Data Science and Artificial Intelligence in Practice
The growth of data has been exponential over the last decade. Ultimately there is a race for companies and even individuals to analyze this data to stay ahead. Drawing actionable insights and making the right business decisions is imperative to maintain a competitive edge. In this course, we explore the principles of Data Science and Machine Learning, providing you with practical models for you to take away and implement when back at your desk.
6th November 2017. 9.00am – 5.00 pm.
Over the day you will achieve all the learning objectives in an easy to follow and highly practical course.
- 1. Data Science in Finance
- Know the reasons for data science within finance.
- Identify the various data sources within finance.
- Apply the relevant data analysis questions.
- Know the 3V’s of ‘Big data’
- Know the common mistakes made in data analysis
- Know the features of common data sources in finance
- Apply principles of tidy data to ensure data is ready for analysis
- 2. Big data
- Understand the impact of the 3Vs on big data
- Know the challenges of big data in finance
- Identify the difference between the structure of data
- Know the principles of big data
- Know the key differences between SQL and NoSQL
- Understand the Hadoop HDFS framework
- Apply NoSQL storage methodologies
- Know the elements of the Hadoop ecosystem
- Know the key features of tick database using MongoDB
- 3. Machine Learning
- Know the definition of machine learning (ML)
- Understand the process of building a machine learning model
- Know the key uses of ML within finance
- Understand the types of machine Learning
- Apply a scatter graph to compare features of a data set
- Identify suitable features of a given data set
- Know the key unsupervised ML algorithms
- Calculate classifications using Bayes theorem
- Apply a decision tree to a set of training data
- Identify the correct decision surface for a set of data
- Identify features and labels of a data set
- Understand the key principles in unsupervised learning
- 4. R programming
- Know the main uses for R in financial data analysis
- Know how to navigate through RStudio
- Understand how to create and assign variable
- Apply basic arithmetic and logical operations
- Know how to create data frames
- Know how to set a working directory
- Understand how to import data into R
- Calculate a comparison table of features
- Know how to create a decision tree on a set of data
- Calculate a ML model given a set of training data
- Understand how to export data from R
If you are unable to attend the course in person, you will still be able to take the course by attending virtually. You simply dial in, listen, ask questions and participate as if you were in the classroom.
The cost to attend the course virtually is $500. This is a saving of over 20% on the equivalent classroom course. It includes a USB stereo PC headset with noise cancelling microphone and inline controls for you to use during the course. Attending virtually means that there is a saving in both cost and time compared to attending in person.
Attending in Person
The cost of the course is £595. This includes all training materials, refreshments and lunch.
Book before 1st September 2017 to receive an “Early Bird” discount of 20% off the listed price. Enter “early bird” at checkout.
- Cancellations and refunds:
- 2 months notice – 100% refund
- 1 months’ notice – 50% refund
- Less than one month – 20% refund
- No refunds for attendees having bought at the early bird price
- You may change the name of the attendee up to the date of the course
- The make and model of the headset depends on availability. CFT reserves the right to supply a comparable replacement headset.
- You are expected to check the compatibility of your equipment for virtual attendance before the date of the course. No refunds will be given for failure to attend due to technical reasons.