59 Data Analyst courses in Birmingham delivered Live Online

The insights gained from data analysis are only truly valuable when you can be clearly expressed to other people – bosses, colleagues, clients, customers, or other stakeholders. In this workshop you’ll learn how to turn raw qualitative or quantitative data into a clear visual story through infographics and data visualization. We'll discuss the key principles for planning an effective visual, look at examples of best (and worst) practice, and learn repeatable and practical design techniques for enhancing the story. We'll also give you an overview of useful tools that will help you turn your idea into a finished infographic or data visualization. You could be conjuring up eye-catching slide decks, building effective reports and dashboards, pitching to investors, or presenting persuasive data to your most important customers.  This is a fully interactive online workshop, so be prepared to join discussions and get hands on with building your own visualisations.  Takeaways Be able to evaluate the elements that make an infographic or visualization effective. Learn quick and repeatable visual tricks for ensuring infographics convey a clear message. Understand how to tailor your approach to different audiences and context. Discover a bunch of free tools and resources to help you build your own visualizations. Understand how online, interactive visualizations work and how to create them. Delivery  We deliver our courses over Zoom, to maximise flexibility. The training can be delivered in a single day, or across multiple sessions. All of our courses are live and interactive – every session includes a mix of formal tuition and hands-on exercises. To ensure this is possible, the number of attendees is capped at 16 people.  Tutor Alan Rutter is the founder of Fire Plus Algebra. He is a specialist in communicating complex subjects through data visualisation, writing and design. He teaches for General Assembly and runs in-house training for public sector clients including the Home Office, the Department of Transport, the Biotechnology and Biological Sciences Research Council, the Health Foundation, and numerous local government and emergency services teams. He previously worked with Guardian Masterclasses on curating and delivering new course strands, including developing and teaching their B2B data visualisation courses. He oversaw the iPad edition launches of Wired, GQ, Vanity Fair and Vogue in the UK, and has worked with Condé Nast International as product owner on a bespoke digital asset management system for their 11 global markets.
 Testimonials "Just to say what a great course this was. I have made my first report employing some of the ideas and tools you showed us – to rapturous responses! The next actions are clear for all and they all understood it! Thank you for helping me to organise my data and thoughts, showing how to present the key message up front, and how to keep it simple and focused. Gearing up for another report now!" Kay Anderson | Head of Finance | Mima "We have been using Tableau to display data for some time but knew we could do more to engage our end users. Alan’s training gave us a framework to start thinking about what we wanted to achieve with our visualisations and analysis, and some great tips on how to display information for maximum impact. Alan was an engaging trainer and we found the workshops very energising." Ellen Austin | Senior Data Analyst | London School of Economics

PYTHON BOOTCAMP: This 12-week Python Data Analytics Data Boot Camp is designed to give you a complete skill set required by data analysts . You will be fully fluent and confident as a Python data analyst, with full understanding of Python Programming. From Data, databases, datasets, importing, cleaning, transforming, analysing to visualisation and creating awesome dashboards The course is a practical, instructor-lead program.

This Level 4 course aims to equip professionals with the knowledge about the skills and practical behaviours which are required for them to step into a leadership/management role. The demand for management roles is expected to grow in the coming years. This is due to a number of factors, including: The ageing population, which is leading to a shortage of skilled workers. The increasing complexity of businesses requires more managers to oversee operations. The growing importance of technology is creating new opportunities for managers to lead and innovate.

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Duration 5 Days 30 CPD hours This course is intended for This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. Overview In this course, you will learn to: Use data science principles to address business issues. Apply the extract, transform, and load (ETL) process to prepare datasets. Use multiple techniques to analyze data and extract valuable insights. Design a machine learning approach to address business issues. Train, tune, and evaluate classification models. Train, tune, and evaluate regression and forecasting models. Train, tune, and evaluate clustering models. Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance. For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice Addressing Business Issues with Data Science Topic A: Initiate a Data Science Project Topic B: Formulate a Data Science Problem Extracting, Transforming, and Loading Data Topic A: Extract Data Topic B: Transform Data Topic C: Load Data Analyzing Data Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Use Visualizations to Analyze Data Topic D: Preprocess Data Designing a Machine Learning Approach Topic A: Identify Machine Learning Concepts Topic B: Test a Hypothesis Developing Classification Models Topic A: Train and Tune Classification Models Topic B: Evaluate Classification Models Developing Regression Models Topic A: Train and Tune Regression Models Topic B: Evaluate Regression Models Developing Clustering Models Topic A: Train and Tune Clustering Models Topic B: Evaluate Clustering Models Finalizing a Data Science Project Topic A: Communicate Results to Stakeholders Topic B: Demonstrate Models in a Web App Topic C: Implement and Test Production Pipelines

