This course will allow you to explore the potential of self-service business intelligence using Power BI Desktop to analyse and connect to different sources of data, creating Relationships between those different datasets, Query the data using Shaping and data Modelling, to create Visualizations, and publish Reports to different platforms . Course Objectives At the end of this course you will be able to: Connect to data from different sources. Use the Query Editor Perform Power BI desktop data Shaping and Transformation. Create Power BI desktop Modelling. Create Power BI desktop Visualizations and Reports. ' 1 year email support service Take a closer look at the consistent excellent feedback from our growing corporate clients visiting our site ms-officetraining co uk With more than 20 years experience, we deliver courses on all levels of the Desktop version of Microsoft Office and Office 365; ranging from Beginner, Intermediate, Advanced to the VBA level and Business Intelligence. Our trainers are Microsoft certified professionals with a proven track record with several years experience in delivering public, one to one, tailored and bespoke course Tailored training courses: in in company training, you can choose to run the course exactly as they are outlined by us or we can customise it so that it meets your specific needs. A tailored or bespoke course will follow the standard outline but may be adapted to your specific organisational needs. Please visit our site (ms-officetraining co uk) to get a feel of the excellent feedback our courses have had and look at other courses you might be interested in. Introduction to Power BI Power BI Jargon explained A quick look at Power BI Desktop A quick look at the Power BI service Helpful resources Power BI and Excel Introduction to using Excel data in Power BI Upload Excel data to Power BI Import Power View and Power Pivot to Power BI Getting started with Power BI Desktop Overview of Power BI Desktop Accessing Help and Helpful resources Connect to data sources in Power BI Desktop Shaping and Transforming Data with Query Editor Introduction to the Query Editor Data Sources Power BI Desktop can Connect to Introduction to Steps and M code Combining Data Using Merge and Append Queries Data Type Properties Working with Delimiters Clean and transform your data with the Query Editor Text Specific Transformation Tools Number Specific Transformation Tools Date Specific Transformation Tools Split and Merge columns Creating an Index Column Adding Conditional Columns Columns From Examples Grouping and Aggregating data Pivoting and Unpivoting Using filters Modeling the data Introduction to modeling your data How to manage your data relationships Create calculated columns Optimizing data models Create calculated measures Show Values As and Quick Measures Create calculated tables Explore your time-based data Introduction to DAX DAX calculation types DAX functions Visualizations Introduction to visuals in Power BI Create and customize simple visualizations Modify colors in charts and visuals Shapes, text boxes, and images Page layout and formatting Group interactions among visualizations Visual hierarchies and drill-down Using custom visualizations Create a KPI Visualization Geo-Data and Maps Reports, Publishing and Sharing Introduction to the Power BI service Quick insights in Power BI Create and configure a dashboard Ask questions of your data with natural language Create custom Q&A suggestions Share dashboards with your organization Introduction to content packs, security, and groups Publish Power BI Desktop reports Print and export dashboards and reports Create groups in Power BI Use content packs Update content packs Publish to web Who is this course for? Who is this course for? This course facilitates you with knowledge on the potential for Power BI Desktop to analyse and connect to different sources of data, creating Relationships between those different datasets, Query the data using Shaping and data Modelling, and to create Visualizations, and publish Reports to different platforms. Requirements Requirements Before attending this course, delegates should have: - A general knowledge of database concepts (fields, records and relationships) - Familiarity with Excel. Career path Career path Business Intelligence Data Analysis ETL & Data Warehousing
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Overview Dive into the intricacies of interpretative phenomenological analysis and develop a solid foundation in this area with our exclusive Interpretative Phenomenological Analysis Course. This course will provide a step-by-step understanding of the IPA research process in no time. From the course, you will acquire a clear understanding of Interpretative Phenomenological Analysis. You will know how to plan for the IPA study. The course will also cover detailed lessons on data collection and data analysis. In addition, you will learn how to transfer the results to the papers. When you complete the course, you will receive a certificate of achievement. This certificate will demonstrate your expertise in this area and help you pursue a career in the related industry. Course Preview Learning Outcomes Understand the definition of Interpretative Phenomenological Analysis Know how to plan an IPA research study Familiarise yourself with the data collection method Develop the skills to analyse the data and find the results Know the steps of writing the paper Why Take This Course From John Academy? Affordable, well-structured and high-quality e-learning study materials Engaging tutorial videos, materials from the industry-leading experts Opportunity to study in a user-friendly, advanced online learning platform Efficient exam systems for the assessment and instant result Earn UK & internationally recognised accredited qualification Easily access the course content on mobile, tablet, or desktop from anywhere, anytime Excellent career advancement opportunities Get 24/7 student support via email. What Skills Will You Learn from This Course? Panning Research Data Collection Data Analysis Who Should Take This Interpretative Phenomenological Analysis Course? Whether you're an existing practitioner or an aspiring professional, this course is an