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This course aims to prepare individuals for the AWS Certified Solutions Architect Associate exam. It covers essential AWS services, cloud architecture design, deployment strategies, and best practices for managing various AWS components. Learning Outcomes: Understand the fundamental concepts of AWS Cloud Services and their application in real-world scenarios. Design and implement AWS Storage and Virtual Private Cloud (VPC) solutions. Learn how to design, implement, and manage Compute Services effectively. Master Identity and Access Management (IAM) and its best practices for secure access control. Explore Auto Scaling Solutions and Virtual Network Services to optimize AWS infrastructure. Gain proficiency in deploying applications and databases on AWS. Discover additional AWS services and their integration for comprehensive cloud solutions. Develop insights into achieving operational excellence with AWS services. Why buy this AWS Certified Solutions Architect Associate Preparation? 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 AWS Certified Solutions Architect Associate Preparation 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 AWS Certified Solutions Architect Associate Preparation 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 AWS Certified Solutions Architect Associate Preparation does not require you to have any prior qualifications or experience. You can just enrol and start learning.This AWS Certified Solutions Architect Associate Preparation 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 AWS Certified Solutions Architect Associate Preparation is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Section 01: Introduction Introduction 00:03:00 Section 02: Exam Tips and Tricks What is AWS? 00:02:00 Why use AWS? 00:03:00 How to Get Started with AWS 00:04:00 AWS Certifications 00:04:00 Preparation Resources 00:02:00 Benefits of Certification 00:02:00 AWS CSA-A Overview 00:04:00 What's New on the 2020 Updated Exam? 00:03:00 AWS CSA-A Exam Objectives 00:06:00 The Four Key Areas (Compute, Networking, Storage, and Databases) 00:04:00 Master the Knowledge Areas 00:02:00 Use the System 00:05:00 Take Notes 00:03:00 Be Mentally and Physically Prepared 00:04:00 Take the Exam 00:04:00 Section 03: AWS Cloud Services Overview Cloud Computing Defined 00:08:00 Benefits of Cloud Computing 00:10:00 Cloud Computing Models 00:07:00 History 00:07:00 Platform 00:06:00 Services, Part 1 00:10:00 Services, Part 2 00:08:00 Security and Compliance 00:07:00 Regions and Availability 00:06:00 Section 04: AWS Storage Design Storage Services 00:07:00 S3 Storage Class 00:07:00 S3 Terminology 00:09:00 S3 Advanced Features 00:08:00 Creating S3 Buckets Lab 00:08:00 S3 Bucket Properties 00:08:00 S3 Managing Objects Lab 00:11:00 Glacier 00:07:00 Setting up a Glacier Vault Lab 00:08:00 S3 and Tape Gateway 00:06:00 S3 Enhanced Features 00:08:00 Elastic Block Store (EBS) 00:08:00 Creating EBS Volumes Lab 00:07:00 Elastic File System (EFS) 00:07:00 Creating an EFS File System Lab 00:07:00 EFS and PrivateLink 00:03:00 Intro to Amazon FSx 00:06:00 Hands-on with FSx 00:06:00 Integrating on-Premises Storage 00:07:00 Storage Access Security Lab 00:10:00 Storage Performance 00:08:00 Section 05: Virtual Private Cloud (VPC) Virtual Private Cloud (VPC) Overview 00:10:00 Creating a VPC Lab 00:11:00 Configuring DHCP Options Lab 00:04:00 Elastic IP Addresses 00:07:00 Elastic Network Interfaces (ENIs) 00:05:00 Endpoints 00:07:00 VPC Peering 00:08:00 Creating a VPC Peering Connection Lab 00:10:00 Security Groups Overview 00:07:00 Network Address Translation (NAT) 00:11:00 Gateways (VPGs and CGWs) 00:08:00 VPN Configuration Option 00:04:00 Section 06: Compute Services Design EC2 Overview 00:11:00 EC2 Instance Types 00:11:00 EC2 Pricing 00:13:00 EBS and EC2 00:05:00 Section 07: Compute Services Implementation Launching an EC2 Linux Instance Lab 00:13:00 Configuring an EC2 Linux Instance Lab 00:08:00 Setting up an EC2 Windows Instance Lab 00:12:00 Shared Tenancy 00:05:00 Dedicated Hosts 00:08:00 Dedicated Instances 00:06:00 AMI Virtualization 00:12:00 Section 08: Compute Services Management Instance Management 00:09:00 Connecting to Instances Lab 00:09:00 Working with Security