An introduction to Statistics, Python, Analytics, Data Science and Machine Learning. Sets up practitioners with working knowledge of whole field of data science, along with immediate practical knowledge of key analytical tasks.

This 5-day course is hands-on, practical and workshop based. It is the start of an experienced developer’s journey towards becoming a Data Scientist. If you are a software engineer, in business intelligence, or you are an SQL specialist, this is the course for you.

By attending this course you will learn how to become a professional Data Scientist. You're going to be able to demystify and understand the language around data science and understand the core concepts of analytics and automation. You'll also develop practical, hands-on, advanced skills in Python, targeted towards data analysis and Machine Learning so you can create sophisticated statistical models.

Target Audience

For fledging data science practitioners, and for IT professionals who wish to move to the exciting world of data analytics and machine learning.

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  • GCSE level mathematics or above. Alternatively, familiar and comfortable with logical and mathematical thinking
  • Familiar with basic knowledge of programming: variables, scope, functions
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Learning Outcomes

At the end of the course attendees will know:

  • Fundamental concepts of Data Science
  • Methodologies used in Machine Learning
  • Summary statistics and how to use statistical inference to analyse data
  • Hands on Python programming language for numerical analysis
  • Most used simple machine learning algorithms

At the end of the course attendees will be able to:

  • Speak the language of data scientists
  • Write Python programs to analyse data
  • Understand a Python program in the context of data analytics
  • Explore and visualise data using Python
  • Build working machine learning models
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Course Outline

01 Introduction to Data Science

  • Understanding Big Data challenges for storage and analytics
  • Identifying potential Big Data projects
  • Designing successful Data Science projects

02 Introduction to Machine Learning

  • Identify types of machine learning:
    • Supervised
    • Unsupervised
    • Reinforcement Learning
  • Identify use cases

03 Jupyter Notebook

  • Identify Anaconda and Jupyter
  • Work with Jupyter Notebooks
  • Practical Lab Activity

04 Python Fundamentals Review

  • Review of Python techniques:
    • Data Types and Assignment
    • Lists, Tuples, Strings, Sets and Dictionaries – and how to address from them
    • Selection and Iteration structures
    • Subroutine definitions
  • Practical Lab Activity

05 Introduction to Pandas and Numpy

  • Dataframes and how to address from them
  • Read from a CSV and exploration of documentation for reading from other sources
  • Dataframe methods and using the documentation
  • Practical Lab Activity

06 Exploratory Data Analysis

  • In the context of identifying appropriate Machine Learning methods:
    • Interpreting descriptive statistics using Pandas
    • Interpreting correlations and associations
  • Practical Lab Activity

07 Data Visualisation

  • In the context of identifying appropriate Machine Learning methods:
    • Interpreting visualisations using Pandas, Seaborn, and Matplotlib
    • Interpreting correlations and associations
    • Interpreting visualisations for EDA
  • Practical Lab Activity

08 Data Preparation

  • Data Preparation techniques in the context of selected Machine Learning methods:
    • Checking the quality of the data and understanding it's source
    • Identifying when to remove, replace, or retain missing data
    • Handling Imbalanced Data
    • Scaling and Normalisation requirements
  • Practical Lab Activity

09 Linear Regression

  • Identify appropriate situations for using Linear Regression
  • Use the Supervised Machine Learning workflow to create, evaluate, tune, and visualise a linear regression model
  • Practical Lab Activity

10 Logistic Regression

  • Identify appropriate situations for using Logistic Regression
  • Use the Supervised Machine Learning workflow to create, evaluate, and tune a logistic regression model
  • Practical Lab Activity

11 Decision Trees and Random Forests

  • Identify appropriate situations for using Decision Trees
  • Use the Supervised Machine Learning workflow to create, evaluate, tune, and visualise a Decision Tree model
  • Identify alternative classification models such as Random Forests and how to compare them
  • Practical Lab Activity

12 Clustering with K-means

  • Understanding and implementing the k-means clustering algorithm
  • Evaluate the model performance and select k
  • Practical Lab Activity
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Why choose QA

Google Cloud learning paths

Want to boost your career in Google Cloud? Click on the roles below to see QA‘s learning pathways, specially designed to give you the skills to succeed.

Required Star = Required
Certification = Certification
Data Scientist
Data Engineer
Developer Software Development Experience
Architect Enterprise Architecture Experience
Administrator Windows Administrator Experience

Frequently asked questions

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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.

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