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13 Data Analysis Expressions courses in Birmingham delivered Live Online
Introduction to R Programming
By Nexus Human
Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently
Mastering Scala with Apache Spark for the Modern Data Enterprise (TTSK7520)
By Nexus Human
Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.
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Show all 95Introduction to DAX for Power BI
By Nexus Human
Duration 2 Days 12 CPD hours This course is intended for This course is intended for business users who have been using Power BI to build analytic solutions and are ready to take advantage of the power and flexibility that DAX provides. Learning DAX is a very common 'next step' for experienced Power BI users. Overview At course completion, you should be able to describe DAX syntax, data types, and errors use DAX to create calculated columns, measures, and tables explain how DAX calculations are evaluated, along with the differences between row context and filter context configure and use Time Intelligence to perform common time-based calculations, for example to-date calculations, year-over-year analysis, moving averages, etc. create calculated columns and measures that use data from multiple tables in the data model write measures that handle error conditions gracefully use DAX to enhance the Power BI user experience use DAX Studio to connect to a Power BI data model and execute simple queries Welcome to Introduction to DAX for Power BI. This two-day instructor-led course is intended for business users who have been using Power BI and want to use DAX to create custom calculations in their data models. In this class, you will be introduced to using Data Analysis Expressions (DAX), which is the expression language that is used to create custom calculations in the Power BI Data model. The course covers some of the theoretical underpinnings of the data model and the DAX language, but the emphasis is on using DAX to solve common business problems. You will learn how to write your own calculated columns, measures, and tables, how to visualize the way Power BI computes DAX calculations, and how to troubleshoot custom code. MODULE 1: GETTING STARTED WITH DAX INTRODUCTION TODAX CREATING OBJECTS WITHDAX CONTEXT AND RULES OF EVALUATION VARIABLES,COMMENTS,AND TESTING MODULE 2: PERFORMING BASIC CALCULATIONS GETTING STARTED IMPLICIT MEASURES ADDING QUICK MEASURES WORKING WITH DAX DATA TYPES . DOING BASIC MATH USING LOGIC IN YOUR CALCULATIONS USING THE IF() FUNCTION NESTED IF() USING THE SWITCH() FUNCTION ADVANCED SWITCH() LOGICAL OPERATORS & FUNCTIONS: ||, OR(), &&, AND(), NOT() AGGREGATING AND SUMMARIZING DATA THE SUM() FUNCTION MODULE 3: WORKING WITH CONTEXT IN THE DATA MODEL CONTEXT DEFINED DATA MODELING BASICS INTRODUCTION TO DIMENSIONAL MODELING RELATIONSHIPS AND THEIR EFFECT ON THE EVALUATION CONTEXT GETTING DATA FROM OTHER TABLES USING RELATED() AND RELATEDTABLE LOOKING UP DATA WITHOUT USING RELATIONSHIPS MODIFYING THE CONTEXT USING CALCULATE() MODULE 4: PERFORMING MORE ADVANCED CALCULATIONS THE DAX ITERATOR FUNCTIONS USING TABLE MANIPULATION FUNCTIONS MODULE 5: WORKING WITH TIME PERFORMING DATE CALCULATIONS WORKING WITH DATE TABLES GENERATING A DATE TABLE WITH THE CALENDAR() FUNCTION DEFINING CUSTOM OPERATING PERIODS YTD, QTD, AND MTD CALCULATIONS CUSTOM TO-DATE CALCULATIONS FINDING YEAR-OVER-YEAR CHANGE FINDING MOVING AVERAGES MODULE 6: ENHANCING THE USER EXPERIENCE CONTROLLING VISIBILITYOF YOUR MEASURES USING WHAT-IF PARAMETERS ADDING BANDING USING DAX TO PROVIDE ROW-LEVEL SECURITY
Mastering the Power BI Key Fundamentals
By Packt
This course teaches the fundamental concepts of DAX in Power BI. If you have the questions: How do I learn DAX? What is the best way to learn DAX fast?-then this is the best course for you. This course teaches fundamental concepts and does not cover visualization or various advanced DAX patterns for specific questions.
