Python Data Science with Pandas: Master 12 Advanced Projects

About Python Data Science with Pandas: Master 12 Advanced Projects

Fully updated and revised in October 2024

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

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!