“Python Data Science with Pandas: Master 12 Advanced Projects” Welcome to the first advanced Pandas Data Science course with projects! Where many other courses end, one course begins: You are able to write some Pandas code, but you are still having trouble with real-world projects because Real-World Data is frequently not provided in a single or small number of text/excel files; therefore, more advanced Data Importing Techniques are required; Real-World Data is large, unstructured, nested, and unclean; consequently, more advanced Data Manipulation and Data Analysis/Visualization Techniques are required; and finally, many simple Pandas methods work best with relatively Whether you require exceptional Pandas abilities for data analysis, machine learning, or finance, this course will help you reach expert level! Understand your practical projects!

The entire Data Workflow A-Z is covered in this course. Import (complicated and nested) (complex and nested) a collection of JSON data. Import (complicated and nested) (complex and nested) data from the internet using wrapper packages, JSON, and web APIs. from SQL Databases, import (complex and nested) data. Use JSON files to store (complex and nested) data. Store (complicated and nested) (complex and nested) SQL Databases’ data. Work simultaneously with SQL databases and Pandas (getting the best of both worlds). Import and combine data from numerous text/CSV sources quickly. Sort out messy, huge datasets with more general code. Nested and stringified Data in DataFrames should be cleaned, handled, and flattened. understanding of handling and normalizing Unicode strings Currently, udemy is offering the course for up to 87 % off i.e. INR 449 (INR 3,499).  (5.5 USD)

Who can Opt for this Course?

  • Anyone who is serious about mastering big, disorderly, and dirty datasets.
  • Anyone looking to advance their abilities from “I can write some Pandas code” to “I can master my real-world Data Projects with Pandas – Data Scientists, Machine Learning Experts, professionals in Finance and Investments, and Researchers.

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,49987 % off
Duration15 Hours
Student Enrollment9,101 students
InstructorAlexander Hagmann https://www.linkedin.com/in/alexanderhagmann
Topics Covered
  • How to get the most out of this Course
  • Data Import from CSV file and first Inspection
  • What are the most common Words in Movie Titles, Taglines and Overviews?
Course LevelN.A
Total Student Reviews654

Learning Outcomes

  • You won’t find this course’s advanced real-world data workflows with pandas anywhere else
  • Working simultaneously with Pandas and SQL Databases
  • Importing big datasets from the Web using APIs, JSON, and Pandas
  • Pushing the Boundaries of Pandas
  • Real estate price prediction using machine learning
  • Applications in finance include index tracking and back- and forward-testing investment strategies
  • Standardization, Feature Engineering, Pandas Sampling, and Dummy Variables utilizing big datasets
  • Working with utterly unkempt or filthy datasets
  • Using Pandas to handle nested and stringified JSON data
  • Data loading from databases (SQL) into Pandas and the other way around
  • JSON data loading into Pandas and the other way around
  • Scraping the web using Pandas
  • Cleaning up huge, cluttered datasets with millions of rows and columns
  • Using Python Wrapper Packages and APIs to import large Datasets from the Web
  • Large real-world data sets for explanatory data analysis
  • Advanced visualizations using Seaborn and Matplotlib