DAX In A Day (DAXIAD)
By Online Productivity Training
OVERVIEW Prerequisites—DIAD training and Advanced Data Modeling and Shaping training or equivalent working experience This course has been designed specifically for experienced model developers and gives a more advanced treatment of DAX formulas than either DIAD or the PL-300 course. We recommend that attendees have prior experience working with Power BI Desktop to create data models. During this course you will review: Writing DAX formulas. Defining calculated tables and columns. Defining measures. Using DAX iterator functions. Modifying filter context. Using DAX time intelligence functions. After completing this training, the attendees should be able to work with Data Analysis Expressions (DAX), perform calculations and define common business calculations for use in reports, address performance and functionality concerns. COURSE BENEFITS: Understand Analytic queries in Power BI Create calculated tables, calculated columns and measures Use DAX functions and operators to build DAX formulas Use DAX iterator functions Create formulas that manipulate the filter context Use DAX time intelligence functions WHO IS THE COURSE FOR? Analysts with experience of Power BI wishing to develop more advanced formulas in DAX Power BI developers who wish to deepen their understanding of the process of calculating formulas so as to make development faster and more reliable LAB OUTLINE Lab 1 Setup Connect to data and understand the objectives of the future labs Lab 2 Write DAX Formulas For Power BI Create a measure Use variables in the measure definition Lab 3 Add Calculated Table And Columns Duplicate a table Create a hierarchy Create a date table Add calculated columns Lab 4 Add Measures To Power BI Desktop Models Add an implicit measure to a report Add an explicit measure Add a compound measure Add a quick measure Lab 5 Use DAX Iterator Functions In A Power BI Desktop Model Complex summarization Higher grain summarization Create ranking measure Lab 6 Modify DAX Filter Context In Power BI Desktop Models Apply Boolean expression filter Remove filters: use ALL Remove filters: use AllSelected Preserve filters: use KeepFilters HASONEVALUE ISINSCOPE Context transition Lab 7 Use DAX Time Intelligence Functions In Power BI Desktop Models TOTALYTD SAMEPERIODLASTYEAR Calculate new occurrences Snapshot calculations
Data Analysts' Toolbox - Excel, Python, Power BI, Alteryx, Qlik Sense, R, Tableau
By Packt
This course explains how huge chunks of data can be analyzed and visualized using the power of the data analyst toolbox. You will learn Python programming, advanced pivot tables' concepts, the magic of Power BI, perform analysis with Alteryx, master Qlik Sense, R Programming using R and R Studio, and create stunning visualizations in Tableau Desktop.
The PowerBI Formulas course delves into the intricacies of Power Query and Power Pivot in Power BI, focusing on various techniques to enhance data analysis and visualization. Through a series of modules, participants will learn how to effectively use Power Query and Power Pivot to transform, manipulate, and model data for creating insightful reports and dashboards. Learning Outcomes: Master the fundamentals of Power Query and Power Pivot in Power BI. Understand data transformation techniques using Power Query. Explore advanced data modeling with Power Pivot. Learn to create calculated columns and measures. Gain proficiency in using DAX (Data Analysis Expressions) formulas. Apply Power Query and Power Pivot to real-world data scenarios. Utilize data relationships and hierarchies for effective data modeling. Create dynamic visualizations and reports using the transformed data. Why buy this PowerBI Formulas? 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 PowerBI Formulas 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? Business analysts and data analysts seeking to enhance their Power BI skills. Data professionals looking to leverage Power Query and Power Pivot for data analysis. Professionals working with data visualization and reporting. Individuals interested in learning advanced data transformation and modeling techniques. Prerequisites This PowerBI Formulas does not require you to have any prior qualifications or experience. You can just enrol and start learning.This PowerBI Formulas was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path Data Analyst: Analyze and visualize data to extract meaningful insights. Business Intelligence Analyst: Transform raw data into actionable business insights. Data Scientist: Apply data manipulation and modeling techniques for predictions. Reporting Specialist: Create engaging and informative reports and dashboards. Business Analyst: Use data-driven insights to inform decision-making processes. Course Curriculum PowerBI Formulas power pivot power query - 1 00:01:00 power pivot power query - 2 00:02:00 power pivot power query - 3 00:06:00 power pivot power query - 4 00:07:00 power pivot power query - 5 00:02:00 power pivot power query - 6 00:05:00 power pivot power query - 7 00:05:00 power pivot power query - 8 00:04:00 power pivot power query - 9 00:03:00 power pivot power query - 10 00:02:00 power pivot power query - 11 00:04:00 power pivot power query - 12 00:08:00 power pivot power query - 13 00:04:00 power pivot power query - 14 00:03:00 power pivot power query - 15 00:13:00 power pivot power query - 16 00:03:00 power pivot power query - 17 00:13:00 Assignment Assignment - PowerBI Formulas 00:00:00
Kick Start Your Career with CompTIA's Data Analysis Certification - Live Classes
(1)By Media Tek Training Solutions Ltd
Get job ready with CompTIA's Data Analysis Certification. Live Classes - Career Guidance - Exam Included.
