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im电竞游戏入口官网(im电竞游戏选手2.5.7) Objectives  CPD Certified

Python is the fastest growing programming language globally, mainly due to its simplicity, large community, and wealth of application; including data science and quant finance. Though VBA is more immediately applicable for Excel users, and therefore still very relevant, Python is more globally scalable in terms of its possibilities. Python is faster at complex calculations, can handle huge datasets (big data), has more packages (libraries), and can be used to build standalone or web-based applications, as well as interact with Excel if required. Python is also much friendlier and easier to learn than C++, thus is viewed as a language suitable for beginners and advanced programmers alike. 

The objective of this course is to get comfortable with the main elements of Python programming. Day one is about acquiring a suite of Python language skills via small exercises concerning stock ticker lists, client databases, basic statistics and other fun topics such as email address, usernames and passwords. Day two further applies python and python packages to the world of finance. On this day, we conduct some data science projects. We specifically aim to import, clean, enrich, transform, visualize and output the analysis of a large dataset of IMF World Economic Data, as well as build a simple Loan Calculator to demonstrate the usefulness of GUIs. 

Key Learning Outcomes:

  • Learn to write, test and debug Python 3 code with confidence, including working with Containers, Conditionals & Loops, Functions & Modules and Error Handling.
  • Learn the fundamentals of some of the most widely used Python packages; including NumPy, Pandas and Matplotlib, then apply them to Data Analysis and Data Visualization projects.
  • Build and code a Graphical User Interface (GUI) to run a program.

Target Audience

This course focuses on the Python language and its applications to basic data science problems. It is deliberately light on complex mathematical content, so to be accessible and suitable for attendees from any background who have an interest in learning what Python can do for them. Attendees only need experience using a windows PC (we will pre-install all required programs ahead of the class). No prior coding experience is necessary, as the course has been designed to be a ground-up approach which teaches the Python language structure from first principles. 
Day One
The Anaconda Distribution & Spyder IDE (0.5 hours)

The 64-bit Python 3.7 Anaconda Distribution will be preinstalled prior to the course. We then focus on:

  • Uses of the Anaconda Command Prompt
  • A tour of Spyder IDE windows and functionality e.g.
    • Editor, IPython Console, File Explorer, Variable Explorer, Shortcuts, getting help
The Python 3 Language (6 – 6.5 hours)

Here we use mini-exercises and class challenges to learn how to run, test, and debug Python code in a highly structured way. Themes for this day section include working with stock ticker lists , client databases , basic statistics and other useful topics.

  • Core Syntax
    • Data & Object Types, Variables
      • Eg. Numeric (Integers, Floats), String, Boolean
    • Math Operators, including **, //, %
    • Basic Input / Output (I/O). e.g. Print, Input
    • Code Presentation: Indentation and Commenting
    • Concatenation, Line Splitting & Continuation
    • Running Code Line by Line and in Cells
  • Containers
    • Containers Compared: Mutable vs. Immutable, Ordered vs Unordered
    • Strings
      • Replacement Fields
      • String Formatting & Slicing
      • String Methods e.g. Count, Index, Replace
    • Lists (Arrays)
      • Creating List Elements
      • Reading & Slicing Lists with Index Numbers
      • Mutating Lists: Amending, Appending, Inserting, Removing, Sorting
      • Working with Multiple Lists e.g. Multiplying List Elements
      • Lists vs Tuples
    • Ranges
      • Creating Lists of Ranges with Start, Stop and Step Parameters
      • Reading through Ranges
    • Dictionaries
      • Writing & Reading Keys and Items
      • Mutating Dictionaries: Amending, Adding, Removing
      • Dictionary Lookups: returning an Item for a Key
  • Conditionals and Loops
    • Comparison Operators, including: ==, !=
    • If, Elif, Else Statements
    • For Loops
    • While Loops, While True Loops with Continue & Break Statements
    • Debugging Code with and without Breakpoints
    • List Comprehensions (Introduction)
  • Functions (User-defined)
    • Defining and Calling
    • The Return Statement
    • Setting default parameter values
    • Using keyword / named / positional arguments
    • Lambda Functions (Introduction)
  • Error Handling
    • Error Types:
      • Syntax (parsing) errors
      • Exception Errors
    • Tracebacks
    • Handling Exceptions: try, except, else, finally
  • Modules
    • Uses of popular built-in modules including: Math, Statistics, DateTime
    • Import methods: import, import…as, from…. import
Day Two
Python Packages (Libraries) (6-7 hours)

Now that we’re more comfortable with the Python language, we conduct a series of mini data science projects which utilize some of the most widely used packages in Python.

Mini-Project 1 (Pandas): Data Analysis

Here we import a large dataset of IMF World Economic Data to acquire the key skills surrounding data import , cleaning , enrichment , transformation and output :

  • File Parsing large csv files into Pandas DataFrames
    • Reading & Locating / Slicing Data
    • Print Display Options
    • Data Cleaning
    • Data Type Assignments
  • Data Transformation: Manipulating DataFrames
    • Removing (Dropping), Renaming Columns
    • Adding Calculated Columns
    • Sorting & Filtering Data
    • Producing Summary Statistics
  • Outputting the Manipulated Data to Excel
  • Demo : The advantage of Python over Excel in working with Big Data

Mini-Project 2 (Matplotlib, NumPy, Pandas): Data Visualization

We now continue with our dataset (and others) in order to visualize our work in flexible ways:

  • Visualizing various types of custom data, with and without DataFrames
  • Plotting Charts
    • Chart Types: Line / Time Series, Scatter / Regression, Pie etc
    • Adding Titles, Labels, Legends, Markers etc
    • Adding Controls e.g. Sliders, Zooms etc
    • Creating Subplots
    • Saving charts as high-quality PNG images for use in Word, PowerPoint etc

Mini-Project 3 (NumPy, PyQt5Designer): Loan Calculator

Finally, we demonstrate the use of GUIs by building out a simple loan calculator:

  • Building a Graphical User Interface (GUI) for user input, using the PyQt5Designer
  • Coding the calculator with NumPy time value of money functions