Fundamentos para Análisis de Datos en NumPy y Pandas

How to improve recommendations and viewing pattern analysis?

In today's streaming media environment, improving recommendations and understanding what types of content are most popular is critical to a company's success. Analyzing viewing patterns of millions of users represents a huge challenge due to the sheer amount of data that must be processed on a daily basis. Fortunately, there are powerful tools in Python that facilitate this process: Our allies are NumPy and Pandas, libraries that allow you to handle large volumes of data efficiently and accurately.

What is NumPy and how does it help in data analysis?

NumPy is essential when working with large data sets, as it allows you to perform high-performance mathematical and statistical operations. By using NumPy, you can efficiently handle operations on arrays, which is crucial in large-scale analysis. This translates into:

• Speed and efficiency: NumPy speeds up data processing thanks to its vectorized operations that are much faster than standard Python lists.
• Ease of use: It handles multidimensional arrays and matrices, which simplifies the manipulation and transformation of complex data.

Why use Pandas?

Pandas is a tool built on top of NumPy that makes it easy to manipulate and analyze data quickly and easily, especially when working with tabular data. Here are some key benefits:

• Tabular data manipulation: ideal for working with spreadsheet or database-style data, allowing operations such as filtering, grouping and pivoting.
• Data analysis: Provides functions such as DataFrames, which facilitate the analysis and understanding of your data for fact-based decision making.

Pandas not only improves efficiency, but also opens doors to new job opportunities in data analytics, business intelligence, machine learning and data science.

How will we work and learn in this course?

Throughout the course, we will develop a realistic project analyzing sales data from an online store. This project will serve to put into practice what you have learned and will be a valuable addition to your professional portfolio.

What will you learn in this course?

1. Data manipulation: Learn how to manage and modify large sets efficiently.
2. Statistical analysis: Perform detailed statistical analysis with ease.
3. Data cleaning and processing: Be prepared to do further analysis with clean and processed data.
4. Effective visualization: Communicate results clearly through effective visualizations.
5. Valuable information extraction: Drive decisions with key information.
6. Task automation: Save time by automating repetitive analysis tasks.

Working environments and initial setup

For the course, we will use Google Collaboratory, but if you prefer, you can use other environments such as Visual Studio Code. Make sure you have the necessary libraries installed: NumPy and Pandas. Here we show you how to do it:

# NumPy and Pandas installation!pip install numpy pandas

After installation, import the libraries into your working environment:

# Importing librariesimport numpy as npimport pandas as pd.

Remember that the standard notation is to import NumPy as np, facilitating its use throughout the analysis.

This course will not only provide you with technical skills but will also prepare you to face real challenges in data analysis. Keep learning with enthusiasm, hone your skills and get ready to excel in the job market!