Python Boot Camp, 12-weeks

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

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.

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.

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

Personalised, 1-1 Mentoring & Small Groups, Max 4.
Practical, Hand-on.
Online Instructor-Led.

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,
Reorder,
Sorting, Stastitics,
Dataframe Joins, Merging, Concatenation, Overlap.
Reshaping and pivoting.

Query a Pandas Dataframe

Data Analysis:
Sorting.
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:

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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