“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 Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 15 Hours |
| Rating | 4.6/5 |
| Student Enrollment | 9,101 students |
| Instructor | Alexander Hagmann https://www.linkedin.com/in/alexanderhagmann |
| Topics Covered |
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| Course Level | N.A |
| Total Student Reviews | 654 |
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
| Parameters | Python Data Science with Pandas: Master 12 Advanced Projects | Interactive Python Dashboards with Plotly and Dash | Python 3: Deep Dive (Part 4 – OOP) |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 15.5 hours | 9.5 hours | 36.5 hours |
| Rating | 4.6 /5 | 4.7 /5 | 4.8 /5 |
| Student Enrollments | 9,095 | 44,008 | 24,101 |
| Instructors | Alexander Hagmann | Jose Portilla | Fred Baptiste |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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