Course details
Learn to code Python, from scratch to job-ready.

With this excellent Python Programming course London you will achieve job-ready coding expertees.

    How does it work?
  • Online, Instructor-led lessons: 1 full day lesson per week, for 12 weeks
  • Plus Self-study Materials and a Structured Self-Study Program
  • Plus 1-1 mentoring Scheduled in addition
  • Plus Live Online Practical Project to showcase your expertees

Part Time
  • 1 full day per week, online instructor-led.
    Self study, in your own time.
    1-1 mentoring, schedule your preferred time.
    Earn and Learn, stay employed, work, earn your salary until you qualify, then change.
1-1 Mentoring
  • Additional, between weekly sessions.
    Work at your pace, 1-1 sessions can cover extra work and/or help you catch up.
    Gain confidence, because we revise & validate your practicals.
    Be re-assured, get immediate answers to your questions.
  • Learn by doing, the best way to re-inforce learning, is by trying on your own.
    Practical, most of the self-study work is practical exercises.
    Gain experience, this aspect of the course gives you experience employesr are seeking.
Practical Project
  • Live online, upload your project.
    Showcase, your expertees are testified online.
    Become known, your project will put you in contact with the coding community.
  • Video Tutorials, Short and easy.
    Python Coding Examples, Plenty thereof.
    Manuals and Notes Reference materials.
    Exercises, Practical work with every class.
  • Best deal: £2100 up front.
    Installments: Contact us to arrange.
Our Style
  • Personalised, 1-1 Mentoring & Small Groups, Max 4.
    Practical, Hand-on.
    Online Instructor-Led.
Weekly topics and other details
Weekly Python lesson topic descriptions

  • Overview of Python Fundamentals:
  • Python Data Types, Variables:
  • Primitive types; Characters; Boolean; Working with variables and its scope; Type conversion and casting;
  • Strings
  • String Functions, Strings vs numbers vs dates.
  • Getting user input.
  • Python Operators and Expressions:
  • Introduction of operators; Arithmetic operators; Relational operators; Assignment operator; Logical operators; Increment and decrement operators.
  • Decision Making:
  • If statement; If - else statement; If- else if - else statement; Nested if - else; Switch Statements
  • Using Loops:
  • The while, do-while and the for loop; Enhanced for loop; Jump statements : break, continue; The return statement; Nesting loops.

  • OOP Principals
  • Using Methods:
    Learn Python method basics. Defining Methods, Parameters, Returning values, Overloading methods, Calling methods. Encapsulation.
  • Classes and Objects Inheritance, Override, Constructors, Parametised Constructors, the self keyword, Inner classes

  • Lists. Tuples. Sets, Dictionary. Json Files.
  • Using Built-in modules and functions for strings, maths and dates.
  • Exception Handling, Files, Streams.

  • Database concepts, Relational Database
  • Data Types, Columns, Tables
  • Relationships
  • SQL statements
  • DDL SQL Statements:
  • Create and drop a databases
  • Create,aleter and drop alter tables
  • Select queries: where-clauses, wildcards, order by, joins, aggregates, having,
  • DML Queries: Insert, Update and deleting records

  • Connect to a from Python to a SQLite3 database,
  • Data Driven Python Project:
  • DDL Queries: Create a table, alter tables, drop a table
  • Creating a log of transactions, using the above
  • DML Queries: insert, delete, update records
  • Creating a log-in facility to register, delete and maintain users
  • Create a Search facility using select queries
  • Query a database with wildcard parameters and display results

  • Numpy Arrays The Python NumPy Module: Working with arrays, create data using arrays. Array manipulation and array-wise math functions. String functions on arrays.
  • Numpy Built-In Functions : Math, arithmetic and statistical functions.
  • Numpy Calculations

  • Pandas Series
  • Data Cleaning
  • Python Pandas Dataframes and data importing
    Python Dataframes
    Data Series. Date/ Time Functionality. Time series.
  • Creating Dataframes, Indexing.
    Dict to Dataframe, Dataframe to Dict.
    Csv to Dataframe, Dataframe to csv.
    Excel to Dataframe, Dataframe to Excel.
  • Data Cleaning and preparation
    Finding, replacing and filtering missing data.
    Remove Duplicates.
    Replacing values.
    Renaming Axis Indexes.
  • Pandas Data Wrangling
    Discretization and Binning.
    Random Sampling.
    Transforing data using function and mapping,
    Hierarchical Indexing,
    Sorting, Stastitics,
    Dataframe Joins, Merging, Concatenation, Overlap.
    Reshaping and pivoting.

  • Query a Pandas Dataframe
  • Data Analysis:
    Analysing and finding data using filter, slicing and dataframe queries.
    Finding data by Iteration.
    Find statistics: Functions, Aggregate functions.
    Unique values.
    String objects, Regex.

  • Chart Types: Bar, Column, Line, Scatter, Pie, Area, Histogram, Funnel Charts
  • Formatting: Changing gridlines lines, axes, scales, markers, colours,
  • Chart Elements: legends, titles, plot seizes, exporting.

  • Supervised Machine Learning:
  • Classification Algorithms:
  • Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine
  • Regression Algorithms: Linear, Polynomial

  • Unsupervised Machine Learning:
  • Clustering Algorithms: K-means clustering, Hierarchical Clustering
  • Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA)
  • Association Algorithms: Apriori, Euclat
  • Ensemble Methods Algorithms: Stacking, bagging, boosting. Random Forest Random Forest, Gradient Boosting
  • Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN)
  • Data Exploration and Preprocessing:
  • Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy.

  • Python Tkinter Front-end Basics
  • Getting Started with HTML
  • Getting Started with CSS
  • Getting Started with Php
  • Getting Started with JavaScripts

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