Thanks to this Postgraduate Diploma in Data Science You will gain in-depth knowledge, ranging from the fundamentals of data science to the practical application of key tools. The focus on databases relational and NoSQL data, together with data analysis in Python and R, provides a balanced overview. The inclusion of MongoDB and the use of programming languages such as programming languages such as Python and R enhances practical applicability. Furthermore, the focus on data pre-processing and processing, with an emphasis on ETL (Extraction, Transformation and Loading) and hypothesis testing will prepare you well for the challenges you’ll face in your data science projects. What’s more, you’ll be supported by a teaching team specialising in this field.
Data Science
Big Data and Business Intelligence Development
130 h
Introduction
Objectives
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Understanding the basics of Data Science and the essential tools.
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Explore further in relational databases, covering models, codes and indices.
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Explore NoSQL databases, by comparing the relations and applying the CAP theorem.
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Gain practical skills with MongoDB, from installation to advanced enquiries.
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Using Python for data analysis and to understand its integration with MongoDB and Hadoop.
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Mastering R as a tool for Big Data, exploring data types and predictive statistics.
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Learn pre-processing techniques and data processing, including ETL and hypothesis testing.
Table of Contents
TEACHING UNIT 1. INTRODUCTION TO DATA SCIENCE
1. What is data science?
2. Tools required for data scientists
3. Data Science & Cloud Computing
TEACHING UNIT 2. RELATIONAL DATABASES
1. Data model
2. Data types
3. Primary keys
4. Indexes
5. The NULL value
6. Other people’s passwords
7. Views
8. Data Definition Language (DDL)
9. Data Control Language (DCL)
TEACHING UNIT 3. NoSQL DATABASES AND SCALABLE STORAGE
1. What is a NoSQL database?
2. Relational databases vs NoSQL databases
3. Types of NoSQL Databases: The CAP Theorem
4. NoSQL database systems
TEACHING UNIT 4. INTRODUCTION TO A NOSQL DATABASE SYSTEM, MONGODB
1. What is MongoDB?
2. How MongoDB works and its uses
3. Getting started with MongoDB: Installation and Command Shell
4. Creating our first NoSQL database: Model and data insertion
5. Updating data in MongoDB: SET and UPDATE statements
6. Working with indexes in MongoDB to optimise data
7. Querying data in MongoDB
TEACHING UNIT 5. PYTHON AND DATA ANALYSIS
1. Introduction to Python
2. What do you need?
3. Libraries for data analysis in Python
4. MongoDB, Hadoop and Python: the Big Data Dream Team
TEACHING UNIT 6. R AS A TOOL FOR BIG DATA
1. Introduction to R
2. What do you need?
3. Data types
4. Descriptive and Predictive Statistics with R
5. Integrating R with Hadoop
TEACHING UNIT 7. DATA PRE-PROCESSING & PROCESSING
1. Data acquisition and cleansing (ETL)
2. Statistical inference
3. Regression models
4. Hypothesis testing
TEACHING UNIT 8. DATA ANALYSIS
1. Analytical Business Intelligence
2. Graph theory and social network analysis
3. Presentation of results
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