Microsoft Power BI: Data Analysis Practitioner (Second Edition) (v1.3)
By Nexus Human
Duration 2 Days 12 CPD hours This course is intended for This course is designed for professionals in a variety of job roles who are currently using desktop or web-based data management tools such as Microsoft Excel or SQL Server reporting services to perform numerical or general data analysis. They are responsible for connecting to cloud-based data sources, as well as shaping and combining data for the purpose of analysis. They are also looking for alternative ways to analyze business data, visualize insights, and share those insights with peers across the enterprise. This includes capturing and reporting on data to peers, executives, and clients. Overview In this course, you will analyze data with Microsoft Power BI. You will: Analyze data with self-service BI. Connect to data sources. Perform data cleaning, profiling, and shaping. Visualize data with Power BI. Enhance data analysis by adding and customizing visual elements. Model data with calculations. Create interactive visualizations. As technology progresses and becomes more interwoven with our businesses and lives, more data is collected about business and personal activities. This era of 'big data' is a direct result of the popularity and growth of cloud computing, which provides an abundance of computational power and storage, allowing organizations of all sorts to capture and store data. Leveraging that data effectively can provide timely insights and competitive advantages. Creating data-backed visualizations is key for data scientists, or any professional, to explore, analyze, and report insights and trends from data. Microsoft© Power BI© software is designed for this purpose. Power BI was built to connect to a wide range of data sources, and it enables users to quickly create visualizations of connected data to gain insights, show trends, and create reports. Power BI's data connection capabilities and visualization features go far beyond those that can be found in spreadsheets, enabling users to create compelling and interactive worksheets, dashboards, and stories that bring data to life and turn data into thoughtful action. Analyzing Data with Self-Service BI Topic A: Data Analysis and Visualization for Business Intelligence Topic B: Self-Service BI with Microsoft Power BI Connecting to Data Sources Topic A: Create Data Connections Topic B: Configure and Manage Data Relationships Topic C: Save Files in Power BI Performing Data Cleaning, Profiling, and Shaping Topic A: Clean, Transform, and Load Data with the Query Editor Topic B: Profile Data with the Query Editor Topic C: Shape Data with the Query Editor Topic D: Combine and Manage Data Rows Visualizing Data with Power BI Topic A: Create Visualizations in Power BI Topic B: Chart Data in Power BI Enhancing Data Analysis Topic A: Customize Visuals and Pages Topic B: Incorporate Tooltips Modeling Data with Calculations Topic A: Create Calculations with Data Analysis Expressions (DAX) Topic B: Create Calculated Measures and Conditional Columns Creating Interactive Visualizations Topic A: Create and Manage Data Hierarchies Topic B: Filter and Slice Reports Topic C: Create Dashboards Additional course details: Nexus Humans Microsoft Power BI: Data Analysis Practitioner (Second Edition) (v1.3) 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 Microsoft Power BI: Data Analysis Practitioner (Second Edition) (v1.3) 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.