Course Content

S.No.Module (Duration)Topics
1.Getting Started (43 minutes)Course Overview (don´t skip!)
Tips: How to get the most out of this Course (don´t skip!)
FAQ / Your Questions Answered
How to download and install Anaconda for Python coding
Jupyter Notebooks – let´s get started
How to work with Jupyter Notebooks
2.Project 1: Explanatory Data Analysis & Data Presentation (Movies Dataset) (01 hour 15 minutes)Project Overview
Downloads (Project 1)
Project Brief for Self-Coders
Data Import from CSV file and first Inspection
The best and the worst movies… (Part 1)
The best and the worst movies… (Part 2)
Which Movie would you like to see next?
What are the most common Words in Movie Titles, Taglines and Overviews?
Are Franchises more successful?
What are the most successful Franchises?
The most successful Directors
The most successful Actors (Part 1)
The most successful Actors (Part 2)
Now it´s your turn (Homework)
3.Project 2: Data Import – Working with APIs and JSON (Movies Dataset) (54 minutes)Project Overview
What is JSON?
Downloads (Project 2)
Project Brief for Self-Coders
Importing Data from JSON files
JSON and Orientation/Formats
What is an API? – The Movie Database API
Working with APIs and JSON (Part 1)
How to work with your own API-KEY
Working with APIs and JSON (Part 2)
Importing and Storing the Movies Dataset (Best Practice)
Importing and Storing the Movies Dataset (Real World Scenario)
4.Project 3: Data Cleaning – Tidy up messy Datasets (Movies Dataset) (59 minutes)Project Overview
Downloads (Project 3)
Project Brief for Self-Coders
First Steps
Dropping irrelevant Columns
How to handle stringified JSON columns (Part 1)
How to handle stringified JSON columns (Part 2)
How to flatten nested Columns
How to clean Numerical Columns (Part 1)
How to clean Numerical Columns (Part 2)
How to clean Columns with DateTime Information
How to clean String / Text Columns
How to Remove Duplicates
Handling Missing Values & Removing Observations/Rows
Final Steps
5.Project 4: Merging, Cleaning & Transforming Data (Movies Dataset) (21 minutes)Project Overview
Downloads (Project 4)
Project Brief for Self-Coders
Getting the Datasets
Preparing the Data for Merge
Merging the Data (Left Join)
Cleaning and Transforming the new “Cast” Column
Cleaning and Transforming the new “Crew” Column
Final Steps
6.Project 5: Working with Pandas and SQL Databases (Movies Dataset) (45 minutes)Project Overview
What is a Database / SQL?
Downloads (Project 5)
Project Brief for Self-Coders
How to create an SQLite Database
How to load Data from DataFrames into an SQLite Database
How to load Data from SQLite Databases into DataFrames
Some simple SQL Queries
Some more SQL Queries
Join Queries
Final Case Study
7.Project 6: Importing & Concatenating many files (Baby Names Dataset) (36 minutes)Project Overview
Downloads (Project 6)
Project Brief for Self-Coders (Part 1)
Getting the Data from the Web
Importing one File & Understanding the Data Structure (an easy case)
Importing & merging many Files (an easy case)
Final Steps
Project Brief for Self-Coders (Part 2)
Importing one File & Understanding the Data Structure (complex case)
The glob module
Importing & merging many Files (complex case)
Excursus: Saving Memory – Categorical Features
8.Project 7: Explanatory Data Analysis & Advanced Visualization (Baby Names) (01 hour 06 minutes)Project Overview
Downloads (Project 7)
Project Brief for Self-Coders
First Inspection: The most popular Names in 2018
Evergreen Names (1880 – 2018)
Advanced-Data Aggregation
What are the most popular Names of all Time?
General Trends over Time (1880 – 2018)
Creating the Features “Popularity” and “Rank”
Visualizing Name Trends over Time
Why does a Name´s Popularity suddenly change? (Part 1)
Why does a Name´s Popularity suddenly change? (Part 2)
Persistent vs. Spike-Fade Names
Most Popular Unisex Names
9.Project 8: Data Preprocessing & Feature Engineering for Machine Learning (01 hour 05 minutes)Project Overview
Downloads (Project 8)
Project Brief for Self-Coders
Data Import and first Inspection
Data Cleaning and Creating Additional Features
Which Factors Influence House Prices?
Advanced Explanatory Data Analysis with Seaborn
Feature Engineering – Part 1
Feature Engineering – Part 2
Splitting the Data into Train and Test Set
Training the ML Model (Random Forest)
Evaluating the Model on the Test Set
Feature Importance
10.Project 9: Data Import – Web Scraping, APIs & Wrappers (US Stocks) (16 minutes)Project Overview
Downloads (Project 9)
Web Scraping – the Dow Jones Constituents
Normalizing Unicode Strings and Getting the Ticker Symbols
Download and Installation of an API Wrapper Package
Loading and Saving Historical Stock Prices
11.Project 10 (Finance Stack): Backtesting Investment Strategies (US Stocks) (39 minutes)Project Overview
Downloads (Project 10)
Importing the Data
Data Visualization & Returns
Backtesting a simple Momentum Strategy
Backtesting a simple Contrarian Strategy
More complex Strategies & Backtesting vs. Fitting
Simple Moving Averages (SMA)
Backtesting Simple Moving Averages (SMA) Strategies
Backtesting the Perfect Strategy (…in case you can predict the future…)
12.Project 11 (Finance Stack): Index Tracking and Forward Testing (US Stocks) (52 minutes)Project Overview
Downloads (Project 11)
Importing & Merging the Data
Transforming the Data
Explanatory Data Analysis (Risk, Return & Correlations)
Index Tracking – Introduction
Index Tracking – Selecting the Tracking Stocks
Index Tracking – A simple Tracking Portfolio
Index Tracking – The optimal Tracking Portfolio
Forward Testing (Part 1)
Forward Testing (Part 2)
13.Project 12: Explanatory Data Analysis and Seaborn Visualization (Olympic Games) (01 hours 13 minutes)Project Overview
Downloads (Project 12)
Project Brief for Self-Coders
Data Import and first Inspection
Merging and Concatenating
Data Cleaning (Part 1)
Data Cleaning (Part 2)
What are the most successful countries of all time?
Do GDP, Population, and Politics matter?
Statistical Analysis and Hypothesis Testing with scipy
Aggregating and Ranking
Summer Games vs. Winter Games – does Geographical Location matter?
Men vs. Women – do Culture & Religion matter?
Do Traditions matter?
14.Extra Project: Prepare yourself for the Future – Pandas Version 1.0 (47 minutes)Intro and Overview
How to update Pandas to Version 1.0
Downloads for this Section
Important Recap: Pandas Display Options (Changed in Version 0.25)
Info() method – new and extended output
NEW Extension dtypes (“nullable” dtypes): Why do we need them?
Creating the NEW extension dtypes with convert_dtypes()
NEW pdf NA value for missing values
The NEW “nullable” Int64Dtype
The NEW StringDtype
The NEW “nullable” BooleanDtype
Addition of the ignore_index parameter
Removal of prior Version Deprecations
15.Appendix: Pandas Crash Course (03 hours 53 minutes)Intro to Tabular Data / Pandas
Downloads for this Section
Create your very first Pandas data frame (from csv)
Pandas Display Options and the methods head() & tail()
First Data Inspection
Built-in Functions, Attributes, and Methods with Pandas
Selecting Columns
Selecting one Column with the “dot notation”
Zero-based Indexing and Negative Indexing
Selecting Rows with iloc (position-based indexing)
Slicing Rows and Columns with iloc (position-based indexing)
Position-based Indexing Cheat Sheets
Selecting Rows with loc (label-based indexing)
Slicing Rows and Columns with loc (label-based indexing)
Label-based Indexing Cheat Sheets
First Steps with Pandas Series
Analyzing Numerical Series with unique(), unique() and value_counts()
Analyzing non-numerical Series with unique(), unique(), value_counts()
Sorting of Series and Introduction to the in place – parameter
Filtering DataFrames by one Condition
Filtering DataFrames by many Conditions (AND)
Filtering DataFrames by many Conditions (OR)
Creating Columns based on other Columns
User-defined Functions with apply(), map()
Data Visualization with Matplotlib
GroupBy – an Introduction
Understanding the GroupBy Object
Splitting with many Keys
split-apply-combine explained
split-apply-combine applied
Data with DateTime Information – Part 1
Data with DateTime Information – Part 2
Data with DateTime Information – Part 3
Data with DateTime Information – Part 4
16.What´s next? (outlook and additional resources) (04 minutes)Bonus Lecture

