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 organisations seeking 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 examples. In addition, you will learn about the legal aspects you must take steps to protect data and privacy.
Sector-specific
Big Data and Business Intelligence Development
35 hours
Data Analytics
Big Data and Business Intelligence Development
35 hours
Introduction
Objectives
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Understand what data science is and what it is 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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Familiarise yourself with the main Python libraries for data analysis.
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Learn how to use MongoDB and Hadoop, two systems that facilitate the management of unstructured or distributed data.
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Apply the knowledge you have gained to different practical examples.
Table of Contents
UNIT 1. INTRODUCTION TO DATA SCIENCE
What is data science?
Tools required by the data scientist
Data Science & Cloud Computing
Legal aspects of data protection
UNIT 2. PYTHON AND DATA ANALYSIS
Introduction to Python
What Do We Need?
Python Libraries for Data Analytics
MongoDB, Hadoop and Python: The Big Data Dream Team
UNIT 3. DATA ANALYTICS
Business Analytics Intelligence
Graph theory and social network analysis
Presentation of results
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