Data Science and Machine Learning For Beginners

Key Learning Objectives

• Develop basic yet practical working knowledge in data science and machine learning

• Master key concepts and techniques to build machine learning models using big datasets

• Discover how to structure your dataset in order to build advanced analytics solutions

• Explore the latest use cases and applications of machine learning

• Develop an understanding of a programming languages for data science

• Learn how experts collect, wrangle and manipulate data for effective data science

• Learn to build and manage a successful data science and analytics capability in your organization

• Develop technical and soft skills required to manage a team of data scientists

About Course

If data is the new ‘Oil’ of the 21st century, data science and machine learning are the ‘engine’ behind it. Learning how to apply and manage data science and basic machine learning techniques is becoming another important skill in the data-driven economy.

This course provides the latest concepts, tools and techniques to build and influence the development of a successful data science and machine learning capability. Delivered through an interactive approach utilising the latest tools, participants of this course are exposed to basic techniques of data science, algorithms and machine learning, basic applied statistics and some of the most utilised cloud and open source tools that powers the world of advanced big data analytics.

Participants are also exposed to the latest thinking in data strategy and managing data science and analytics teams and projects.

Who Will Benefit

Anyone who wants to understand the role data science and analytics plays in driving competitive advantage in teams and organisations but have not had any (or major) exposure to the field.

It can also be beneficial to those who want to pursue a change in career and work more closely with advanced analytics, data science and machine learning capacity but have not had a chance to figure out how to go about it.

Lastly, it can benefit anyone who works (or manages a team) in a business or technical role and uses data to answer questions, solve problems or build data-driven solutions but want to have a different perspective in the field.

Outline

DAY ONE

Data science and machine learning history and fundamentals

• Explore the fundamentals and historical developments of data science and machine learning

• Showcase and explain in layman’s terms the latest trends and hot topics in analytics

• Discuss and define a variety of concepts and use cases in data science and machine learning

Organisational structures, roles and technology considerations in data science

• Learn about how organisations are structuring themselves for analytics

• Introduce and clarify roles, tools, techniques and tactics needed in data science

• Discuss job opportunities and skills needed in the marketplace

Building and managing a successful data science capability in your organisation

• Making sense of your organisation’s analytics capability and maturity

• Plotting a roadmap from business strategy to data science realization

• Understanding of what makes organisations do data science and machine learning right

Data visualisation and action-oriented data storytelling to communicate results

• Contextualise and define concepts in data visualisation for data science and analytics

• Showcase and explain in layman’s terms the latest trends and hot topics in data visualization

• Learn about the latest tools and techniques to create compelling visual data storytelling

DAY TWO

Fundamentals of applied statistics for data science

• Explore introductory concepts in statistics and probability

• Contextualise differences between supervised and unsupervised learning, regression vs classification models, etc.

• Explore sampling, bias, data quality and other issues affecting machine learning models

Introduction to descriptive and predictive analytics in practice

• Explore and work through an exploratory data analysis (EDA) exercise

• Introduce and apply a machine learning model to a basic and simple dataset

• Utilise a popular business tool to interpret and summarise the results of a predictive model

Interactive and scalable data science and analytics solutions

• Introduce and work through a basic supervised learning model using R and Jupyter Notebooks

• Introduce and work through a basic unsupervised learning model using R and Jupyter Notebooks

• Introduce and work through a basic supervised learning model using a machine learning solution in the cloud

Data science and machine learning in action

• Revisit main themes, tools, techniques and strategies

• Build a practical action plan to apply learnings to your organization

• Group discussion, final reflections and insights