Resources Required

  • Python should be known to you (Standard Library, Numpy, Matplotlib)
  • You ought to have experience with pandas (at least you should know the basics)
  • a desktop computer running Linux, Windows, or a Macintosh that can store and run Anaconda
  • You will be guided through installing the required free software in the course
  • an internet connection with HD video streaming capabilities
  • Math abilities from high school would be ideal (not mandatory, but it helps)

Featured Review

Masha Ninova (5/5): *******BEST TEACHER****** This is the BEST course on PANDA & SQL integration! WHY Because Alexander is an exceptional teacher. I find his style to be genuine and constructive. He explains everything and answers questions within a day or 2!!! I am a newbie to programming*** (specialization in development economics) Main thing about the teaching method I like and prefer ONLY him is that he has so much material available in form of exercises and other downloadable content for each section! I have never just stared at my udemy window wondering when I would be able to apply all that I watch him do…instead from the beginning he has you working on exercises… I literally went through so many python related courses and read all the negative reviews (which is crucial for me) and after days of research, I found this course (i am enrolled in his other course and didn’t even know he had this course available)!! had I known I would not have wasted 4 days searching through other courses that are high rated & bestsellers but are nothing compared to the content quality that you get from Alex.

Pros of course

  • Jorge Castillo (5/5): Great course, very good projects, and good explanation of how to use Pandas
  • Martin Deal (5/5): Great set of projects to work through to enhance skills or serve as a refresher.
  • Vitaliy Babchuk (5/5): This is the best course for data analysts! There are more exercises to train your skills.
  • Ugochukwu Osuji (5/5): Nicely put together with great examples to help anyone grasp the concepts.

About the Author

The instructor of this course is Alexander Hagmann who is a Data Scientist, Finance Professional, and Entrepreneur with a 4.6 instructor rating and 10,522 reviews on Udemy. He/She offers 16 courses and has taught 91,054 students so far.

  • Alexander has more than 10 years of experience in the finance and investment industry as a data scientist and finance professional
  • Additionally, he is a top-rated Udemy instructor for data analysis and manipulation using pandas (Financial) (Financial) Python for Data Science in Business and Finance – Automated Trading Alexander began his career in the conventional finance industry before advancing gradually into data-driven and AI-driven finance roles
  • He presently develops solutions for Robo Investing and Algorithmic Trading while working on cutting-edge Fintech initiatives
  • And here on Udemy, Alexander is eager to impart his skills to others
  • His graduates hold positions in some of the biggest and most well-known IT and banking firms worldwide
  • One thing all of Alexander’s courses have in common is their practical, real-world-proven content and principles
  • The emphasis is clearly on understanding concepts and developing abilities rather than memorization
  • Alexander has a Master’s in Finance and has successfully completed all three CFA Exams (he is currently no active member of the CFA Institute)

Comparison Table

ParametersPython Data Science with Pandas: Master 12 Advanced ProjectsInteractive Python Dashboards with Plotly and DashPython 3: Deep Dive (Part 4 – OOP)
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration15.5 hours9.5 hours36.5 hours
Rating4.6 /54.7 /54.8 /5
Student Enrollments9,09544,00824,101
InstructorsAlexander HagmannJose PortillaFred Baptiste
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