Groups 00:10:00 Working with Security Groups Lab 00:10:00 Advanced EC2 Management 00:06:00 AWS Batch 00:06:00 Elastic Container Service (ECS) 00:08:00 Elastic Beanstalk Environment 00:11:00 Section 09: Identity and Access Management (IAM) Identity and Access Management (IAM) Overview 00:07:00 Principals 00:10:00 Root User 00:06:00 Authentication 00:06:00 Authorization Policies 00:13:00 Multi-Factor Authentication 00:08:00 Key Rotation 00:10:00 Multiple Permissions 00:06:00 AWS Compliance Program 00:07:00 AWS Security Hub 00:06:00 Shared Responsibility Models 00:06:00 Section 10: IAM Best Practices User Accounts 00:11:00 Password Policies 00:09:00 Credential Rotation 00:06:00 Principle of Least Privilege 00:05:00 IAM Roles 00:08:00 Policy Conditions 00:08:00 CloudTrail 00:12:00 Section 11: Auto Scaling Solutions Auto Scaling Overview 00:06:00 Auto Scaling Groups 00:04:00 Termination Policies 00:07:00 Auto Scaling Configuration Lab 00:13:00 Launch Methods 00:04:00 Load Balancer Concepts 00:08:00 Elastic Load Balancing (ELB) 00:10:00 Section 12: Virtual Network Services DNS 00:14:00 Configuring DNS Lab 00:07:00 Configuring Route 53 Lab 00:13:00 Configuring ACLs and NACLs Lab 00:09:00 Flow Logs 00:07:00 Section 13: AWS Application Deployment Application and Deployment Services 00:04:00 Lambda 00:06:00 API Gateway 00:09:00 Kinesis 00:06:00 Kinesis Data Streams and Firehose 00:06:00 Kinesis Data Analytics 00:04:00 Reference Architectures 00:06:00 CloudFront 00:10:00 Web Application Firewall (WAF) 00:09:00 Simple Queue Service (SQS) 00:10:00 Simple Notification Service (SNS) 00:08:00 Simple Workflow (SWF) 00:07:00 Step Functions 00:05:00 OpsWorks 00:08:00 Cognito 00:04:00 Elastic MapReduce (EMR) 00:05:00 CloudFormation 00:10:00 CloudFormation Properties 00:03:00 CloudWatch 00:06:00 Trusted Advisor 00:07:00 Organizations 00:09:00 Section 14: AWS Database Design Database Types 00:08:00 Relational Databases 00:08:00 Database Hosting Methods 00:05:00 High Availability Solutions 00:06:00 Scalability Solutions 00:06:00 Database Security 00:08:00 Aurora 00:06:00 Redshift 00:11:00 DynamoDB 00:10:00 Section 15: Database Deployment DynamoDB Tables Lab 00:08:00 MySQL Lab 00:13:00 Configuration Lab 00:13:00 Backups Lab 00:04:00 Restore Lab 00:04:00 Snapshot Lab 00:08:00 Monitoring Lab 00:06:00 Section 16: Additional AWS Services Media Content Delivery 00:13:00 Desktop and Appstreaming 00:06:00 ElastiCache 00:05:00 Security Services Lab 00:12:00 Analytics Engines 00:11:00 Development Operations (DevOps) 00:12:00 AWS Solutions 00:05:00 AWS Transit Gateway 00:03:00 AWS Backup 00:04:00 AWS Cost Explorer 00:04:00 Section 17: Operational Excellence with AWS The Operational Excellence Process 00:08:00 Widget Makers Scenario 00:06:00 Resilient Design 00:08:00 Resilient Design Scenario 00:05:00 Performant Design 00:09:00 Performant Design Scenario 00:06:00 Secure Design 00:08:00 Secure Design Scenario 00:05:00 Cost Optimization 00:07:00 Cost Optimization Scenario 00:05:00 General Best Practices 00:07:00

Are you ready to be at the helm, steering the ship into a realm where data is the new gold? In the infinite world of data, where information spirals at breakneck speed, lies a universe rich in potential and discovery: the domain of Data Science and Visualisation. This 'Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3' course unravels the wonders of extracting meaningful insights using Python, the worldwide leading language of data experts. Harnessing the strength of Python, you'll delve deep into data analysis, experience the finesse of visualisation tools, and master the art of Machine Learning. The need to understand, interpret, and act on this data has become paramount, with vast amounts of data increasing the digital sphere. Envision a canvas where raw numbers are transformed into visually compelling stories, and machine learning models foretell future trends. This course provides a meticulous pathway for anyone eager to learn the data representation paradigms backed by Python's robust libraries. Dive into a curriculum rich with analytical explorations, visual artistry, and machine learning predictions. Learning Outcomes Understanding the foundations and functionalities of Python, focusing on its application in data