About the Course

In today's data-driven landscape, the significance of data as a pivotal asset for any thriving business cannot be overstated. Data serves as the compass that guides a business through its journey, illuminating its current position and charting the course for its future. Beyond the realm of mere insights, data wields the power to shape decision-making processes and impact day-to-day operations, making it an invaluable resource.

To harness the potential of data, a proficient and adaptable workforce is essential, equipped with the skills to analyse, comprehend, manipulate, and effectively communicate data within a structured and replicable framework. In essence, the modern business environment demands the presence of adept data science practitioners who can transform data into actionable intelligence.

This 5-day Data Science and Machine Learning training course represents a transformative opportunity for individuals seeking to make their mark in the business world. It empowers the delegates with the knowledge and skills to bridge the data and business value gap. By mastering the principles and practices of data science, delegates will be poised to contribute significantly to their organisation's success.

In conclusion, recognising data as a vital asset and embracing the capabilities of data science practitioners is essential for businesses to thrive in today's data-centric world. It allows delegates to be at the forefront of this transformative journey, where data becomes the cornerstone of informed decision-making and sustainable business growth.

Core Objectives

The delegates will achieve the following objectives:

• Understand the use of data science principles to address business issues
• Apply the extract, transform, and load (ETL) process to prepare datasets
• Know how to use multiple techniques to analyse data and extract valuable insights
• Design a machine learning approach to address business issues
• Train, tune, and evaluate classification models
• Train, tune, and evaluate regression and forecasting models
• Train, tune, and evaluate clustering models
• Acquire data science projects by presenting models to an audience, putting models into production, and monitoring model performance

Training Approach

The delegates will be taught through learning techniques that ensure maximum understanding, comprehension, and retention of the information presented. The training methods will vary depending on their needs, from front-end to blended learning. It is divided through information received, participation, and Learning by Doing to provide the highest level of apprehension and retention of the presented material. They will be working on case studies and doing a hands-on project, and at the end of the training course, they will be assessed to determine the level of knowledge they have retained.

The Attendees

This training course is designed for business professionals who leverage data to address business issues. The delegates will have several years of experience with computing technology, including some aptitude in computer programming. However, there are more organisational roles that this programme targets. A prospective delegate might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analysing, and manipulating data through code or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Finally, any professionals who want to learn how to extract insights from their work more effectively and leverage that insight in addressing business issues, thereby bringing more excellent value to the business.

Likewise, it will be valuable to the professionals but not limited to the following:

• Programmer
• Data Analyst
• IT Managers
• Data Scientists
• Anyone who would like to become certified in Artificial Intelligence

Daily Discussion

DAY ONE: ADDRESSING BUSINESS ISSUES WITH DATA SCIENCE

• Initiate a Data Science Project
• Formulate a Data Science Problems
• Extracting, Transforming, and Loading Data
• Extract Data
• Transform Data
• Load Data

DAY TWO: ANALYSING DATA

• Examine Data
• Explore the Underlying Distribution of Data
• Use Visualisations to Analyse Data
• Preprocess Data

DAY THREE: DESIGNING A MACHINE LEARNING APPROACH

• Identify Machine Learning Concepts
• Test a Hypothesis
• Developing Classification Models
• Train and Tune Classification Models
• Evaluate Classification Models

DAY FOUR: TRAIN MACHINE LEARNING MODELS

• Prepare to Train a Machine Learning Model
• Developing Regression Models
• Train and Tune Regression Models
• Evaluate Regression Models
• Developing Clustering Models
• Train and Tune Clustering Models
• Evaluate Clustering Models

DAY FIVE:  FINALISING A DATA SCIENCE PROJECT

• Communicate Results to Stakeholders
  • Audience and Insights
  • Factors That Drive Outcomes
  • Present Model Results
  • Visuals and Dashboards
  • Cumulative Gains Charts and Lift Charts
• Demonstrate Models in a Web App
  • Web App
  • HTML, CSS, and JavaScript
  • Web Frameworks
  • Flask and Django
  • Demonstrating Models in a Web App
• Implement and Test Production Pipelines
  • Put a Model into Production
  • Data Pipelines
  • Model Drift
  • Docker and Kubernetes
  • Amazon SageMaker and Azure Machine Learning
  • Monitor Models in Production