09:29Jupyter Notebooks - let´s get started
- 14:00How to work with Jupyter Notebooks
Instructor
![Alexander Hagmann](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.6 Instructor Rating
- 2,759 Reviews
- 30,401 Students
- 7 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).