Recognizing this market demand, educational institutions such as EAE Business School in Madrid offer specialized programs such as the Master in Big Data & Analytics, which, in addition to the use of tools and technologies such as SQL, Python and data science, also provides a very useful complementary broad business vision.
Understanding and relating Python and data science
Python and data science are deeply and multifacetedly related. Data science, which encompasses the extraction of useful knowledge and patterns from large volumes of data, requires a tool that is versatile and powerful, as well as accessible to professionals with diverse backgrounds. And Python meets these requirements perfectly, explaining its popularity and widespread adoption in the community. Several reasons underline the importance of Python for data science :
Ease of learning and clear syntax , which favors understanding and rapid code development. This feature makes it especially attractive for those who are new to programming, and for data scientists who often have to share and collaborate on projects with colleagues from different disciplines.
Broad ecosystem of libraries and frameworks dedicated to data science and machine learning. In addition to tools that simplify the manipulation and analysis of complex data, they allow data visualization in an intuitive way and using little code.
Community and support that actively contributes to its development and continuous improvement With learning resources available, tutorials, documentation, forums and even conferences. And a support network that means access to solutions for common problems, regular updates to libraries and tools, and opportunities to collaborate and learn from other professionals.
Flexibility and Scalability Admired for its flexibility, this language is capable of handling both small scripts and complex systems. A scalability that makes it suitable for Python and data science projects of any size.
Essential Python libraries for data science
Python libraries for data science are tools that any data scientist needs, as they offer various functionalities such as data manipulation or the implementation of complex machine learning algorithms.
We list some of the most used Python libraries in the field of data science:
NumPy. Numerical Python: Fundamental for handling vectors and matrices, necessary for numerical analysis.
pandas: Used for exploration and analysis of tabular and time series data.
Matplotlib: 2D graphics library that allows overseas chinese phone number data data visualization and creation of histograms, power spectra, bar graphs, error plots, etc.
seaborn: This Matplotlib -based library offers a very nice professional interface for creating attractive and informative statistical graphs. It also offers greater integration with pandas and support for more complex graphs with less code.
scikit-learn: A simple and efficient library for machine learning and statistical analysis in Python. It is widely used for the implementation of machine learning algorithms.
TensorFlow and PyTorch: Developed by Google and Facebook (Meta) respectively, these deep learning libraries allow you to build and train neural networks with efficiency and flexibility.
statsmodels: Python library that allows users to explore data, estimate statistical models, and perform statistical tests. Ideal for statistical analysis, economic modeling, regression analysis, and other statistics-related testing.
Plotly: Data visualization library that produces interactive charts and dashboards . It supports a wide variety of chart types and integrates well with other web libraries and frameworks.
SQL in data science
SQL, or Structured Query Language , although not often associated with data analysis or machine learning models like Python, plays a very important role in the world of data science. Its importance should not be underestimated, as it acts as a starting point for accessing, manipulating and querying data stored in relational databases.
If we look more closely at the role and importance of SQL in data science , we find that it offers us access to databases, data cleansing and preparation for analysis, and can also be used to quickly explore information, calculate descriptive statistics, identify correlations or detect outliers. SQL integrates with other data visualization tools and advanced analytics platforms, so users can perform queries and view results without leaving the tool.
Additionally, in the production environment, data science models often need to interact with data stored in SQL databases to make predictions or automate decisions based on new or real-time data.
SQL basics for working with structured data
These are the fundamentals of SQL data science that every professional working with structured data should know:
Understand the relational model of databases . This model organizes data into tables (also known as "relations"), made up of rows and columns. Each row represents a unique record, and each column represents a specific field in that record.
Basic SELECT queries . These allow users to extract data from one or more tables, and understand how to specify the columns to retrieve by filtering records using `WHERE`, and how to order the results with `ORDER BY`.
Aggregate functions such as `COUNT`, `SUM`, `AVG`, `MAX`, and `MIN` , which are used to perform calculations on a set of values and return a single value.
Unions (JOIN) that make it possible to combine rows from two or more tables based on a column that is related to each other. A very necessary command for working with data that is distributed across multiple tables.
Subqueries , which are SQL queries within another query, allowing more complex operations to be performed in multiple steps.
Data manipulation (INSERT, UPDATE, DELETE) to extract and modify data using the specified commands.
Transaction controls that make it easy for users to handle database changes safely, and ensure that operations are completed successfully or rolled back in case of error.
Creating and managing tables . SQL is also used to define the structure of databases by creating tables, modifying existing tables, and deleting tables.
Mastering these fundamentals of SQL data science opens up a world of possibilities for working with structured data, whether in performing basic data analysis, or in developing complex database management systems.