science. Applying various Python libraries like NumPy and Pandas for effective data analysis. Demonstrating proficiency in creating detailed visual narratives using tools like matplotlib, Seaborn, and Plotly. Implementing Machine Learning algorithms in Python using scikit-learn, ranging from regression models to clustering techniques. Designing and executing a holistic data analysis and visualisation project, encapsulating all learned techniques. Exploring advanced topics, encompassing recommender systems and natural language processing with Python. Attaining the confidence to independently analyse complex data sets and translate them into actionable insights.   Video Playerhttps://studyhub.org.uk/wp-content/uploads/2021/03/Data-Science-and-Visualisation-with-Machine-Learning.mp400:0000:0000:00Use Up/Down Arrow keys to increase or decrease volume. Why buy this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3? 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 Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 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 Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 course for? Aspiring data scientists aiming to harness the power of Python. Researchers keen to enrich their analytical and visualisation skills. Analysts aiming to add machine learning to their toolkit. Developers striving to integrate data analytics into applications. Business professionals desiring data-driven decision-making capabilities. Career path Data Scientist: £55,000 - £85,000 Per Annum Machine Learning Engineer: £60,000 - £90,000 Per Annum Data Analyst: £30,000 - £50,000 Per Annum Data Visualisation Specialist: £45,000 - £70,000 Per Annum Natural Language Processing Specialist: £65,000 - £95,000 Per Annum Business Intelligence Developer: £40,000 - £65,000 Per Annum Prerequisites This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 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 £85 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 Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Visualisation with Machine Learning 00:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

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The Statistical Analysis Training Course is pivotal in the modern world, offering essential skills that are increasingly demanded across various industries. As businesses and organizations generate vast amounts of data, the ability to analyze and interpret this data becomes crucial. Learning from The Statistical Analysis Training Course equips individuals with expertise in key areas such as probability, hypothesis testing, regression analysis, and predictive analytics, enhancing their employability. In the UK, proficiency gained from this Statistical Analysis Training course can significantly boost job opportunities, with data analysts and statisticians earning an average salary of £35,000 to £50,000 annually. The demand for statistical analysis skills is on the rise, with the sector experiencing a growth rate of 33% over the past five years. Advantages of the Statistical Analysis Training course include a comprehensive understanding of both foundational and advanced statistical concepts, which are integral in roles across finance, healthcare, marketing, and technology. The Statistical Analysis Training Course ensures that learners are well-versed in modern analytical techniques, making them valuable assets in a data-driven economy. As the importance of data analytics continues to grow, so does the value of this training, making it an indispensable tool for career advancement. Key Features: CPD Certified Statistical Analysis Course Free Certificate from Reed CIQ Approved Statistical Analysis Course Developed by Specialist Lifetime Access Course Curriculum: Statistical Analysis Training Module 01: The Realm of Statistics Module 02: Basic Statistical Terms Module 03: The Center of the Data Module 04: Data Variability Module 05: Binomial and Normal Distributions Module 06: Introduction to Probability Module 07: Estimates and Intervals Module 08: Hypothesis Testing Module 09: Regression Analysis Module 10: Algorithms, Analytics and Predictions Module 11: Learning From Experience: The Bayesian Way Module 12: Doing Statistics: The Wrong