The Data Science and the Artificial Intelligence (AI) have emerged as cornerstones of the digital age, transforming the strategic decision-making in companies. The daily data explosion has led to the the urgent need to understand, analyse and extract valuable information. This Postgraduate Diploma in Data Science and Artificial Intelligence offers a a thorough understanding of the subject, from the An Introduction to Data Science until the advanced analysis and real-time data processing. Throughout the programme, you will acquire the the skills needed to solve complex problems in the field of data and AI, using key programming languages such as Python and R.
Cross-cutting
AI Academy
50 hours
Data Science and Artificial Intelligence
AI Academy
50 hours
Introduction
Objectives
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Understanding Data Science and their main applications.
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Master relational and NoSQL databases.
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Using MongoDB for the effective management of NoSQL data.
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Using Python in the data analysis and Big Data.
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Master R for descriptive and predictive statistics.
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Carry out pre-processing and advanced data analysis.
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Presenting results effectively, both in data analysis and social media.
Table of Contents
TEACHING UNIT 1. INTRODUCTION TO DATA SCIENCE
What is data science?
Essential tools for data scientists
Data Science & Cloud Computing
TEACHING UNIT 2. RELATIONAL DATABASES
Data model
Data types
Primary keys
Indexes
The NULL value
Other people’s passwords
Views
Data Definition Language (DDL)
Data Control Language (DCL)
TEACHING UNIT 3. NoSQL DATABASES AND SCALABLE STORAGE
What is a NoSQL database?
Relational Databases vs Non-SQL Databases
Types of NoSQL databases: the CAP theorem
NoSQL Database Systems
TEACHING UNIT 4. INTRODUCTION TO A NOSQL DATABASE SYSTEM, MONGODB
What is MongoDB?
How MongoDB works and its uses
Getting started with MongoDB: Installation and command shell
Creating our first NoSQL database: Model and data insertion
Updating data in MongoDB: SET and UPDATE statements
Working with indexes in MongoDB for data optimisation
Querying data in MongoDB
TEACHING UNIT 5. PYTHON AND DATA ANALYSIS
Introduction to Python
What do you need?
Libraries for data analysis in Python
MongoDB, Hadoop and Python: the Big Data Dream Team
TEACHING UNIT 6. R AS A TOOL FOR BIG DATA
Introduction to R
What do you need?
Data types
Descriptive and Predictive Statistics with R
Integrating R with Hadoop
TEACHING UNIT 7. DATA PRE-PROCESSING & PROCESSING
Data acquisition and cleansing (ETL)
Statistical inference
Regression models
Hypothesis testing
TEACHING UNIT 8. DATA ANALYSIS
Business Analytics
Graph theory and social network analysis
Presentation of results
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