1721 Developer courses delivered On Demand

Learn Kubernetes and prepare yourself for CKA and CKAD certifications. This course helps you gain the knowledge required to design and deploy cloud-native applications on a Kubernetes cluster. All you need is a good understanding of the Docker fundamentals to get started.

In this course, we will process massive streams of real-time data using Spark Streaming and create Spark applications using the Scala programming language (v2.12). We will also get our hands-on with some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models.

Teacher Training Diploma Level 5 course equips learners with essential teaching skills and knowledge to excel in the education sector. It provides comprehensive insights into effective teaching methodologies, psychology in teaching, and strategies for managing diverse classroom situations.

Unlock the world of programming excellence with our 'Diploma in Python Programming' course. In this dynamic learning journey, you'll delve into the fundamental concepts of Python and emerge as a proficient Python programmer. Whether you're a novice or have some prior coding experience, this course caters to all levels of learners. You'll start with the basics, gradually working your way up to complex Python operations, data structures, and even creating a fun Rock, Paper, and Scissors project. By the end of this course, you'll have a strong grip on Python, be able to write user functions, handle exceptions, explore databases, and much more. Take your first step towards becoming a skilled Python programmer and discover the endless possibilities this versatile language offers. Learning Outcomes Master the foundational concepts of Python programming. Develop essential skills in working with strings, dates, and files using Python. Create user functions, handle exceptions, and install packages. Explore database management using SQLite and interact with the operating system. Gain the knowledge and confidence to run Python programs in different environments, including Jupyter Notebook. Why choose this Python Programming Diploma? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Python Programming Diploma for? Aspiring programmers looking to kickstart their coding journey. Professionals seeking to expand their skill set and explore Python. Students aiming to excel in programming and computer science. Anyone curious about the power and versatility of Python as a programming language. Career path Python Programmer: £25,000 - £70,000 Data Analyst: £30,000 - £60,000 Web Developer: £25,000 - £50,000 Software Engineer: £35,000 - £80,000 Machine Learning Engineer: £40,000 - £90,000 Data Scientist: £40,000 - £70,000 Prerequisites This Diploma in Python Programming does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Diploma in Python Programming was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Unit 01: Introduction to Python Programming Module 01: Course Introduction 00:02:00 Unit 02: Getting Started with Python Module 01: Software Installation 00:02:00 Module 02: Hello World Program 00:06:00 Module 03: Input and Output 00:07:00 Module 04: Calculating Average of 5 Numbers 00:03:00 Unit 03: Conditional Branching with Python Module 01: If Loop In Python 00:06:00 Module 02: Program Using If Else part 1 00:03:00 Module 03: Program Using If Else part 2 00:08:00 Module 04: Program for Calculator 00:02:00 Module 05: Program Using For Loop 00:08:00 Module 06: For Table 00:05:00 Module 07: For loop and Mathematical Operator in Python 00:04:00 Module 08: Factorial of Number Using Python 00:06:00 Module 09: Program Using While 00:05:00 Module 10: While Loop Example 00:07:00 Module 11: Tasks for Practice 00:02:00 Unit 04: Importing external/internal library in python Module 01: Importing Library in Python 00:07:00 Unit 05: Project Rock Paper and Scissors Module 01: Rock Paper and Scissor Game 00:06:00 Unit 06: Strings Operation in Python Module 01: Program Using String part 1 00:05:00 Module 02: Program using String 2 00:06:00 Module 03: Program Using String 3 00:06:00 Module 04: Program Using String part 4 00:03:00 Unit 07: Date and time in Python Module 01: Use of Date and Time part 1 00:05:00 Module 02: Use of Date and Time part 2 00:05:00 Unit 08: File Handling, read and write using Python Module 01: File Handling Part 1 00:08:00 Module 02: File Handling Part 2 00:07:00 Unit 09: Data Storage Structures, Tuple, List and Dictionary Module 01: Tuple in Python Part 1 00:10:00 Module 02: Tuple in Python Part 2 00:07:00 Module 03: Using Lists part 1 00:07:00 Module 04: Using List part 2 00:12:00 Module 05: Using Lists part 3 00:06:00 Module 06: Using Lists part 4 00:08:00 Module 07: Using Lists part 5 00:02:00 Module 08: Use of Dictionary Part 1 00:04:00 Module 09: Use of Dictionary Part 2 00:05:00 Module 10: Use of Dictionary Part 3 00:08:00 Module 11: Use of Dictionary Part 4 00:07:00 Unit 10: Writing user functions in Python Module 01: Function in Python Part 1 00:06:00 Module 02: Function in Python Part 2 00:05:00 Module 03: Function in Python Part 3 00:04:00 Module 04: Function in Python Part 4 00:07:00 Module 05: Function in Python Part 5 00:08:00 Unit 11: Sending mail Module 01: Send Email 00:09:00 Unit 12: Import Tricks in Python Module 01: Import Study part 1 00:07:00 Module 02: Import Study part 2 00:03:00 Unit 13: Import Operating System and Platform Module 01: Importing OS 00:06:00 Module 02: Import Platform 00:05:00 Unit 14: Exceptions handling in python Module 01: Exception in Python part 1 00:11:00 Module 02: Exception in Python part 2 00:07:00 Module 03: Exception in Python part 3 00:05:00 Unit 15: Installing Packages and Scheduling In Python Module 01: Installing Packages using built in package manager 00:08:00 Module 02: Scheduler in Python 00:05:00 Unit 16: Data Base In Python using sqlite Module 01: Data Base 1 00:08:00 Module 02: Data Base 2 00:09:00 Module 03: Data Base 3 00:08:00 Module 04: Data base 4 00:07:00 Module 05: Data Base 5 00:06:00 Unit 17: Running Program from Command Prompt and jupyter Notebook Module 01: IDE_1 00:05:00 Module 02: IDE_2 00:07:00 Unit 18: Conclusion Module 01: Conclusion 00:02:00 Resources Resources - Diploma in Python Programming 00:00:00 Assignment Assignment - Diploma in Python Programming 00:00:00 Recommended Materials Workbook - Diploma in Python Programming 05:14:00

