Programming Training | F# For Data Scientist Training in Brno

F# for Data Scientist Training Course Outline

Module 1: Get started with F#

• Install F#
• F# in Visual Studio
• F# in Visual Studio Code
• F# with the .NET CLI
• F# in Visual Studio for Mac

Module 2: Literals

• Literal Types
• Named Literals
• Remarks
• Integers in Other Bases
• Underscores in Numeric Literals

Module 3: Strings

• Verbatim Strings
• Triple Quoted Strings
• String Indexing and Slicing
• String Operators
• String Class
• Interpolated Strings
• Syntax
• Typed Interpolated Strings
• Verbatim Interpolated Strings
• Format Specifiers
• Aligning Expressions in Interpolated Strings
• Interpolated Strings and FormattableString Formatting

Module 4: let Bindings

• Function Bindings
• Type Annotations
• let Bindings in Classes
• Type Parameters in let Bindings
• Attributes on let Bindings
• Scope and Accessibility of let Bindings
• do Bindings
• Fixed Keyword

Module 5: Functions

• Recursive Functions
• Inline Functions
• Function Expressions

Module 6: Loops and Conditional

• Conditional Expressions: if...then...else
• for...in Expression
• for...to Expression
• while...do Expression

Module 7: Pattern Matching

• Constant Patterns
• Identifier Patterns
• Variable Patterns
• as Pattern
• OR Pattern
• AND Pattern
• Cons Pattern
• List Pattern
• Array Pattern
• Parenthesised Pattern
• Tuple Pattern
• Record Pattern
• Wildcard Pattern
• Patterns That Have Type Annotations
• Type Test Pattern
• Match Expressions
• Active Patterns

Module 8: Exception Handling

• Exception Handling Basics
• Exception Types
• Assertions

Module 9: Types and Interference

• Overview
• Basic Types
• Unit Type
• Type Inference
• Type Abbreviations
• Casting and Conversions
• Generics
• Automatic Generalisation
• Constraints
• Statically Resolved Type Parameters
• Flexible Types
• Units of Measure
• Byrefs

Module 10: Collections

• Lists
• Arrays
• Slices
• Sequences
• Reference Cells

Module 11: Records and Union

• Records
• Discriminated Unions

Module 12: Object Programming

• Classes
• Interfaces
• Members
• Constructors
• let Bindings in Classes
• do Bindings in Classes
• Properties
• Methods
• Method Parameters
• Indexed Properties
• Operator Overloading
• Explicit Fields
• Object Expressions
• Type Extensions
• Inheritance
• Abstract Classes

Module 13: Structs

• Structures
• Structs Types

Module 14: Computation

• Computation Expressions
• Async Expressions
• Task Expressions
• Lazy Expressions

Module 15: Organising Codes

• Struct Types
• Namespaces
• Modules
• Open Declarations
• Signature Files
• Access Control
• XML Documentation
• Entry Point

Module 16: Queries and Interoperability

• Queries Types
• Query Expressions
• Null Values
• Nullable Value Types
• Delegates
• Enums
• Events
• External Functions

Module 17: Reflection

• External Functions
• Attributes
• Code Quotations
• Nameof
• Caller Information
• Source Line, File, and Path Identifiers
• Plain Text Formatting

Module 18: Type Providers

• Overview
• Create a Type Provider
• Type Provider Security
• Troubleshooting Type Providers

Who Should Attend the F# for Data Scientist Training Course?

The F# for Data Scientist Training Course is designed to empower data science professionals with the skills and tools needed to effectively use the F# programming language for data analysis, machine learning, and advanced data manipulation. This course will benefit the following professionals:

• Data Scientists
• Data Analysts
• Machine Learning Engineers
• Quantitative Analysts
• Data Engineers
• Research Scientists
• Statisticians

Prerequisites of the F# for Data Scientist Training Course

There are no formal prerequisites to attend the F# for Data Scientist Training Course. However, having a basic understanding of Functional Programming, C#, and .NET would be beneficial.

F# for Data Scientist Training Course Overview

F# is a universal programming language for creating code that is condensed, reliable, and effective. You can write clear, self-documenting code with F# that keeps your attention on your problem domain rather than the specifics of programming. Data scientists apply statistical analysis, machine learning, visualisation, and programming to understand and interpret real-world data. This training will help learners develop strategies for processing data and handling large data streams with the F# platform. Holding the knowledge and F# programming skills to design and build applications will enable individuals to get the desired designations as developers and other higher prospects.

In this 2-day F# for Data Scientist Training course, delegates will learn about the various essential concepts of the F# programming language. During this training course, they will thoroughly understand valuable structures for small aggregates of data that are accessed and copied frequently. Delegates will also learn about Pattern matching expressions for complex branching based on comparing a test expression with a set of patterns. This course is led by our highly professional and knowledgeable instructor with years of experience teaching programming language courses.

Course Objectives

• To simplify handling data from relational databases, web APIs, Azure services, and non-.NET languages
• To learn how to build efficient, extensible engines to parse and process documents and data streams
• To perform operations on a list or other collection and avoid the extra work of defining a function
• To learn about interfaces that specify sets of related members that other classes implement
• To add new members to a previously described object type using type extensions

At the end of this training course, delegates will be able to use F#'s integrated data science packages and use them to interoperate with other languages and platforms. They will also be able to access and visualise data with the F# programming language.

What’s included in this F# for Data Scientist Training Course?

• World-Class Training Sessions from Experienced Instructors
• F# for Data Scientist Certificate
• Digital Delegate Pack