Thanks to this Course in Data Science and Artificial Intelligence you’ll be able to discover the world of data science in a world as vast as that of information. Once the course is complete, students will have the knowledge to form opinions on data-related content, as well as being familiar with more tools than they were before starting the course. Not to mention that, thanks to the data analysis, students will learn what they should and should not do when managing them. Finally, students will have the opportunity to study the business whom they represent, or for whom they work, so that they can implement changes – or at the very least propose them – with a view to offering services, improving them, and attracting and retaining more customers, which is what really matters.
Data Science and Artificial Intelligence
Big Data
200 hours
Introduction
Objectives
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To gain first-hand insight into everything to do with data in the data science.
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Find out about cases in the sector, to have the opportunity to learn about its strengths and weaknesses.
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To have the opportunity to find out about updates within the sector and how they have come to public attention.
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Understanding the structure that needs to be followed in order to carry out the plan for data management.
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To assimilate the online and offline activities which can be applied in this sector.
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