## Develop your skills before you commit to a full degree

Studying a single unit in data science with Monash Online equips you with the latest knowledge in managing, analysing, and visualising data in just six weeks. Hand pick your preferred data science units, with access to curriculum delivered and assessed by our academic staff.

Why Monash Online single units?

Flexible– Offered 100% online and taught in six weeksCredit-bearing– May be used as credit for those considering a Graduate Diploma of Data ScienceCredible– Learn from Monash’s leading academic staff and researchersHands-on support– Dedicated Student Success Advisor guiding you through your studiesPostgraduate curriculum– Derived from Monash Online’s Graduate Diploma of Data Science

##### Ideal for:

- Working out whether data science could be a new career path for you.
- Hand-picking data science skills or knowledge you need immediately to boost your current expertise.
- Staying relevant in an ever changing industry, with a unit from a leading university.

##### Do a postgraduate course if:

- You are dedicated to the long-term investment of becoming a fully-qualified data scientist.
- You need a formal tertiary-level qualification to change careers.
- You need a formal qualification to gain recognition for your experience or move to a higher level of seniority.

## FIT9133 Programming foundations in Python

This unit introduces you to program design and the Python language. You’ll learn ways of using algorithms to solve computational problems.

View unit## MAT9004 Mathematical foundations for data science

This unit includes the mathematical topics fundamental to data science, computing and statistics.

View unit### Introduction

If you have a background in programming and databases, and want to evolve into a new role in data science, this foundational unit can be your first step.

**Prerequisites:
**FIT9131 – Programming foundations in Java

**OR**FIT9133 – Programming foundations in python,

**AND**FIT9132 – Introduction to databases

**Intakes:** June 2019

This unit explores the many facets of working with data. It includes:

- The handling of data
- Styles of data analysis
- Working with data as part of a business model
- Reviewing case studies of successful data analysis and exploration.

**Key Outcome:** To understand the role of data in organisations and apply basic tools to analyse and manage data.

### Programming

Delve into data analysis and program design with the professional skills of a data scientist. Programming units focus on: solving problems with programs like Python; machine learning; data visualisation; and data modelling

**Prerequisites:
**FIT9133: Programming foundations in Python &

**MAT9004: Mathematical foundations for data science**

**March & August 2019**

Intakes:

Intakes:

This unit explores basic probability distributions, random number generation, and estimation methods. It reviews the statistical modelling foundations that underlie the analytics aspects of data science.

**Key Outcome:** To perform exploratory data analysis, construct and implement models for various statistical analysis, as well as interpreting their results.

**Prerequisites:
**FIT5197: Modelling for data analysis

**Intakes:** June 2019

This unit introduces machine learning and the major kinds of statistical learning models and algorithms used in data analysis. You’ll be presented with foundational concepts in machine learning and statistical learning theory, and how model complexity interplays with a model’s performance on unobserved data.

**Key Outcome:** To describe, assess and develop statistical machine learning. You’ll also be able to scale typical statistical learning algorithms to learn from big data.

**Prerequisites:** No. However, FIT5197: Modelling for data analysis strongly recommended

**
Intakes:** March 2019

This unit introduces statistical and visualisation techniques for the exploratory analysis of data. It covers:

- The role and limitations of data visualisation
- The visualisation of qualitative, quantitative, temporal and spatial data
- Creating effective data visualisations with R.

**Key Outcome:** To perform exploratory data analysis using a range of visualisation tools, and to critically evaluate and interpret data visualisation. You’ll also be able to implement interactive data visualisations using python, R and other tools.

**Prerequisites: **FIT9133: Programming Foundations in Python as a pre-requisite.

**Intakes:** March and August 2019

This unit introduces you to program design and the Python language. You’ll learn ways of using algorithms to solve computational problems.

**Key Outcome:** The ability to use Python and recognise the relationship between problem description and design, as well as the skills required to investigate and evaluate different strategies for algorithm development.

**Prerequisites:
**FIT5197: Modelling for data analysis

**Intakes:**October 2019

This unit will give you the analytical and data modelling skills you’ll need to be a data scientist or business analyst. You’ll be introduced to various data analysis techniques and learn how to use them to address characteristic problems.

**Key Outcome:** To analyse data with a range of tools, and evaluate the limitations and benefits of different methods. You’ll also be able to design solutions to real-world problems, evaluate and then communicate the results.

### Databases

The effective management of databases is vital to both small businesses and large enterprises. Our short database units give you a deeper understanding of the inner workings of a database, and how to manage large data with technology, software tools and techniques.

**Prerequisites: **No

**Intakes:** May & October 2019

This unit reviews organisational data management through relational database technology, including relational model analysis and design.

**Key Outcome:** The ability to develop a database and construct queries to meet user requirements. Also, to have the skills to explain motivations and theories that underpin both processes.

**Prerequisites:
**FIT9132: Introduction to databases &

FIT9133: Programming foundations in Python

**Intakes:**May 2019

This unit teaches about working with different kinds of data, documents, graphs, and spatial data. Distributed processing is introduced using Hadoop and Spark technologies, including streaming, graph processing and using NoSQL. Programming assignments are generally done in Spark, Linux, and similar shell-like environments.

**Key Outcome:** To apply spatial data methods, distributed processing, and large-scale graph, vector and document processing methods.

**Prerequisites:
**FIT9132: Introduction to databases

**FIT9133: Programming Foundations in Python as a pre-requisite.**

**Intakes:** August 2019

This unit introduces the software tools and techniques for data engineering. It covers traditional methods of data processing, introduction to distributed databases, and the handling and processing of big data.

**Key Outcome:** Use and explain distributed databases, write and interpret SQL queries and identify and assess big data concepts and technologies.

**Prerequisites:
**FIT9133: Programming foundations in Python

**May & October 2019**

Intakes:

Intakes:

In this unit you’ll develop the skills required to prepare raw data for analytics, including data cleansing and pre-processing. Python and the Pandas environment will be used as part of this course.

**Key Outcome:** To analyse and assess data, resolve data quality issues as well as integrated data sources, and document the entire wrangling process.

### Maths and stats

The core of data science is a long sequence of numbers and algorithms. Maths and stats is the basis of this field and you will need to be an expert in numbers.

**Prerequisites: **No

**Intakes:** June 2019

This unit includes the mathematical topics fundamental to data science, computing and statistics, including:

- Linear algebra
- Trees and other graphs
- Principles of elementary probability theory
- Fundamental concepts of calculus in one and several variables
- Counting in combinatorics

**Key Outcome:** To explain and apply different principles in data science including tree graphs, combinatronics, elementary probability, and more. Also, to demonstrate basic knowledge and skills of linear algebra.