Duration 3 Days 18 CPD hours This course is intended for Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform Overview This course teaches students the following skills: Derive insights from data using the analysis and visualization tools on Google Cloud Platform Interactively query datasets using Google BigQuery Load, clean, and transform data at scale Visualize data using Google Data Studio and other third-party platforms Distinguish between exploratory and explanatory analytics and when to use each approach Explore new datasets and uncover hidden insights quickly and effectively Optimizing data models and queries for price and performance Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL. Introduction to Data on the Google Cloud Platform Highlight Analytics Challenges Faced by Data Analysts Compare Big Data On-Premises vs on the Cloud Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud Navigate Google Cloud Platform Project Basics Lab: Getting started with Google Cloud Platform Big Data Tools Overview Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools Demo: Analyze 10 Billion Records with Google BigQuery Explore 9 Fundamental Google BigQuery Features Compare GCP Tools for Analysts, Data Scientists, and Data Engineers Lab: Exploring Datasets with Google BigQuery Exploring your Data with SQL Compare Common Data Exploration Techniques Learn How to Code High Quality Standard SQL Explore Google BigQuery Public Datasets Visualization Preview: Google Data Studio Lab: Troubleshoot Common SQL Errors Google BigQuery Pricing Walkthrough of a BigQuery Job Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs Optimize Queries for Cost Lab: Calculate Google BigQuery Pricing Cleaning and Transforming your Data Examine the 5 Principles of Dataset Integrity Characterize Dataset Shape and Skew Clean and Transform Data using SQL Clean and Transform Data using a new UI: Introducing Cloud Dataprep Lab: Explore and Shape Data with Cloud Dataprep Storing and Exporting Data Compare Permanent vs Temporary Tables Save and Export Query Results Performance Preview: Query Cache Lab: Creating new Permanent Tables Ingesting New Datasets into Google BigQuery Query from External Data Sources Avoid Data Ingesting Pitfalls Ingest New Data into Permanent Tables Discuss Streaming Inserts Lab: Ingesting and Querying New Datasets Data Visualization Overview of Data Visualization Principles Exploratory vs Explanatory Analysis Approaches Demo: Google Data Studio UI Connect Google Data Studio to Google BigQuery Lab: Exploring a Dataset in Google Data Studio Joining and Merging Datasets Merge Historical Data Tables with UNION Introduce Table Wildcards for Easy Merges Review Data Schemas: Linking Data Across Multiple Tables Walkthrough JOIN Examples and Pitfalls Lab: Join and Union Data from Multiple Tables Advanced Functions and Clauses Review SQL Case Statements Introduce Analytical Window Functions Safeguard Data with One-Way Field Encryption Discuss Effective Sub-query and CTE design Compare SQL and Javascript UDFs Lab: Deriving Insights with Advanced SQL Functions Schema Design and Nested Data Structures Compare Google BigQuery vs Traditional RDBMS Data Architecture Normalization vs Denormalization: Performance Tradeoffs Schema Review: The Good, The Bad, and The Ugly Arrays and Nested Data in Google BigQuery Lab: Querying Nested and Repeated Data More Visualization with Google Data Studio Create Case Statements and Calculated Fields Avoid Performance Pitfalls with Cache considerations Share Dashboards and Discuss Data Access considerations Optimizing for Performance Avoid Google BigQuery Performance Pitfalls Prevent Hotspots in your Data Diagnose Performance Issues with the Query Explanation map Lab: Optimizing and Troubleshooting Query Performance Advanced Insights Introducing Cloud Datalab Cloud Datalab Notebooks and Cells Benefits of Cloud Datalab Data Access Compare IAM and BigQuery Dataset Roles Avoid Access Pitfalls Review Members, Roles, Organizations, Account Administration, and Service Accounts

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.

The course is relevant to anyone requiring an understanding of the use of Agile or looking to adopt it. This includes, but is not limited to, organisational leaders and managers, marketing executives and managers, and/or all professionals working in an Agile environment, including software sesters, developers, business analysts, UX designers, project management office (PMO), project support and project coordinators.

This practitioner-level 4 award encourages individuals in IT and technical roles to explore the many teams, ideas, and functions within an organisation and maximise their contribution. You will achieve this by learning the key concepts and considering behaviour and response in different scenarios.

This award introduces the critical concepts associated with AI and explores its relationship with the systems and processes that make up the digital ecosystem. It explores how AI can empower organisations to utilise Big Data through the use of Business Analysis and Machine Learning, and encourages candidates to consider a future vision of the world that is powered by AI.