This tutorial takes you through the heart of data analysis. From the basic steps of downloading and uploading the publicly available dataset to our database and understanding how the data can be analysed for more meaningful insights.
Whether you're a budding data enthusiast, a professional looking to pivot into a data-centric role, or simply curious about how data analysis shapes the world around us, this article is your gateway to understanding and beginning your journey in the dynamic and increasingly indispensable field of data analysis.
What is Data Analysis
Data analysis is like taking a messy attic full of information and transforming it into a well-organized library of knowledge. It's the process of examining, cleaning, and interpreting raw data to uncover hidden patterns, trends, and insights. Using a mix of statistics, technology, and creativity, data analysts extract valuable information that empowers better decision-making, solves problems, and drives progress in various fields, from business and science to healthcare and entertainment.
In short, it's about turning numbers into stories that inform and inspire.
Today, we will see how we can convert numbers into actionable insights with the publicly available IMDb dataset.
Pre-Requisites:
- Signup to SingleStore to use the Notebooks feature
- Download the Netflix IMDb data (the csv file) from Kaggle.
Tutorial
Sign up to SingleStore and once you sign up, you will receive $600 worth free computing resources.
The first thing you need to do is to create a workspace.
Once you create a workspace, the next thing is to create a database to load our data.
Create a database
Go to the stages to load the data
This is where we upload our data.
Hope you have saved the Netflix IMDb data (the csv file) from Kaggle mentioned in the prerequisites section.
Upload the same csv file.
Once the csv file is loaded to the stage, go to the actions tab and from the dropdown, select to load the data into the database.
Load and generate the Notebook to work with data analysis.
What is SingleStore Notebooks?
Notebooks have become increasingly popular in the data science community as they provide an efficient way to explore, analyze and visualize data, making it easier to communicate insights and results. SingleStore's Notebook feature is based on the popular Jupyter Notebook, which is widely used in data science and machine learning communities.
One interesting fact about SingleStore Notebooks is that, they allow users to query SingleStore's distributed SQL database directly from within the notebook interface.
This is how the SingleStore Notebook looks like.
You don't have to do anything, everything gets created for you. You just need to run the code cell one by one.
Start running each code cell to load and visualize data.
It is time to add more colors to our analysis. Let's do some basic to advanced data analysis.
Heatmap Analysis
A common use for a heatmap in a dataset like ours (IMDb data) is to visualize the correlation between different numeric features such as imdb_score, runtime, release_year, and any other numerical columns you might have. Here's a generic example of how you can create a heatmap using Python's Seaborn and Matplotlib libraries.
The complete Notebook code is here: Data Analysis on Netflix Movies & Shows.
As you go through exploring the 'Netflix TV Shows and Movies' dataset, it's evident how data analysis can unlock a wealth of insights into the entertainment industry. This analysis not only provides a deeper understanding of Netflix's vast library but also demonstrates the power of data analysis in transforming raw information into meaningful knowledge. Whether you're a data enthusiast or a film buff, the skills and methods we've discussed can be your tools for uncovering hidden stories in any dataset.
Use SingleStore Notebooks for all your data analysis and exploring activities.
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