By the end of this course, you will be able to:

• Define the concept of cloud computing
• Classify private and public clouds
• Classify cloud service models
• Categorize virtualization technologies including VMs and containers
• Describe the concept of vertical and horizontal scaling
• Discuss the connection between microservices and cloud-native apps
• Explain how IaaS is transforming IT into a utility
• Classify the building blocks of cloud computing
• Discuss IaaS pricing models
• Evaluate the advantages and disadvantages of IaaS
• Recognize typical market-use cases

By the end of this course, you will be able to:

• Outline the connection between artificial intelligence, machine learning and deep learning
• Describe the concept of supervised and unsupervised learning
• Recognise supervised learning building blocks of features and labels and provide examples
• Explain the process of training a model in supervised learning
• Recognise the challenges of underfitting and overfitting
• Identify classification and regression tasks
• Outline clustering and dimension reduction
• Describe reinforcement learning

By the end of this course, you will be able to:

• Outline the challenges of the old-school software development style
• List the main building blocks of a single microservice
• Describe the concept of microservices architecture
• Define ‘cloud-native applications’
• Analyse the key advantages of microservices
• Estimate the implementation challenges of microservices
• Discuss the Netflix microservices industry case study

By the end of this course, you will be able to:

• Define the basic terminology of DB, DBMS Schema and ETL/ELT
• Classify operational and analytics data
• Compare data warehouse and data lakes
• Identify batch and stream processing
• Compare SQL, NoSQL and NewSQL technologies
• Describe in-memory and time-series technologies
• Recognize the market options of ‘database as a service’ (DBaaS)

By the end of this course, you will be able to:

• Explain the fundamental syntax of Python
• Discuss the JupyterLab development tool
• Arrange and manage projects with Jupyter notebooks
• Analyse examples of data science projects in Python
• Arrange tabular datasets from CSV files into a DataFrame object
• Classify, select, filter and clean raw data
• Describe how to transform data in rows and columns as a pre-processing step before using algorithms
• Compare, group and sort data in multiple dimensions
• Arrange and export the output data to CSV files
• Summarise an end-to-end data exploration

By the end of this course, you will be able to:

• Analyse datasets using Exploratory Data Analysis (EDA)
• Summarise data using a variety of chart types
• Explain the fundamentals of Matplotlib and Seaborn
• Classify bar, grouped bar, stacked bar and lollipop charts
• Analyse data using pie and tree-map charts
• Analyse data using line, area and stacked area charts
• Evaluate data using histogram, density, box-and-whisker and swarm charts
• Evaluate data using scatter, correlogram, heat-Map and hexbin-map charts
• Select the right chart to perform the right task
• Describe and discuss the of outcome of the analysis performed