Discover Data Analytics & Machine Learning

Program Details

PROGRAM OVERVIEW

Acquire the complex skills of a 21st-century business strategist by working through a series of realistic projects. In the Data Analytics & Machine Learning program, you will learn how to apply methods of data analytics to clean and prepare a data set, learn about data mining using R and Python, and learn and use the most common Machine Learning algorithms to model complex business problems. You will also build and deliver convincing presentations to recommend your ideas.

MEET YOUR DATA ANALYTICS MENTOR

"During the Data Analytics Course we see how many students, at one point, begin to understand that what we teach them and everything we do at Ubiqum is aimed at training them to think like a professional data analyst."

Got a question?

What You’ll Learn

When I found Ubiqum and saw that the Data Analytics Course was based around real life projects I knew this was the right course for me

R and Python

The R and Python programming languages are the most straightforward languages in use by data scientists and the best languages to learn to develop good data models. They remain essential for almost all jobs in data analysis and data science.

At Ubiqum, you will become fluent in both and will be prepared to work in teams from the two main schools of data science: those who prefer R and those who prefer Python.

Both R and Python have tools that will help you in the pre-processing phases of a project — cleansing and completing data — and they will provide the most commonly used Machine Learning algorithms in classification, regression, visualization and time series analysis. These tools are essential to reach the position of data analyst you aspire to.

Python

dplyr

ggplot2

Amazon Web Services

Statistics

Machine Learning

Data Mining

Algorithms

Agile

Project 1

Develop regression and classification algorithms with Python to analyze customer behaviour and predict future profitability of Blackwell Electronics, a fictional electronics retailer. Collect your findings in a presentation and deliver it to a mock team of non-technical executives.

Predicting Profitability

Aurore Paligot

“In just one week in the Data Analytics Course, I've learned how to apply machine learning techniques in RapidMiner and received great advice from my mentor about how to present my data in an effective way. I can't wait to see what's next!”

Project 2

As a member of an Internet of Things startup, you have two problems to solve for two fictional clients:

1) Uncovering patterns of energy usage for cleaner energy design
2) Using wifi fingerprinting to determine indoor locationing with GPS. You'll use R to create complex visualisations, descriptive statistics, and predictive models for both problems.

Deep Analytics and Visualisation

Carles Castillejo

"I found this task quite challenging because of the following:

- Data wrangling (loading and cleaning)
- Dataset dimensions

But I got there in the end!"

Project 3

As an analyst for a major data analytics firm, your client, Helios, needs to understand how mobile devices are used by aid workers in developing countries. You will crawl large amounts of smartphone data from AWS to construct a predictive model for aid workers' device usage. You will visualize your model and present it.

Web Mining

Pau Pardo

"In this project, I got an idea of how Amazon Web Services (AWS) can be utilised in order to work with a huge amount of data (Big Data)."

Project 4

In the final month of the Data Analytics Course, you will work with the Program Owner to design your own project, for example learning Python to predict and visualise global economic patterns in financial markets.

Design Your Own Analysis

Juan Manuel

"I’m very interested in exploring how analysing data could help improve living conditions in cities, so I decided to look into how safe the streets of Barcelona are for pedestrians."

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A Day in Ubiqum

A TYPICAL DAY IN THE LIFE OF AN UBIQUM DATA ANALYST MIGHT LOOK SOMETHING LIKE THIS:

9:00

Plan your day's tasks in Trello

9.30

"Scrum" standup meeting