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Agile Project Management will give you the knowledge you need to implement an agile approach in your business.

This SEN Teacher Training Course equips you with the knowledge and skills to support children with special educational needs (SEN). Master inclusive teaching strategies, curriculum adaptation techniques, and positive behavior management approaches. Develop your confidence to create a nurturing and stimulating learning environment where every child can thrive. Ideal for aspiring and existing teachers seeking to specialize in SEN education. Enroll now and embark on a rewarding career path!

Feeling overwhelmed? Take control of your day with our Effective Time Management Course. Learn proven strategies to boost productivity, prioritize tasks, and achieve your goals. Stop feeling like you're constantly running out of time and start accomplishing more with less stress.

Are you embarking on the journey of mastering data analytics and visualisation in the UK? The 'Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7' is your beacon. Positioned to illuminate the intricate realm of Power BI, this course offers a comprehensive look into the foundational aspects and the advanced features that make Microsoft's tool a standout. With sections meticulously designed ranging from the fundamentals, like data transformation, to advanced concepts, such as integrating Power BI with Python and storytelling with data, this course ensures learners grasp the complete spectrum. With the rising emphasis on data analytics in today's business world, this course acquaints you with Power BI's prowess. It prepares you for the sought-after Microsoft Power BI certification in the UK. Learning Outcomes Comprehend the fundamental aspects of Power BI, from initiating a project to understanding the user interface. Develop proficiency in advanced data transformation techniques and data model creation. Integrate Python with Power BI and harness the benefits of both for enhanced data analytics. Master the art of 'Storytelling with Data' to deliver impactful presentations and reports. Understand and implement Row-Level Security and harness Power BI Cloud services efficiently. Why choose this Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are 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. Who is this Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7 for? Individuals keen on obtaining the Microsoft power bi certification UK. Analysts and data professionals aspiring to enhance their data visualisation skills. Business professionals wanting to leverage Power BI for insightful business decision-making. Tech enthusiasts aiming to amalgamate programming (Python) with data analytics. Those seeking to stay updated with the latest trends in Power BI and its evolving capabilities. Career path Data Analyst: Average Salary £30,000 - £40,000 Annually Business Intelligence Developer: Average Salary £35,000 - £45,000 Annually Power BI Developer: Average Salary £40,000 - £50,000 Annually Data Visualisation Specialist: Average Salary £32,000 - £42,000 Annually Business Intelligence Manager: Average Salary £45,000 - £55,000 Annually Data Strategy Consultant: Average Salary £50,000 - £60,000 Annually Prerequisites This Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This course 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. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £135 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Section 01: Introduction Welcome! 00:01:00 What is Power BI? 00:03:00 Download & Installing Power BI Desktop 00:04:00 Getting to know the interface 00:03:00 Mini Project: Transform Data 00:07:00 Mini Project: Visualize Data 00:05:00 Mini Project: Creating a Data Model 00:07:00 Course Outline: What will you learn in this course? 00:05:00 How to learn best with this course? 00:03:00 Section 02: Preparing our Project Creating our initial project file 00:04:00 Working with the attached project files 00:04:00 Section 03: Data Transformation - The Query Editor Exploring the Query Editor 00:06:00 Connecting to our data source 00:07:00 Editing rows 00:08:00 Changing data types 00:08:00 Replacing values 00:03:00 Close & Apply 00:03:00 Connecting to a csv file 00:03:00 Connecting to a web page 00:05:00 Extracting characters 00:06:00 Splitting & merging columns 00:09:00 Creating conditional columns 00:06:00 Creating columns from examples 00:09:00 Merging Queries 00:17:00 Pivoting & Unpivoting 00:06:00 Appending Queries 00:08:00 Practice & Solution: Population table 00:15:00 The Fact-Dimension-Model 00:09:00 Practice: Load the dimension table 00:04:00 Organizing our queries in groups 00:03:00 Entering data manually 00:05:00 Creating an index column 00:03:00 Workflow & more transformations 00:05:00 Module summary 00:05:00 Exercise 1 - Instruction 00:02:00 Exercise Solution 00:11:00 Section 04: Data Transformation - Advanced Advanced Editor - Best