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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 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 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. Self-Study Self-Study 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 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. Materials Materials Video Tutorials, Short and easy. Python Coding Examples, Plenty thereof. Manuals and Notes Reference materials. Exercises, Practical work with every class. Payments Payments Best deal: → £2100 up front. Installments: Contact us to arrange. Our Style 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, 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: 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 Book the Python Boot Camp About us Our experienced trainers are award winners. More about us FAQ's Client Comments
