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!