Microsoft Power BI: Data Analysis Professional (Second Edition) (v1.3)
By Nexus Human
Duration 3 Days 18 CPD hours This course is intended for This course is designed for professionals in a variety of job roles who are currently using desktop or web-based data management tools such as Microsoft Excel or SQL Server reporting services to perform numerical or general data analysis. They are responsible for connecting to cloud-based data sources, as well as shaping and combining data for the purpose of analysis. They are also looking for alternative ways to analyze business data, visualize insights, and share those insights with peers across the enterprise. This includes capturing and reporting on data to peers, executives, and clients. This course is also designed for professionals who want to pursue the Microsoft Power BI Data Analyst (Exam PL-300) certification. Overview In this course, you will analyze data with Microsoft Power BI. You will: Analyze data with self-service BI. Connect to data sources. Perform data cleaning, profiling, and shaping. Visualize data with Power BI. Enhance data analysis by adding and customizing visual elements. Model data with calculations. Create interactive visualizations. Use advanced analysis techniques. Enhance reports and dashboards. Publish and share reports and dashboards. Extend Power BI beyond the desktop. As technology progresses and becomes more interwoven with our businesses and lives, more data is collected about business and personal activities. This era of 'big data' is a direct result of the popularity and growth of cloud computing, which provides an abundance of computational power and storage, allowing organizations of all sorts to capture and store data. Leveraging that data effectively can provide timely insights and competitive advantages. Creating data-backed visualizations is key for data scientists, or any professional, to explore, analyze, and report insights and trends from data. Microsoft© Power BI© software is designed for this purpose. Power BI was built to connect to a wide range of data sources, and it enables users to quickly create visualizations of connected data to gain insights, show trends, and create reports. Power BI's data connection capabilities and visualization features go far beyond those that can be found in spreadsheets, enabling users to create compelling and interactive worksheets, dashboards, and stories that bring data to life and turn data into thoughtful action. Analyzing Data with Self-Service BI Topic A: Data Analysis and Visualization for Business Intelligence Topic B: Self-Service BI with Microsoft Power BI Connecting to Data Sources Topic A: Create Data Connections Topic B: Configure and Manage Data Relationships Topic C: Save Files in Power BI Performing Data Cleaning, Profiling, and Shaping Topic A: Clean, Transform, and Load Data with the Query Editor Topic B: Profile Data with the Query Editor Topic C: Shape Data with the Query Editor Topic D: Combine and Manage Data Rows Visualizing Data with Power BI Topic A: Create Visualizations in Power BI Topic B: Chart Data in Power BI Enhancing Data Analysis Topic A: Customize Visuals and Pages Topic B: Incorporate Tooltips Modeling Data with Calculations Topic A: Create Calculations with Data Analysis Expressions (DAX) Topic B: Create Calculated Measures and Conditional Columns Creating Interactive Visualizations Topic A: Create and Manage Data Hierarchies Topic B: Filter and Slice Reports Topic C: Create Dashboards Using Advanced Analysis Techniques Topic A: Create Calculated Tables, Variables, and Parameters Topic B: Enhance Visuals with Statistical Analysis Topic C: Perform Advanced Analysis Enhancing Reports and Dashboards Topic A: Enhance Reports Topic B: Enhance Dashboards Publishing and Sharing Reports and Dashboards Topic A: Publish Reports Topic B: Create and Manage Workspaces Topic C: Share Reports and Dashboards Extending Power BI Beyond the Desktop Topic A: Use Power BI Mobile Topic B: Extend Access with the Power BI API Additional course details: Nexus Humans Microsoft Power BI: Data Analysis Professional (Second Edition) (v1.3) 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 Microsoft Power BI: Data Analysis Professional (Second Edition) (v1.3) 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.
Power BI Masterclass 8 -Python, Finance, and Advanced DAX
By Packt
This comprehensive training program covers many concepts in Microsoft Power BI. From beginner to advanced levels, learn data visualization, advanced DAX expression, Python integration, custom visuals, data preparation, and collaboration in Power BI service. Develop expertise in Power BI and position yourself for a successful career in data analytics.