ideal training opportunity. It will elevate your expertise and boost your CV with key skills and a recognised qualification attesting to your knowledge. Are There Any Entry Requirements? This Interpretative Phenomenological Analysis Course is available to all learners of all academic backgrounds. But learners should be aged 16 or over to undertake the qualification. And a good understanding of the English language, numeracy, and ICT will be helpful. Certificate of Achievement After completing this course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates & Transcripts can be obtained either in Hardcopy at £14.99 or in PDF format at £11.99. Career Pathâ This exclusive Interpretative Phenomenological Analysis Course will equip you with effective skills and abilities and help you explore career paths such as Qualitative Researcher Clinical Psychologist Counselling Psychologist Qualitative Data Analyst Research Psychologist Educational Researcher Module 01: Introduction to Interpretative Phenomenological Analysis (IPA) Introduction to Interpretative Phenomenological Analysis (IPA) 00:13:00 Module 02: IPA and the Theory IPA and the Theory 00:10:00 Module 03: Planning an IPA Research Study Planning an IPA Research Study 00:15:00 Module 04: Collecting Data Collecting Data 00:15:00 Module 05: Analysis Analysis 00:15:00 Module 06: Writing Writing 00:11:00 Module 07: Advanced Designs and Innovative Approaches Advanced Designs and Innovative Approaches 00:15:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00
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Course Overview: Are you ready to unlock the world of digital possibilities by understanding the art and science of data analytics? We live in an era where data back every decision and every action requires insightful analysis. Clive Humby said, "Data is the new oil, and it's the new oil, so it's an invaluable resource for companies worldwide. This comprehensive course covers a broad spectrum of data analytics, starting with an engaging 'Introduction to the World of Data,' before delving into the fundamental components like the 'Basics of Data Analytics,' 'Statistics for Data Analytics,' and the 'Actions Taken in the Data Analysis Process.' Each subsequent module is carefully designed to guide you through various stages of data analytics. You'll explore 'Data Mining,' work with 'Excel for Data Analytics,' and discover 'Tools for Data Analytics.' The curriculum wraps up with a focus on 'Data-Analytic Thinking' and 'Data Visualisation.' Enrol today and start your journey to becoming a data analytics expert! Key Features of the Course: This course comes with a CPD certificate, affirming your proficiency in data analytics. It offers 24/7 learning assistance to ensure you get the most out of your learning journey. The content is presented in easy-to-understand and engaging learning materials, carefully curated to make your journey in data analytics enlightening. Who is This Course For? This course suits professionals seeking to leverage data analytics in their respective fields, individuals aspiring to venture into the data science arena, and students keen to acquire contemporary skills for the digital age. What You Will Learn: Understand the fundamental concepts of data analytics. Apply statistical techniques to analyse large data sets. Implement effective strategies for data collection and storage. Master the art of data mining and extraction of valuable insights. Utilise Excel and other tools effectively for data analysis. Develop a data-analytic mindset for problem-solving. Translate data insights into compelling visualisations. Why Enrol in This Course: This course will open doors to many opportunities. You will learn from top-notch professionals, utilise quality learning materials and have access to trending and recently updated curriculum. Requirements: A basic understanding of computers A willingness to learn Career Path: The expertise gained from this Data Analytics course can pave your way into a variety of professions, such as: Data Analyst (£30,000-£60,000) Business Analyst (£35,000-£70,000) Market Research Analyst (£27,000-£55,000) Operations Analyst (£31,000-£62,000) Quantitative Analyst (£45,000-£85,000) Data Scientist (£50,000-£90,000) Data Engineer (£35,000-£75,000) Certification: On successful completion of this course, you will receive a CPD certificate, testifying your mastery in the field of data analytics. With this recognition, you can confidently showcase your skills and expertise in the professional world. Data analytics is a powerful tool that can be used to make better decisions, improve efficiency, and drive innovation. If you want to join this growing field, this course is for you! FAQ What do you mean by data analytics? Data analytics is the process of collecting, cleaning, analyzing, and interpreting data to extract insights. What data analytics actually do? Data analytics helps businesses make better decisions by providing them with insights into their data. This can include insights into customer behavior, market trends, and product performance. What are the 5 data analytics? The 5 data analytics are: Descriptive analytics: This type of analytics describes what has happened in the past. Diagnostic analytics: This type of analytics identifies the root cause of problems. Predictive analytics: This type of analytics predicts what will happen in the future. Prescriptive analytics: This type of analytics recommends actions to take based on the predictions. Visual analytics: This type of analytics uses visual representations of data to make it easier to understand. What is data analytics examples? Here are some examples of data analytics: A retailer uses data analytics to track customer behaviour and identify trends. A bank uses data analytics to predict which customers are likely to default on their loans. A healthcare provider uses data analytics to identify patients who are at risk for certain diseases. What are 4 types of analytics? The 4 types of analytics are: Descriptive analytics: This type of analytics describes what has happened in the past. Diagnostic analytics: This type of analytics identifies the root cause of problems. Predictive analytics: This type of analytics