Dive deep into the vast realm of Python data science with our meticulously crafted course: 'Python Data Science with Numpy, Pandas and Matplotlib'. Explore the intricate details of Python, setting the stage with Pandas and Numpy, before delving into the power of Python data structures. With topics ranging from Python Strings to Matplotlib Histograms, you'll gain a holistic insight, ensuring that every dataset you touch unveils its story compellingly. So, if you're keen on transmuting raw data into visual masterpieces or insights, this journey is tailor-made for you. Learning Outcomes Grasp foundational knowledge of Python and its data structures like strings, lists, and dictionaries. Understand the potential of NumPy, from basic array operations to handling multi-dimensional arrays. Master the versatility of Pandas, encompassing everything from dataframe conversions to intricate operations like aggregation and binning. Efficiently manage, manipulate, and transform data using Pandas' diverse functionalities. Create visually striking and informative graphs using the power of Matplotlib. Why buy this Python Data Science with Numpy, Pandas and Matplotlib course? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Python Data Science with Numpy, Pandas and Matplotlib there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this Python Data Science with Numpy, Pandas and Matplotlib course for? Beginners eager to jumpstart their journey in Python data science. Analysts looking to enhance their data manipulation skills using Python. Statisticians keen on expanding their toolset with Python-based libraries. Data enthusiasts desiring a deep dive into Python's data libraries and structures. Professionals aiming to upgrade their data visualisation techniques. Prerequisites This Python Data Science with Numpy, Pandas and Matplotlib does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Python Data Science with Numpy, Pandas and Matplotlib was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path Data Scientist: £40,000 - £80,000 Python Developer: £35,000 - £70,000 Data Analyst: £30,000 - £55,000 Business Intelligence Analyst: £32,000 - £60,000 Research Analyst: £28,000 - £52,000 Data Visualization Engineer: £33,000 - £65,000 Course Curriculum Course Introduction and Table of Contents Course Introduction and Table of Contents 00:09:00 Introduction to Python, Pandas and Numpy Introduction to Python, Pandas and Numpy 00:07:00 System and Environment Setup System and Environment Setup 00:08:00 Python Strings Python Strings - Part 1 00:11:00 Python Strings - Part 2 00:09:00 Python Numbers and Operators Python Numbers and Operators - Part 1 00:06:00 Python Numbers and Operators - Part 2 00:07:00 Python Lists Python Lists - Part 1 00:05:00 Python Lists - Part 2 00:06:00 Python Lists - Part 3 00:05:00 Python Lists - Part 4 00:07:00 Python Lists - Part 5 00:07:00 Tuples in Python Tuples in Python 00:06:00 Sets in Python Sets in Python - Part 1 00:05:00 Sets in Python - Part 2 00:04:00 Python Dictionary Python