Way Module 13: How We Can Do Statistics Better Learning Outcomes: Grasp fundamental statistical concepts for data analysis proficiency. Understand measures of central tendency and dispersion in datasets. Apply probability theory to make informed statistical decisions. Utilize hypothesis testing techniques to draw meaningful conclusions. Master regression analysis for predictive modelling and trend identification. Embrace Bayesian methods and enhance statistical inference capabilities. CPD 10 CPD hours / points Accredited by CPD Quality Standards Statistical Analysis Training 4:44:42 1: Module 01: The Realm of Statistics Preview 15:23 2: Module 02: Basic Statistical Terms 27:51 3: Module 03: The Center of the Data 10:00 4: Module 04: Data Variability 21:00 5: Module 05: Binomial and Normal Distributions 21:00 6: Module 06: Introduction to Probability 23:42 7: Module 07: Estimates and Intervals 21:35 8: Module 08: Hypothesis Testing 21:51 9: Module 09: Regression Analysis 21:00 10: Module 10: Algorithms, Analytics and Predictions 31:05 11: Module 11: Learning From Experience: The Bayesian Way 20:08 12: Module 12: Doing Statistics: The Wrong Way 23:39 13: Module 13: How We Can Do Statistics Better 25:28 14: CPD Certificate - Free 01:00 Who is this course for? This Statistical Analysis Training course is accessible to anyone eager to learn more about this topic. Through this course, you'll gain a solid understanding of Statistical Analysis Training. Moreover, this course is ideal for: Aspiring data analysts seeking statistical foundations for career advancement. Professionals in research roles aiming to refine statistical analysis skills. Students pursuing degrees in mathematics, economics, or related disciplines. Business professionals looking to leverage data-driven insights for strategic decisions. Anyone interested in enhancing statistical literacy and analytical reasoning abilities. Requirements There are no requirements needed to enrol into this Statistical Analysis Training course. We welcome individuals from all backgrounds and levels of experience to enrol into this Statistical Analysis Training course. Career path After finishing this Statistical Analysis Training course you will have multiple job opportunities waiting for you. Some of the following Job sectors of Statistical Analysis Training are: Data Analyst - £30K to £45K/year. Statistician - £35K to £50K/year. Market Research Analyst - £25K to £40K/year. Business Intelligence Analyst - £35K to £55K/year. Healthcare Data Analyst - £30K to £50K/year. Certificates Digital certificate Digital certificate - Included Reed Courses Certificate of Completion Digital certificate - Included Will be downloadable when all lectures have been completed.

A course by Sekhar Metla IT Industry Expert RequirementsNo prior technical experience is required! All you need a computer!No SQL experience needed. You will learn everything you need to knowNo software is required in advance of the course (all software used in the course is free) Audience Beginner SQL, Data Science and Analytics - developers curious about SQL Career Anyone who wants to generate new income streams Anyone who works with data analytics, or databases! Anyone who wants to become Business intelligence developer Anyone who wants to start their own business or become freelance Anyone who wants to become a Data Science developer If you work in: marketing, finance, accounting, operations, sales, manufacturing, healthcare, financial services, or any other industry/function that collects information Someone who wants to learn skills that give them the potential to earn near SIX figures! Audience Beginner SQL, Data Science and Analytics - developers curious about SQL Career Anyone who wants to generate new income streams Anyone who works with data analytics, or databases! Anyone who wants to become Business intelligence developer Anyone who wants to start their own business or become freelance Anyone who wants to become a Data Science developer If you work in: marketing, finance, accounting, operations, sales, manufacturing, healthcare, financial services, or any other industry/function that collects information Someone who wants to learn skills that give them the potential to earn near SIX figures!