predicts what will happen in the future. Prescriptive analytics: This type of analytics recommends actions to take based on the predictions. Why do we use data analytics? We use data analytics to make better decisions, improve efficiency, and drive innovation. Here are some of the benefits of using data analytics: Better decision-making: Data analytics can help businesses make better decisions by providing them with insights into their data. Improved efficiency: Data analytics can help businesses improve efficiency by identifying areas where they can save time and money. Driven innovation: Data analytics can help businesses drive innovation by identifying new opportunities and trends. Course Curriculum 13 sections • 13 lectures • 12:25:00 total length •Introduction to the World of Data: 01:00:00 •Basics of Data Analytics: 00:40:00 •Statistics for Data Analytics: 01:00:00 •Actions Taken in the Data Analysis Process: 00:55:00 •Gathering the Right Information: 01:00:00 •Storing Data: 01:15:00 •Data Mining: 01:00:00 •Excel for Data Analytics: 01:20:00 •Tools for Data Analytics: 01:20:00 •Data-Analytic Thinking: 01:10:00 •Data Visualisation That Clearly Describes Insights: 00:45:00 •Data Visualization Tools: 01:00:00 •Assignment - Data Analytics: 00:00:00
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Duration 3 Days 18 CPD hours This course is intended for Organizations today demand a professional-level cybersecurity threat intelligence analyst who can extract the intelligence from data by implementing various advanced strategies. Such professional-level programs can only be achieved when the core of the curricula maps with and is compliant to government and industry published threat intelligence frameworks. Ethical Hackers Security Practitioners, Engineers, Analysts, Specialist, Architects, and Managers Threat Intelligence Analysts, Associates, Researchers, Consultants Threat Hunters SOC Professionals Digital Forensic and Malware Analysts Incident Response Team Members Any mid-level to high-level cybersecurity professionals with a minimum of 2 years of experience. Individuals from the information security profession and who want to enrich their skills and knowledge in the field of cyber threat intelligence. Individuals interested in preventing cyber threats. Overview This program will benefit students who are looking to build effective threat intelligence for their organization in order to combat modern-day cyber-attacks and prevent future attacks. Certified Threat Intelligence Analyst (C|TIA) is designed and developed in collaboration with cybersecurity and threat intelligence experts across the globe to help organizations identify and mitigate business risks by converting unknown internal and external threats into known threats. It is a comprehensive, specialist-level program that teaches a structured approach for building effective threat intelligence. Introduction to Threat Intelligence Understanding Intelligence Understanding Cyber Threat Intelligence Overview of Threat Intelligence Lifecycle and Frameworks Cyber Threats and Kill Chain Methodology Understanding Cyber Threats Understanding Advanced Persistent Threats (APTs) Understanding Cyber Kill Chain Understanding Indicators of Compromise (IoCs) Requirements, Planning, Direction, and Review Understanding Organization?s Current Threat Landscape Understanding Requirements Analysis Planning Threat Intelligence Program Establishing Management Support Building a Threat Intelligence Team Overview of Threat Intelligence Sharing Reviewing Threat Intelligence Program Data Collection and Processing Overview of Threat Intelligence Data Collection Overview of Threat Intelligence Collection Management Overview of Threat Intelligence Feeds and Sources Understanding Threat Intelligence Data Collection and Acquisition Understanding Bulk Data Collection Understanding Data Processing and Exploitation Data Analysis Overview of Data Analysis Understanding Data Analysis Techniques Overview of Threat Analysis Understanding Threat Analysis Process Overview of Fine-Tuning Threat Analysis Understanding Threat Intelligence Evaluation Creating Runbooks and Knowledge Base Overview of Threat Intelligence Tools Intelligence Reporting and Dissemination Overview of Threat Intelligence Reports Introduction to Dissemination Participating in Sharing Relationships Overview of Sharing Threat Intelligence Overview of Delivery Mechanisms Understanding Threat Intelligence Sharing Platforms Overview of Intelligence Sharing Acts and Regulations Overview of Threat Intelligence Integration
Duration 1 Days 6 CPD hours This course is intended for This class is intended for the following: Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform. Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports. Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists. Overview This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform. This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. Introducing Google Cloud Platform Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products. Compute and Storage Fundamentals CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline. Data Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc. Scaling Data Analysis Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset. Machine Learning Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs. Data Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing. Summary Why GCP? Where to go from here Additional Resources Additional course details: Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals 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 Google Cloud Platform Big Data and Machine Learning Fundamentals 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.
If you are working on data science projects and want to create powerful visualization and insights as an outcome of your projects or are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course exclusively focuses on explaining how to build fantastic visualizations using Python. It covers more than 20 types of visualizations using the most popular Python visualization libraries, such as Matplotlib, Seaborn, and Bokeh along with data analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.