Dictionary - Part 1 00:07:00 Python Dictionary - Part 2 00:07:00 NumPy Library - Introduction NumPy Library Intro - Part 1 00:05:00 NumPy Library Intro - Part 2 00:05:00 NumPy Library Intro - Part 3 00:06:00 NumPy Array Operations and Indexing NumPy Array Operations and Indexing - Part 1 00:04:00 NumPy Array Operations and Indexing - Part 2 00:06:00 NumPy Multi-Dimensional Arrays NumPy Multi-Dimensional Arrays - Part 1 00:07:00 NumPy Multi-Dimensional Arrays - Part 2 00:06:00 NumPy Multi-Dimensional Arrays - Part 3 00:05:00 Introduction to Pandas Series Introduction to Pandas Series 00:08:00 Introduction to Pandas Dataframes Introduction to Pandas Dataframes 00:07:00 Pandas Dataframe conversion and drop Pandas Dataframe conversion and drop - Part 1 00:06:00 Pandas Dataframe conversion and drop - Part 2 00:06:00 Pandas Dataframe conversion and drop - Part 3 00:07:00 Pandas Dataframe summary and selection Pandas Dataframe summary and selection - Part 1 00:06:00 Pandas Dataframe summary and selection - Part 2 00:06:00 Pandas Dataframe summary and selection - Part 3 00:07:00 Pandas Missing Data Management and Sorting Pandas Missing Data Management and Sorting - Part 1 00:07:00 Pandas Missing Data Management and Sorting - Part 2 00:07:00 Pandas Hierarchical-Multi Indexing Pandas Hierarchical-Multi Indexing 00:06:00 Pandas CSV File Read Write Pandas CSV File Read Write - Part 1 00:05:00 Pandas CSV File Read Write - Part 2 00:07:00 Pandas JSON File Read Write Pandas JSON File Read Write Operations 00:07:00 Pandas Concatenation Merging and Joining Pandas Concatenation Merging and Joining - Part 1 00:05:00 Pandas Concatenation Merging and Joining - Part 2 00:04:00 Pandas Concatenation Merging and Joining - Part 3 00:04:00 Pandas Stacking and Pivoting Pandas Stacking and Pivoting - Part 1 00:06:00 Pandas Stacking and Pivoting - Part 2 00:05:00 Pandas Duplicate Data Management Pandas Duplicate Data Management 00:07:00 Pandas Mapping Pandas Mapping 00:04:00 Pandas Grouping Pandas Groupby 00:06:00 Pandas Aggregation Pandas Aggregation 00:09:00 Pandas Binning or Bucketing Pandas Binning or Bucketing 00:08:00 Pandas Re-index and Rename Pandas Re-index and Rename - Part 1 00:04:00 Pandas Re-index and Rename - Part 2 00:05:00 Pandas Replace Values Pandas Replace Values 00:05:00 Pandas Dataframe Metrics Pandas Dataframe Metrics 00:07:00 Pandas Random Permutation Pandas Random Permutation 00:08:00 Pandas Excel sheet Import Pandas Excel sheet Import 00:07:00 Pandas Condition Selection and Lambda Function Pandas Condition Selection and Lambda Function - Part 1 00:05:00 Pandas Condition Selection and Lambda Function - Part 2 00:05:00 Pandas Ranks Min Max Pandas Ranks Min Max 00:06:00 Pandas Cross Tabulation Pandas Cross Tabulation 00:07:00 Matplotlib Graphs and plots Graphs and plots using Matplotlib - Part 1 00:06:00 Graphs and plots using Matplotlib - Part 2 00:02:00 Matplotlib Histograms Matplotlib Histograms 00:03:00 Resource File Resource File - Python Data Science with Numpy, Pandas and Matplotlib 00:00:00