AI in Project Management: The Next Generation of Project Decision Making Project managers need to make critical project decisions on a daily basis. They are confronted with increasing complexities, high ambiguity and the need to process an exponentially growing amount of data and information in order to make informed and good decisions. This leads to an increasing risk of project failure - meanwhile, the project management industry is already challenged with ongoing low project success rates, caused by often massive failures of projects. Project Data Analytics and Artificial Intelligence (AI) are expected to fill the gap by providing analytical and unbiased capabilities that go beyond human possibilities, towards a data-driven and fact-based decision-making approach. While there is little doubt that AI as a trending technology will disrupt the project management practice and augment today's project management capabilities, AI cannot be seen as just another new tool to make project management more effective. Rather, AI will act as a complement to human intelligence, requiring a collaborative approach and, accordingly, a significant change in project culture and peoples' mindset. Today's project decisions are usually driven by human intuition, experience, leadership, and often do not follow any rational logic. Project decision-makers will be required to abandon such an approach and shift to a data-driven, decision-making approach. This session will provide an overview of the expected changes from AI-driven project management, the resulting impact on project decision making and changes in project culture, and what actions can be taken by project professionals to match their beliefs and behaviours with the new project culture. Learning goals: Gain insights into how AI for project management will significantly change decision-making in projects Gain an understanding of how to transition to a new AI-powered project culture

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Architects and operators who build and manage data analytics pipelines Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a batch data analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Introduction to Amazon EMR Using Amazon EMR in analytics solutions Amazon EMR cluster architecture Interactive Demo 1: Launching an Amazon EMR cluster Cost management strategies Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage Storage optimization with Amazon EMR Data ingestion techniques Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR Apache Spark on Amazon EMR use cases Why Apache Spark on Amazon EMR Spark concepts Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell Transformation, processing, and analytics Using notebooks with Amazon EMR Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive Using Amazon EMR with Hive to process batch data Transformation, processing, and analytics Practice Lab 2: Batch data processing using Amazon EMR with Hive Introduction to Apache HBase on Amazon EMR Module 5: Serverless Data Processing Serverless data processing, transformation, and analytics Using AWS Glue with Amazon EMR workloads Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions Module 6: Security and Monitoring of Amazon EMR Clusters Securing EMR clusters Interactive Demo 3: Client-side encryption with EMRFS Monitoring and troubleshooting Amazon EMR clusters Demo: Reviewing Apache Spark cluster history Module 7: Designing Batch Data Analytics Solutions Batch data analytics use cases Activity: Designing a batch data analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization. Overview After completing this course delegates will be capable of writing effective R code to manipulate, analyse and visualise data to enable their organisations make better, data-driven decisions. This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualisation in R. Course Outline Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. The R programming language is one of the most powerful and flexible tools in the data analytics toolkit. This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualisation in R. Mastery of these techniques will allow delegates to immediately add value in their work place by extracting valuable insight from company data to allow better, data-driven decisions. The course will explore the following topics through a series of interactive workshop sessions: What is R? Basic R programming conventions Data structures in R Accessing data in R Descriptive statistics in R Statistical analysis in R Data manipulation in R Data visualisation in R Additional course details: Nexus Humans Beginning Data Analytics With R training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Beginning Data Analytics With R course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines. Completed either AWS Technical Essentials or Architecting on AWS Completed Building Data Lakes on AWS Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a data warehouse analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Using Amazon Redshift in the Data Analytics Pipeline Why Amazon Redshift for data warehousing? Overview of Amazon Redshift Module 2: Introduction to Amazon Redshift Amazon Redshift architecture Interactive Demo 1: Touring the Amazon Redshift console Amazon Redshift features Practice Lab 1: Load and query data in an Amazon Redshift cluster Module 3: Ingestion and Storage Ingestion Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API Data distribution and storage Interactive Demo 3: Analyzing semi-structured data using the SUPER data type Querying data in Amazon Redshift Practice Lab 2: Data analytics using Amazon Redshift Spectrum Module 4: Processing and Optimizing Data Data transformation Advanced querying Practice Lab 3: Data transformation and querying in Amazon Redshift Resource management Interactive Demo 4: Applying mixed workload management on Amazon Redshift Automation and optimization Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster Module 5: Security and Monitoring of Amazon Redshift Clusters Securing the Amazon Redshift cluster Monitoring and troubleshooting Amazon Redshift clusters Module 6: Designing Data Warehouse Analytics Solutions Data warehouse use case review Activity: Designing a data warehouse analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures