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Curriculum of Bundle Course 01: Introduction to Data Analysis Module 01: Introduction Module 02: Agenda and Principles of Process Management Module 03: The Voice of the Process Module 04: Working as One Team for Improvement Module 05: Exercise: The Voice of the Customer Module 06: Tools for Data Analysis Module 07: The Pareto Chart Module 08: The Histogram Module 09: The Run Chart Module 10: Exercise: Presenting Performance Data Module 11: Understanding Variation Module 12: The Control Chart Module 13: Control Chart Example Module 14: Control Chart Special Cases Module 15: Interpreting the Control Chart Module 16: Control Chart Exercise Module 17: Strategies to Deal with Variation Module 18: Using Data to Drive Improvement Module 19: A Structure for Performance Measurement Module 20: Data Analysis Exercise Module 21: Course Project Module 22: Test your Understanding Course 02: Python for Data Analysis Welcome, Course Introduction & overview, and Environment set-up Python Essentials Python for Data Analysis using NumPy Python for Data Analysis using Pandas Python for Data Visualization using matplotlib Python for Data Visualization using Seaborn Python for Data Visualization using pandas Python for interactive & geographical plotting using Plotly and Cufflinks Capstone Project - Python for Data Analysis & Visualization Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Python for Machine Learning - scikit-learn - Logistic Regression Model Python for Machine Learning - scikit-learn - K Nearest Neighbors Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Python for Machine Learning - scikit-learn - K Means Clustering Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Recommender Systems with Python - (Additional Topic) Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Course 03: Statistical Analysis 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 Course 04: SQL NoSQL Big Data and Hadoop Module 01: Introduction Module 02: Relational Database Systems Module 03: Database Classification Module 04: Key-Value Store Module 05: Document-Oriented Databases Module 06: Search Engines Module 07: Wide Column Store Module 08: Time Series Databases Module 09: Graph Databases Module 10: Hadoop Platform Module 11: Big Data SQL Engines Module 12: Distributed Commit Log Module 13: Summary Course 05: Complete Microsoft Power BI 2021 Module 01: Introduction Module 02: Preparing our Project Module 03: Data Transformation - The Query Editor Module 04: Data Transformation - Advanced Module 05: Creating a Data Model Module 06: Data Visualization Module 07: Power BI & Python Module 08: Storytelling with Data Module 09: DAX - The Essentials Module 10: DAX - The CALCULATE function Module 11: Power BI Service - Power BI Cloud Module 12: Row-Level Security Module 13: More data sources Module 14: Next steps to improve & stay up to date Course 06: Data Analysis in Excel Level 3 Course Modifying a Worksheet Working with Lists Analyzing Data Visualizing Data with Charts Using PivotTables and PivotCharts Working with Multiple Worksheets and Workbooks Using Lookup Functions and Formula Auditing Automating Workbook Functionality Creating Sparklines and Mapping Data Forecasting Data Course 07: Data Analytics with Tableau Module 01: Introduction to the Course Module 02: Project 1: Discount Mart (Sales and Profit Analytics) Module 03: Project 2: Green Destinations (HR Analytics) Module 04: Project 3: Superstore (Sales Agent Tracker) Module 05: Northwind Trade (Shipping Analytics) Module 06: Project 5: Tesla (Stock Price Analytics) Module 07: Bonus: Introduction to Database Concepts Module 08: Tableau Stories Course 08: Basic Google Data Studio Module 01: Introduction to GDS Module 02: Data Visualization Module 03: Geo-visualization Module 04: A Socio-Economic Case Study Course 09: Business Analytics Module 01: What is business analysis? Module 02: Strategy analysis Module 03: Collaboration Module 04: Requirements analysis and Design definition Module 05: Requirements lifecycle management Module 06: Solution quality Module 07: Stakeholder management Module 08: BA Governance Module 09: Legal notes and Copyright information Course 10: Complete Introduction to Business Data Analysis Level 3 Module 1: Statistics Fundamentals Module 2: Data Analysis Module 3: Probability Module 4: Random Variables and Discrete Distributions Module 5: Continuous Distributions Module 6: Sampling Distributions Module 7: Confidence Interval Module 8: Hypothesis Testing with One Sample Module 9: Hypothesis Testing with Two Samples Module 10: The Chi-Square Distribution Module 11: F Distribution and One-Way ANOVA Module 12: Correlation analysis Module 13: Simple Linear Regression Analysis Course 11: Business Intelligence and Data Mining Masterclass Module 01: What is Business Intelligence? Module 02: Starting Case in understanding BI needs in diff phase of business Module 03: Decision Making Process and Need of IT systems Module 04: Problem Structure and Decision Support System Module 05: Introduction to BI Applications Module 06: Dashboard presentation systems Module 07: Different Types of Charts used in 131 Dashboards Module 08: Good Dashboard and BSC Module 09: Examples of Bad Dashboards 1 Module 10: Examples of Bad Dashboards 2 And much more... Course 12: Research Methods in Business Section 01: Applied Project & Research Methods in Business Section 02: Writing a Purpose / Quantitative and Qualitative Research Approaches Section 03: Mixed Method Research Approaches, Ethical Considerations & Writing Effectively Written Methodology Part 3 !@@ Section 04: Writing Data Collection Tools, Qualitative & Quantitative Data Analysis Section 05: Comparing Findings to Literature and Writing the Final Paper Course 13: Computer Science: Graph Theory Algorithms Module 00: Promo Module 01: Introduction Module 02: Common Problem Module 03: Depth First Search Module 04: Breadth First Search Module 05: Breadth First Search Shortest Path on a Grid And much more... Course 14: Data Protection and Data Security Level 2 GDPR Basics GDPR Explained Lawful Basis for Preparation Rights and Breaches Responsibilities and Obligations CPD 165 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this Data Analysis and Data Science bundle. Requirements Our Data Analysis and Data Science course is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices. Career path Having this Data Analysis and Data Science expertise will increase the value of your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included You will get the PDF Certificate for the title course (Introduction to Data Analysis) absolutely Free! Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (Introduction to Data Analysis) absolutely Free! Other Hard Copy certificates are available for £10 each. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

Duration 4 Days 24 CPD hours This course is intended for This course is for experienced cloud security engineers who have taken a previous certification in the security, compliance and identity portfolio. Specifically, students should have advanced experience and knowledge in a wide range of security engineering areas, including identity and access, platform protection, security operations, securing data, and securing applications. They should also have experience with hybrid and cloud implementations. Beginning students should instead take the course SC-900: Microsoft Security, Compliance, and Identity Fundamentals. This is an advanced, expert-level course. Although not required to attend, students are strongly encouraged to have taken and passed another associate level certification in the security, compliance and identity portfolio (such as AZ-500, SC-200 or SC-300) before attending this class. This course prepares students with the expertise to design and evaluate cybersecurity strategies in the following areas: Zero Trust, Governance Risk Compliance (GRC), security operations (SecOps), and data and applications. Students will also learn how to design and architect solutions using zero trust principles and specify security requirements for cloud infrastructure in different service models (SaaS, PaaS, IaaS). Prerequisites Highly recommended to have attended and passed one of the associate level certifications in the security, compliance and identity portfolio (such as AZ-500T00 Microsoft Azure Security Technologies, SC-200T00: Microsoft Security Operations Analyst, or SC-300T00: Microsoft Identity and Access Administrator.) Advanced experience and knowledge in identity and access, platform protection, security operations, securing data and securing applications. Experience with hybrid and cloud implementations. 1 - Introduction to Zero Trust and best practice frameworks Zero Trust initiatives Zero Trust technology pillars part 1 Zero Trust technology pillars part 2 2 - Design solutions that align with the Cloud Adoption Framework (CAF) and Well-Architected Framework (WAF) Define a security strategy Cloud Adoption Framework secure methodology Design security with Azure Landing Zones The Well-Architected Framework security pillar 3 - Design solutions that align with the Microsoft Cybersecurity Reference Architecture (MCRA) and Microsoft cloud security benchmark (MCSB) Design solutions with best practices for capabilities and controls Design solutions with best practices for attack protection 4 - Design a resiliency strategy for common cyberthreats like ransomware Common cyberthreats and attack patterns Support business resiliency Ransomware protection Configurations for secure backup and restore Security updates 5 - Case study: Design solutions that align with security best practices and priorities Case study description Case study answers Conceptual walkthrough Technical walkthrough 6 - Design solutions for regulatory compliance Translate compliance requirements into a security solution Address compliance requirements with Microsoft Purview Address privacy requirements with Microsoft Priva Address security and compliance requirements with Azure policy Evaluate infrastructure compliance with Defender for Cloud 7 - Design solutions for identity and access management Design cloud, hybrid and multicloud access strategies (including Azure AD) Design a solution for external identities Design modern authentication and authorization strategies Align conditional access and Zero Trust Specify requirements to secure Active Directory Domain Services (AD DS) Design a solution to manage secrets, keys, and certificates 8 - Design solutions for securing privileged access The enterprise access model Design identity governance solutions Design a solution to secure tenant administration Design a solution for cloud infrastructure entitlement management (CIEM) Design a solution for privileged access workstations and bastion services 9 - Design solutions for security operations Design security operations capabilities in hybrid and multicloud environments Design centralized logging and auditing Design security information and event management (SIEM) solutions Design solutions for detection and response Design a solution for security orchestration, automation, and response (SOAR) Design security workflows Design threat detection coverage 10 - Case study: Design security operations, identity and compliance capabilities Case study description Case study answers Conceptual walkthrough Technical walkthrough 11 - Design solutions for securing Microsoft 365 Evaluate security posture for collaboration and productivity workloads Design a Microsoft 365 Defender solution Design configurations and operational practices for Microsoft 365 12 - Design solutions for securing applications Design and implement standards to secure application development Evaluate security posture of existing application portfolios Evaluate application threats with threat modeling Design security lifecycle strategy for applications Secure access for workload identities Design a solution for API management and security Design a solution for secure access to applications 13 - Design solutions for securing an organization's data Design a solution for data discovery and classification using Microsoft Purview Design a solution for data protection Design data security for Azure workloads Design security for Azure Storage Design a security solution with Microsoft Defender for SQL and Microsoft Defender for Storage 14 - Case study: Design security solutions for applications and data Case study description Case study answers Conceptual walkthrough Technical walkthrough 15 - Specify requirements for securing SaaS, PaaS, and IaaS services Specify security baselines for SaaS, PaaS, and IaaS services Specify security requirements for web workloads Specify security requirements for containers and container orchestration 16 - Design solutions for security posture management in hybrid and multicloud environments Evaluate security posture by using Microsoft Cloud Security Benchmark Design integrated posture management and workload protection Evaluate security posture by using Microsoft Defender for Cloud Posture evaluation with Microsoft Defender for Cloud secure score Design cloud workload protection with Microsoft Defender for Cloud Integrate hybrid and multicloud environments with Azure Arc Design a solution for external attack surface management 17 - Design solutions for securing server and client endpoints Specify server security requirements Specify requirements for mobile devices and clients Specify internet of things (IoT) and embedded device security requirements Secure operational technology (OT) and industrial control systems (ICS) with Microsoft Defender for IoT Specify security baselines for server and client endpoints Design a solution for secure remote access 18 - Design solutions for network security Design solutions for network segmentation Design solutions for traffic filtering with network security groups Design solutions for network posture management Design solutions for network monitoring 19 - Case study: Design security solutions for infrastructure Case study description Case study answers Conceptual walkthrough Technical walkthrough Additional course details: Nexus Humans SC-100T00 Microsoft Cybersecurity Architect 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 SC-100T00 Microsoft Cybersecurity Architect 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 3 Days 18 CPD hours This course is intended for Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Overview By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. In this course you will start with the absolute basics of Python, focusing mainly on data structures. Then you will delve into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python.This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. Introduction to Data Structure using Python Python for Data Wrangling Lists, Sets, Strings, Tuples, and Dictionaries Advanced Operations on Built-In Data Structure Advanced Data Structures Basic File Operations in Python Introduction to NumPy, Pandas, and Matplotlib NumPy Arrays Pandas DataFrames Statistics and Visualization with NumPy and Pandas Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame Deep Dive into Data Wrangling with Python Subsetting, Filtering, and Grouping Detecting Outliers and Handling Missing Values Concatenating, Merging, and Joining Useful Methods of Pandas Get Comfortable with a Different Kind of Data Sources Reading Data from Different Text-Based (and Non-Text-Based) Sources Introduction to BeautifulSoup4 and Web Page Parsing Learning the Hidden Secrets of Data Wrangling Advanced List Comprehension and the zip Function Data Formatting Advanced Web Scraping and Data Gathering Basics of Web Scraping and BeautifulSoup libraries Reading Data from XML RDBMS and SQL Refresher of RDBMS and SQL Using an RDBMS (MySQL/PostgreSQL/SQLite) Application in real life and Conclusion of course Applying Your Knowledge to a Real-life Data Wrangling Task An Extension to Data Wrangling

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Learn to build and deploy a RESTful API using ASP.Net Core, Entity framework, and enterprise-grade practices for a scalable as well as future-proofed web API solution along with understanding the folder structure and files. You will also implement various support tools for logging, documentation, security, and error handling.

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Equip yourself with the Data Analysis bundle to confidently navigate your career path or personal development journey. Enrol our Data Analysis bundle today and start your career growth! This Data Analysis Bundle Comprises the Following CPD Accredited Courses: Course 1: Data Analytics Course 2: Diploma in Data Analytics with Tableau Course 3: Google Data Studio: Data Analytics Course 4: Business Intelligence Analyst Course 5: Diploma in Business Analysis Course 6: Data Science & Machine Learning with Python Course 7: Spatial Data Visualization and Machine Learning in Python Course 8: Python Programming: Beginner To Expert Course 9: SQL For Data Analytics & Database Development Course 10: Easy to Advanced Data Structures Course 11: R Programming for Data Science Course 12: Data Center Training Essentials: General Introduction Course 13: Data Analysis In Excel Course 14: Data Protection Course 15: Microsoft Excel Complete Course Course 16: Career Development Plan Fundamentals Course 17: CV Writing and Job Searching Course 18: Learn to Level Up Your Leadership Course 19: Networking Skills for Personal Success Course 20: Ace Your Presentations: Public Speaking Masterclass Learning Outcome: By completing the Data Analysis, Business Intelligence, Business Analysis with Python & Tableau course, you will: Gain comprehensive insights into multiple fields. Foster critical thinking and problem-solving skills across various disciplines. Understand industry trends and best practices through the Data Analysis Bundle. Develop practical skills applicable to real-world situations. Enhance personal and professional growth with the Data Analysis Bundle. Build a strong knowledge base in your chosen course via the Data Analysis Bundle. Benefit from the flexibility and convenience of online learning. With the Data Analysis package, validate your learning with a CPD certificate. Each course in Data Analysis, Business Intelligence, Business Analysis with Python & Tableau bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development. Embrace the future of learning with the Data Analysis, Business Intelligence, Business Analysis with Python & Tableau, a rich anthology of 15 diverse courses. Each course in the Data Analysis bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisData Analysis, Business Intelligence, Business Analysis with Python & Tableau bundle will take you on a unique and enriching educational journey. The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Data Analysis, Business Intelligence, Business Analysis with Python & Tableau bundle offers you the flexibility and convenience to learn at your own pace. Make the Data Analysis package your trusted companion in your lifelong learning journey. CPD 200 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Data Analysis, Business Intelligence, Business Analysis with Python & Tableau bundle is perfect for: Data Enthusiasts: Keen to master data analysis tools like Python, Tableau, and Excel. Aspiring Business Analysts: Eager to derive actionable insights using data analysis techniques. IT Professionals: Broadening skills with data analysis, machine learning, and business intelligence. Career Changers: Seeking a solid foundation in data analysis for a professional pivot. Leadership Aspirants: Complementing data analysis expertise with leadership and presentation skills. Requirements Without any formal requirements, you can delightfully enrol in this Data Analysis, Business Intelligence, Business Analysis with Python & Tableau course. Career path Our bundle will prepare you for a range of careers, including: Data Analyst Business Intelligence Analyst Data Scientist Python Developer SQL Database Developer Business Analyst Data Protection Officer Certificates Certificate Of Completion Digital certificate - Included Certificate Of Completion Hard copy certificate - Included You will get a complimentary Hard Copy Certificate.