Course Overview: Data analysis is a hot skill in today's job market. According to a recent study by LinkedIn, demand for data analysts is growing 15 times faster than the average for all occupations. And the salaries are good too. The average salary for a data analyst in the UK is £40,000. If you're looking to get ahead in your career or learn a new skill that's in high demand, then the Data Analysis in Excel Level 3 Course is for you. In this course, you'll learn how to use Excel to analyse data like a pro. You'll learn how to clean, format, and analyse data using various Excel tools and techniques. You'll also learn how to create charts and graphs to visualise your data. This course won't merely make you adept at Excel. It will mould you into a data wizard, wielding Excel as a potent tool to make data sing and secrets reveal themselves. Enrol Today and Start Learning! Key Features of the Course: Embark on your data analysis journey with us and discover these exciting features: A CPD Certificate to validate your newfound skills. 24/7 Learning Assistance for any hour inspiration strikes. Engaging learning materials to ensure an enriching learning experience. Who is This Course For? Our Data Analysis in Excel Level 3 Course caters to ambitious individuals with a basic understanding of Excel, eager to take their skills to new heights. This course welcomes everything from budding data enthusiasts to established business analysts seeking to fortify their analytical toolkits. What You Will Learn: Dive deep into the labyrinth of data as you master essential modules such as 'Search for and Replace Data,' 'Sort and Filter Data,' and 'Query Data with Database Functions.' Venture further into data exploration with our 'Outline and Subtotal Data' module, where you'll learn to summarise your data for a more organised view efficiently. The journey continues with modules like 'Create Charts' and 'Modify and Format Charts,' enabling you to translate complex data into easy-to-understand visuals. Furthermore, our dedicated modules on 'Creating a PivotTable' and 'Analysing PivotTable Data' promise a comprehensive understanding of one of Excel's most powerful tools. Why Enrol in This Course: Rated as a top-reviewed course and constantly updated to keep up with the latest trends, our Data Analysis in Excel Level 3 Course equips you with sought-after skills like data forecasting, creating sparklines, using advanced chart features, and more. Requirements: While no specific prerequisites are required, a basic understanding of Excel and an eagerness to explore the fascinating world of data analysis would be beneficial. Career Path: On completion of this Data Analysis in Excel Level 3 Course course, you'll be ready to step into diverse roles such as: Data Analyst (£30,000-£35,000) Business Intelligence Analyst (£32,000-£37,000) Market Research Analyst (£28,000-£33,000) Operations Analyst (£31,000-£36,000) Financial Analyst (£35,000-£40,000) Supply Chain Analyst (£30,000-£35,000) Sales Analyst (£29,000-£34,000) Certification: Upon successful completion of the course, you'll be awarded a prestigious CPD Certificate, demonstrating your expertise in data analysis using Excel. So, are you ready to discover the hidden stories in data and revolutionise decision-making? Enrol in our Data Analysis in Excel Level 3 Course and start your journey today! Course Curriculum 11 sections • 32 lectures • 04:43:00 total length •Insert, Delete, and Adjust Cells, Columns, and Rows: 00:10:00 •Search for and Replace Data: 00:09:00 •Use Proofing and Research Tools: 00:07:00 •Sort Data: 00:10:00 •Filter Data: 00:10:00 •Query Data with Database Functions: 00:09:00 •Outline and Subtotal Data: 00:09:00 •Apply Intermediate Conditional Formatting: 00:07:00 •Apply Advanced Conditional Formatting: 00:05:00 •Create Charts: 00:13:00 •Modify and Format Charts: 00:12:00 •Use Advanced Chart Features: 00:12:00 •Create a PivotTable: 00:13:00 •Analyze PivotTable Data: 00:12:00 •Present Data with PivotCharts: 00:07:00 •Filter Data by Using Timelines and Slicers: 00:11:00 •Use Links and External References: 00:12:00 •Use 3-D References: 00:06:00 •Consolidate Data: 00:05:00 •Use Lookup Functions: 00:12:00 •Trace Cells: 00:09:00 •Watch and Evaluate Formulas: 00:08:00 •Apply Data Validation: 00:13:00 •Search for Invalid Data and Formulas with Errors: 00:04:00 •Work with Macros: 00:18:00 •Create Sparklines: 00:07:00 •MapData: 00:07:00 •Determine Potential Outcomes Using Data Tables: 00:08:00 •Determine Potential Outcomes Using Scenarios: 00:09:00 •Use the Goal Seek Feature: 00:04:00 •Forecasting Data Trends: 00:05:00 •Assignment - Data Analysis in Excel Level 3 Course: 00:00:00
