Data science is a discipline that combines the use of statistics, computer science, and domain knowledge to extract valuable insights from data. Data science has become an essential tool for companies and organizations looking to improve their decision-making, innovation, and competitiveness. In Data analytics, you will learn the basic concepts of data science, the necessary tools for data analysis—such as the use of Python and its main libraries for data analysis, including NumPy, Pandas, Matplotlib, and Scikit-learn—and how to apply them to different practical cases. In addition, you will learn about the legal aspects you must consider to protect data and privacy.
Sector-specific
35 hours
Desarrollo de Big Data y Business Intelligence
Data Analytics
Data analytics is aimed at anyone who wants to get started in the world of data science and data analysis. No prior knowledge of programming or statistics is required, although a basic understanding of mathematics and logic is recommended. The course is designed to provide a theoretical and practical introduction to data science.
35 hours
Desarrollo de Big Data y Business Intelligence
Course objectives
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Understand what data science is and what its applications and benefits are.
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Learn to use Python, one of the most popular and versatile languages for data analysis.
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Get familiar with the main Python libraries for data analysis.
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Learn to use MongoDB and Hadoop, two systems that facilitate the management of unstructured or distributed data.
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Apply the acquired knowledge to different practical cases.
What does it prepare you for?
Data analytics prepares you to become a data scientist capable of handling, analyzing, and interpreting data using Python and other tools. By the end of the course, you will be able to carry out data science projects from scratch, using the most appropriate techniques and methodologies for each case. You will also be able to communicate the results of your analyses clearly and effectively, using charts, tables, or reports.
Teaching units
UNIT 1. INTRODUCTION TO DATA SCIENCE
What is data science?
Tools needed by the data scientist
Data Science & Cloud Computing
Legal aspects in data protection
UNIT 2. PYTHON AND DATA ANALYSIS
Introduction to Python
What Do We Need?
Python Libraries for Data Analytics
MongoDB, Hadoop and Python: Big Data Dream Team
UNIT 3. DATA ANALYTICS
Business Analytics Intelligence
Graph theory and social network analysis
Presentation of results