practices 00:09:00 Performance: References vs. Duplicating 00:10:00 Performance: Enable / Disable Load & Report Refresh 00:05:00 Group by 00:05:00 Mathematical Operations 00:05:00 Run R Script 00:15:00 Using Parameters to dynamically transform data 00:06:00 M formula language: Basics 00:07:00 M formula language: Values, Lists & Tables 00:14:00 M formula language: Functions 00:13:00 M formula language: More functions & steps 00:05:00 Exercise 2 - Instructions 00:01:00 Exercise 2 - solution 00:05:00 Section 05: Creating a Data Model Understanding the relationship 00:05:00 Create & edit relationships 00:06:00 One-to-many & one-to-one relationship 00:06:00 Many-to-many (m:n) relationship 00:08:00 Cross filter direction 00:06:00 Activate & deactivate relationships 00:06:00 Model summary 00:03:00 Exercise 3 Create Model 00:03:00 Exercise 3 Solution 00:02:00 Section 06: Data Visualization Our first visual 00:08:00 The format tab 00:12:00 Understanding tables 00:10:00 Conditional formatting 00:09:00 The Pie Chart 00:06:00 All about the filter visual 00:13:00 The filter pane for developers 00:09:00 Cross filtering & edit interactions 00:04:00 Syncing slicers across pages 00:07:00 Creating drill downs 00:08:00 Creating drill throughs 00:07:00 The tree map visual 00:07:00 The decomposition tree 00:05:00 Understanding the matrix visual 00:05:00 Editing pages 00:07:00 Buttons & Actions 00:09:00 Bookmarks to customize your report 00:10:00 Analytics and Forecasts with line charts 00:10:00 Working with custom visuals 00:07:00 Get data using R Script & R Script visual 00:08:00 Asking questions - Q&A visual 00:04:00 Wrap up - data visualization 00:08:00 Section 07: Power BI & Python Python in Power BI - Plan of attack 00:03:00 Setting up Python for Power BI 00:03:00 Transforming data using Python 00:11:00 Creating visualizations using Python 00:08:00 Violin plots, pair plots & ridge plots using Python 00:15:00 Machine learning (BayesTextAnalyzer) using Python 00:00:00 Performance & Troubleshooting 00:03:00 Section 08: Storytelling with Data Introduction 00:01:00 Show Empathy & Identify the Requirement 00:03:00 Finding the Most Suitable KPI's 00:02:00 Choose an Effective Visualization 00:04:00 Make Use of Natural Reading Pattern 00:03:00 Tell a Story Using Visual Cues 00:05:00 Avoid Chaos & Group Information 00:02:00 Warp Up - Storytelling with Data 00:02:00 Section 09: DAX - The Essentials Introduction 00:03:00 The project data 00:04:00 Measures vs. Calculated Columns 00:15:00 Automatically creating a date table in DAX 00:08:00 CALENDAR 00:05:00 Creating a complete date table with features 00:04:00 Creating key measure table 00:03:00 Aggregation functions 00:06:00 The different versions of COUNT 00:14:00 SUMX - Row based calculations 00:09:00 Section 10: DAX - The CALCULATE function CALCULATE - The basics 00:11:00 Changing the context with FILTER 00:07:00 ALL 00:08:00 ALL SELECTED 00:03:00 ALL EXCEPT 00:07:00 Section 11: Power BI Service - Power BI Cloud How to go on now? 00:03:00 Power BI Pro vs Premium & Signing up 00:04:00 Exploring the interface 00:04:00 Discovering your workspace 00:03:00 Connecting Power BI Desktop & Cloud 00:04:00 Understanding datasets & reports 00:03:00 Working on reports 00:04:00 Updating reports from Power BI Desktop 00:04:00 Creating and working with workspaces 00:07:00 Installing & using a data gateway 00:13:00 Get Quick Insights 00:03:00 Creating dashboards 00:04:00 Sharing our results through Apps 00:10:00 Power BI Mobile App 00:05:00 Creating the layout for the Mobile App 00:04:00 Wrap up Power BI Cloud 00:07:00 Section 12: Row-Level Security Introduction 00:03:00 Creating a Row-Level Security 00:05:00 Row-Level Security in the Cloud 00:04:00 Row-Level Security & Data Model 00:05:00 Dynamic Row-Level Security 00:07:00 Dynamic Many-to-Many RLS 00:04:00 Hierarchical Row-Level Security 00:13:00 Section 13: More data sources JSON & REST API 00:10:00 Setting up a local MySQL database 00:14:00 Connecting to a MySQL database in Power BI 00:05:00 Connecting to a SQL database (PostgreSQL) 00:05:00 Section 14: Next steps to improve & stay up to date Congratulations & next steps 00:06:00 The End 00:01:00 Resources Resources - Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7 00:00:00 Assignment Assignment - Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7 04:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? 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 Complete Python Machine Learning & Data Science Fundamentals 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 course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals 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 As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00

Overcoming Overwhelm: A jam-packed 60 minute masterclass covering how to identify your energy leaks so you can create more success and impact in your career, without burnout.

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