Section 1.1: Daily Stories Overview

1️⃣ December 1 — Netflix Data Exploration

Today, we’ll analyze our Netflix viewing habits utilizing Python Pandas and Matplotlib. Together, we will gather, transform, and visualize the data. Enjoy!

2️⃣ December 2 — Web Scraping Restaurants

It’s web scraping day! We’ll extract data from Starbucks, Subway, and McDonald's locations in Berlin. Happy crawling!

3️⃣ December 3 — Address to Geolocation

We have a dataset containing addresses of various restaurants in Berlin. Today, we’ll enrich it with geolocation data by utilizing an API to fetch latitude and longitude.

4️⃣ December 4 — Visualize Data on a Map

Using Python Folium, we will represent a list of restaurant addresses in Berlin as markers on a map. Let’s code!

5️⃣ December 5 — Live Stock Market Visualization

In this session, we’ll leverage the yfinance API to obtain live stock market data, which we will visualize on an interactive chart using Python Plotly.

6️⃣ December 6 — Premier League Twitter Activity

Today, we’ll generate a dataset from live Tweets about the Premier League, employing Python Tweepy and Pandas.

7️⃣ December 7 — Twitter Visualization & Analytics

Today focuses on visualizing Twitter activity from the top six Premier League clubs. Happy plotting!

8️⃣ December 8 — Python Sankey Diagrams

Sankey Diagrams are excellent for visualizing data flows. We’ll create our own using Python Plotly today.

9️⃣ December 9 — Crawl Nintendo Game Reviews

What’s the best Nintendo game? Today, we’ll scrape game reviews from Mobygames.com using Python BeautifulSoup. Enjoy the crawl!

1️⃣0️⃣ December 10 — The Best Nintendo Games Ever

Following our scraping of Nintendo game reviews, we will analyze and visualize the ratings in today’s story.

1️⃣1️⃣ December 11 — Flight Data Generation

We’re kicking off a new project on generating aircraft location data. This will resemble what you might find on Flightradar24, albeit on a smaller scale. We’ll learn about Python, Kafka, and important data architecture principles, including separation of concerns and decoupling.

1️⃣2️⃣ December 12 — Flight Data Live Visualization

We’ll consume airplane location data today to create a live map displaying current flights and their positions.

The first video discusses the process of web scraping with Python Selenium, providing practical insights into gathering data from various sources.

1️⃣3️⃣ December 13 — Medium Stats & Earnings

As we reach the midpoint of our journey, we'll analyze the Medium statistics, such as views and earnings, using Python Pandas and Matplotlib.

1️⃣4️⃣ December 14 — Web Scraping Soccer World Cups

We’ll utilize Python BeautifulSoup and Pandas to scrape data from Wikipedia’s World Cup pages.

1️⃣5️⃣ December 15 — Visualizing Soccer World Cup Data

Using the dataset we collected from Wikipedia, we will explore and visualize the soccer data with Pandas and Matplotlib.

1️⃣6️⃣ December 16 — S&P500 Bear Market Analysis

As global stock markets face challenges, we’ll analyze the S&P 500 index’s performance during this bear market compared to prior ones, using Python Pandas and Matplotlib.

1️⃣7️⃣ December 17 — COVID-19 Analysis

Each COVID-19 case is significant, and we will examine the trends of cases and deaths across various European countries using Python Pandas, Plotly, and Matplotlib.

1️⃣8️⃣ December 18 — Supply Chain Control Tower

Due to unforeseen circumstances, I will share a remarkable article by Samir Saci on the Supply Chain Control Tower, ensuring your learning continues.

1️⃣9️⃣ December 19 — Cool Visualizations Beyond Pie Charts

While I’m still recovering, I recommend an article by Boriharn K. showcasing nine innovative visualizations that surpass simple pie charts.

2️⃣0️⃣ December 20 — Can AI Be a Data Scientist?

Another insightful article by Salvatore Raieli explores the potential of AI in the role of a data scientist.

2️⃣1️⃣ December 21 — Interactive Weather Visualization

I’m back and excited to recommend an article by Will Norris on creating interactive weather visualizations using Python Plotly.

2️⃣2️⃣ December 22 — Bokeh Data Visualizations

Today’s recommendation is from Payal Patel, who presents an excellent read on creating data visualizations with Bokeh in Python.

2️⃣3️⃣ December 23 — Anomaly Detection

Today, I'll share Peter Mqoaie’s article on Anomaly Detection with Python, a must-read for data enthusiasts.

2️⃣4️⃣ December 24 — Medium Earnings & Stats

We’ve reached the final chapter! Let’s review the Medium statistics and earnings accrued throughout this series. This journey has been incredible, and I am grateful to everyone who joined and supported me. I hope you’ve learned as much as I have! 🙏

The second video delves into analyzing Premier League Twitter activity, offering a unique perspective on data collection and visualization.