Overview

This five day course is aimed at those who are familiar with data analysis and are interested in learning about how Data Science, Analytics, Machine Learning, and Artificial Intelligence (AI) can be used to yield value from data assets.

This course will be of interest if you are interested in developing your own skills to move from analytics to Data Science, or if you are working with Data Scientists and want to learn more about what’s possible.

You will be introduced to key concepts and tools for use in Data Science, including typical Data Science Project lifecycles, potential applications & project pitfalls, relevant aspects of data governance and ethics, roles and responsibilities, Machine Learning and AI model development, exploratory analysis and visualisation, as well as techniques and strategies for model deployment.

Throughout the course you will engage in activities and discussions with one of our Data Science technical specialists. Theoretical modules are complimented with comprehensive practical labs.

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Prerequisites

We recommend that delegates are familiar with fundamental data science concepts, such as those found on our QA Introduction to Data Science for Data Professionals, as well as programming techniques found in QA Python for Data Handling. You should also have an interest in developing Data Science within your organisation or in becoming a Data Scientist.

Target Audience

Members of the audience are required to have a some technical expertise such as table structure, working with tabular data in Python, and simple data analysis.

They may be Mid/Senior Leadership seeking a greater understanding of how to implement Data Science within their organization.

They may come from other technical backgrounds such as Data Analysts, Software Developers, and Data Engineers who either work with Data Scientists or are using this course in their journey towards training as a Data Scientist.

In the latter case, audience members may ask for recommendations for their next steps in training towards becoming Data Scientists. We recommend the following refreshed courses which are due to launch in 2023 and 2024 in this suggested sequence:

  • Statistics for Data Analysis in Python
  • Time Series and Forecasting with Python
  • Maths and Statistics for Data Science with Python
  • Practical Big Data Analytics (with Python and Spark)
  • Generative AI Essentials
  • Fundamentals of Deep Learning with Python (followed by selected Python & NVIDIA training)
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Delegates will learn how to

  • Introduction to Data Science & Machine Learning
  • Introduction to Python for Data Science
  • Descriptive & Inferential Statistics with Python
  • Preprocessing Data for Analysis
  • Supervised Learning: Regression
  • Supervised Learning: Classification
  • Model Selection & Evaluation
  • Unsupervised Learning
  • Ethics for Data Scientists
  • Deploying Models & Insights
  • Where to Go Next
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Outline

Introduction to Data Science & Machine Learning

  • Explain the role of the Data Scientist and the skillset it requires
  • Describe common application areas of Data Science, and examples of its usage in industry
  • Outline the Data Science process detailed in the CRISP-DM methodology
  • Detail the characteristics of problems which Data Science can be used to solve
  • Define how to evaluate the success of a Data Science Project

Introduction to Python for Data Science

  • Understand why notebooks are often used in Data Science projects
  • Use Python and associated libraries to manipulate datasets.
  • Describe why virtual environments are used
  • Visualise data using Python

Descriptive & Inferential Statistics with Python

  • Understand the role that descriptive and inferential statistics play in Data Science
  • Use measures of central tendency, variation, and correlation to understand data
  • Use hypothesis tests to establish the significance of effects
  • Use statistical visualisations to understand data distributions
  • Describe the role of Exploratory Data Analysis in a Data Science project

Preprocessing Data for Analysis

  • Appropriately process duplicated data, missing values & outliers
  • Understand the importance of scaling, encoding, and feature selection
  • Describe the importance of training, testing & validation sets
  • Engineer novel features to analyse

Supervised Learning: Regression

  • Describe regression in the context of machine learning
  • Build simple and multiple linear regression models
  • Understand non-linear regression approaches
  • Evaluate & compare regression models

Supervised Learning: Classification

  • Describe classification in the context of machine learning
  • Build simple and multiple logistic regression models for classification
  • Build Decision Tree & Random forest models for Classification
  • Evaluate and compare classification models

Model Selection & Evaluation

  • Understand how to choose the best model for regression and classification problems
  • Consider tests & baselines that can be used to evaluate model performance & behaviour
  • Evaluate 'how good is good enough'

Unsupervised Learning

  • Describe clustering and dimensionality reduction in the context of machine learning
  • Apply and evaluate KMeans clustering
  • Apply and evaluate dimensionality reduction techniques

Ethics for Data Scientists

  • Be aware of the legislation and standards Data Scientists must adhere to
  • Discuss the importance of legal, ethical, and moral considerations in Data Analytics projects and identify applicable UK legislation for which employees should receive training
  • Discuss ethical considerations for data handling
  • Recognise ethical considerations in examples of machine learning, deep learning, and AI

Deploying Models & Insights

  • Understand how analytical models can be deployed
  • Evaluate how best to deploy a given model
  • Define checks which can be used to prevent model failures
  • Use Python and associated libraries to deploy a machine learning model
  • Describe which metrics can be used to monitor deployed machine learning models

Where to Go Next

  • Understand the role of deep learning in modern Artificial Intelligence
  • Know which qualifications and professional memberships can benefit data scientists
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Why choose QA

Dates & Locations

Frequently asked questions

See all of our FAQs

How can I create an account on myQA.com?

There are a number of ways to create an account. If you are a self-funder, simply select the "Create account" option on the login page.

If you have been booked onto a course by your company, you will receive a confirmation email. From this email, select "Sign into myQA" and you will be taken to the "Create account" page. Complete all of the details and select "Create account".

If you have the booking number you can also go here and select the "I have a booking number" option. Enter the booking reference and your surname. If the details match, you will be taken to the "Create account" page from where you can enter your details and confirm your account.

Find more answers to frequently asked questions in our FAQs: Bookings & Cancellations page.

How do QA’s virtual classroom courses work?

Our virtual classroom courses allow you to access award-winning classroom training, without leaving your home or office. Our learning professionals are specially trained on how to interact with remote attendees and our remote labs ensure all participants can take part in hands-on exercises wherever they are.

We use the WebEx video conferencing platform by Cisco. Before you book, check that you meet the WebEx system requirements and run a test meeting (more details in the link below) to ensure the software is compatible with your firewall settings. If it doesn’t work, try adjusting your settings or contact your IT department about permitting the website.

Learn more about our Virtual Classrooms.

How do QA’s online courses work?

QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course. When you book a QA online learning course you will receive immediate access to it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.

All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.

Learn more about QA’s online courses.

When will I receive my joining instructions?

Joining instructions for QA courses are sent two weeks prior to the course start date, or immediately if the booking is confirmed within this timeframe. For course bookings made via QA but delivered by a third-party supplier, joining instructions are sent to attendees prior to the training course, but timescales vary depending on each supplier’s terms. Read more FAQs.

When will I receive my certificate?

Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.

Contact Us

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