Python Boot Camp, 12-weeks Blended Learning Program, £2100
Course details
Python Boot Camp Course Summary.

This excellent Python Programming course will help you to learn coding in Python thoroughly.
Learn to code from scratch to job-ready.

    "Blended" learning programming:
  • Online, Instructor-led lessons: 1 full day lesson per week
  • Plus Self-study Materials and a Structured Self-Study Program
  • Plus 1-1 mentoring sessions to learn Python
  • Live Practical Project
    Upload and showcase your project
  • Payments
    £2100 in one payment or talk to us about monthly installments

Our Style: Instructor-led, Online, Hands-on, Practical Course.
Group Size: Max 4 people per group.
Qualification: PCWorkshops Programmer II Certification
Hours: Part-time, study while working

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.

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

  • 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

  • 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

  • Python Coding Boot Camp Self-Study Program:

  • Video TutorialsExcellent videos, short and easy
    Python Coding Examples All lessons are illustrated with code examples
    Manuals and Notes Reference materials
    Exercises Exercises after every class
    Tests Many tests to re-inforce

  • 1-1 Mentoring:

  • Book 1-1 Sessions Individually
    Dedicated trainer per student

  • Practical Projects:

  • Projects Combine the exercises into great projects
    Show case your projects on Github

    Python Boot Camp Course Materials
    • Python Boot Camp Certificate on completion
    • Python Boot Camp Videos
    • Python Boot Camp Notes
    • Python Code Examples
    • Practical Python Boot Camp exercises, Python Course Revision work
    • Mock tests and contests for the Python certificate
    Book the Python Boot Camp
    About us
    Learn Java
    What's new in Python 17? Accelerating Java’s Adoption in the Cloud with continuous innovation that address the evolving needs of developers. To accelerate Python adoption in the cloud, Oracle recently introduced the Oracle Python Management Service
    What's new in Python 17? Updates and Improvements to Libraries JEP 306: Restore Always-Strict Floating-Point Semantics –
    What's new in Python 17? Future Proofing Python Programs JEP 403: Strongly Encapsulate JDK Internals – It will no longer be possible to relax the strong encapsulation of internal elements
    What's new in Python 17? Read the Python 17 technical blog :

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