Let's build sophisticated visualizations and dashboards using Sankey diagrams and geospatial, sunburst, and circular charts and animate your visualizations. We will also cover advanced Tableau topics, such as Tableau parameters and use cases and Level of Detail (LOD) expressions, spatial functions, advanced filters, and table calculations.

Discover the power of data science and machine learning with Python! Learn essential techniques, algorithms, and tools to analyze data, build predictive models, and unlock insights. Dive into hands-on projects, from data manipulation to advanced machine learning applications. Elevate your skills and unleash the potential of Python for data-driven decision-making.

Embark on a comprehensive journey into the world of MySQL with a focus on its applications in Data Science and Analytics. This course is structured to take you from the fundamentals to advanced topics in MySQL Server. Covering SQL basics, data manipulation and definition, control and analytic functions, and database management, you'll acquire the essential skills for harnessing MySQL's power in data-driven decision-making. Learning Outcomes: Establish a strong foundation in SQL and MySQL. Set up and configure SQL Server for efficient data handling. Master SQL's Data Manipulation, Definition, and Control Language. Create and optimize SQL queries for data analysis. Perform advanced data analytics using SQL. Understand the power of GROUP BY and JOIN statements. Implement data constraints and views for data integrity and security. Develop proficiency in stored procedures, data import/export, and database backup/restore. Why buy this Learn MySQL from Scratch for Data Science and Analytics?  Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Learn MySQL from Scratch for Data Science and Analytics you will be able to take the MCQ test that will assess your knowledge. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Learn MySQL from Scratch for Data Science and Analytics course is ideal for Aspiring Data Scientists and Analysts. Database Administrators and Developers. Students and professionals seeking to enter the field of Data Science. Anyone looking to enhance their SQL and MySQL skills for data-related roles. Prerequisites This Learn MySQL from Scratch for Data Science and Analytics was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path Data Analyst: £25,000 - £50,000 per year. Database Administrator: £30,000 - £60,000 per year. SQL Developer: £30,000 - £60,000 per year. Data Scientist: £40,000 - £80,000 per year. Business Intelligence Analyst: £35,000 - £65,000 per year. Course Curriculum Learn MySQL from Scratch for Data Science and Analytics Section 01: Getting Started Introduction 00:02:00 How to get course requirements 00:01:00 Getting started on Windows, Linux or Mac 00:01:00 How to ask great questions 00:01:00 FAQ's 00:01:00 What is Source Code? 00:09:00 Section 02: SQL Server setting up Section Introduction 00:01:00 MySQL Server Installation 00:14:00 Connect MySQL Server Instance 00:06:00 MySQL Workbench overview 00:11:00 Download and Restore Sample Database 00:08:00 Section 03: SQL Database basics Section Introduction 00:01:00 Overview of Databases 00:09:00 Creating Database 00:07:00 SQL Data Types 00:04:00 Column Data Types on Workbench 00:07:00 Creating Table 00:04:00 Overview of Primary and Foreign Key 00:03:00 Primary Key 00:06:00 Foreign Key 00:12:00 Creating Temporary tables 00:12:00 EER - Enhanced Entity Relationship Diagrams 00:04:00 Section 04: SQL DML (Data Manipulation Language) Section Introduction 00:01:00 Insert statement 00:07:00 Update statement 00:06:00 Delete statement 00:03:00 Section 05: SQL DDL (Data Definition Language) Section Introduction 00:01:00 CREATE table statement 00:08:00 DROP statement 00:03:00 ALTER statement 00:05:00 TRUNCATE statement 00:03:00 COMMENT in query 00:02:00 RENAME table 00:03:00 Section 06: SQL DCL (Data Control Language) Create Database user 00:03:00 GRANT permissions 00:06:00 REVOKE permissions 00:04:00 Section 07: SQL Statement Basic Section Introduction 00:01:00 SQL Statement basic 00:03:00 SELECT Statement 00:03:00 SELECT DISTINCT 00:02:00 SELECT with column headings 00:01:00 Column AS statement 00:02:00 DASHBOARD Analytics 00:06:00 Section 08: Filtering Data rows SELECT WHERE Clause - theory 00:03:00 SELECT WHERE Clause - practical 00:06:00 Section 09: Aggregate functions for Data Analysis Sum() 00:06:00 Min()-Max() 00:03:00 Section 10: SQL Data Analyticstatements Order By statement 00:05:00 SELECT TOP 3 records 00:02:00 BETWEEN command 00:06:00 IN operator 00:03:00 Search Data usingLIKE cards 00:05:00 Section 11: SQL Group by statement Section Introduction 00:01:00 Group by - theory 00:04:00 Data Analytics with Group By 00:04:00 HAVING statement 00:03:00 Section 12: JOINS Overview of Joins 00:02:00 What are Joins 00:02:00 Inner join 00:07:00 Left outer join 00:02:00 Right outer join 00:02:00 Union 00:03:00 CERTESIAN Product or Cross Join 00:03:00 Query Exercise 00:01:00 Solution for Query Exercise 00:01:00 Section 13: SQL Constraints Section introduction 00:01:00 Check constraint 00:09:00 NOT NULL constraint 00:03:00 UNIQUE constraint 00:06:00 Section 14: Views Creating Views 00:03:00 Data Analytic Views from multiple tables 00:03:00 Section 15: Advanced SQL Functions Section Introduction 00:01:00 Timestamp 00:03:00 Extract from timestamp 00:03:00 Mathematical scalar functions 00:03:00 String functions3 00:07:00 Advanced functions 00:04:00 Sub Queries 00:03:00 SELECT with calculations 00:05:00 Section 16: SQL Stored procedures Create stored procedure 00:06:00 Stored procedure with parameter 00:03:00 Drop Procedure 00:01:00 Section 17: Import & Export data Section Introduction 00:01:00 Import .csv file 00:04:00 Export Data to .csv file 00:02:00 Section 18: Backup and Restore Database Section Introduction 00:01:00 Creating Database backup 00:02:00 Restoring Database backup 00:02:00

Learn about automated software testing with Python, BDD, Selenium WebDriver, and Postman, focusing on web applications

Learn Vue.js through a practical, project-based approach, along with understanding how to use the Vue CLI and Firebase storage