59 Data Analyst courses delivered Live Online

Duration 2 Days 12 CPD hours This course is intended for The primary audience for this course are persons who are new to reporting with Microsoft© SQL Server© Report Builder and SSRS, persons who are transitioning from another reporting software application, and persons who are existing Report Builder and SSRS report authors. The secondary audience for this course are persons who are using Report Designer (SSRS). Course participants may be business analysts, programmer analysts, data analysts, database administrators, or IT professionals and may or may not have experience with Microsoft© SQL Server© Report Builder and SSRS, programming (Visual Basic), and/or Transact-Structured Query Language (T-SQL) experience. Overview After completing this course, students will be able to: Create parameter reports. Create list reports. Format reports with complex expressions. Add images and subreports. Add drilldown and drillthrough functionality. Add sparklines, data bars, and indicators. In this course, students will continue their learning on the foundations of report writing with Microsoft© SQL Server© Report Builder and SSRS. The focus will be on report creation by connecting to a database and manipulating the data. 1 - Parameter Reports Parameterized Reporting Create Report Parameters Modify Report Parameters Provide Default Parameter Values Provide Available Values for Parameters Lab: Creating Parameterized Reports 2 - List Data Regions Introducing the List Data Region Create Reports Using List Data Regions Modify Reports Which Use List Data Regions Lab: Creating List Reports 3 - Enhancing Reports with Complex Expressions Formatting Reports Top N Reports Running Values Lab: Enhancing Reports with Complex Expressions 4 - Enhance Reports with Images and Subreports Add Images to Reports Retrieve Images from a Database Introducing SSRS (SQL Server Reporting Services) Report Server Add Subreports to Reports Lab: Working With Images and Subreports 5 - Drilldown and Drillthrough Reports Add and Configure Drilldown Reporting Add and Configure Drillthrough Reporting Lab: Add Drilldown and Drillthrough Functionality to Reports 6 - Sparklines, Data Bars, and Indicators Sparklines Data Bars Indicators Lab: Adding Sparklines, Data Bars, and Indicators to Reports Additional course details: Nexus Humans 55128 Writing Reports with Report Builder and SSRS Level 2 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 55128 Writing Reports with Report Builder and SSRS Level 2 course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is designed for professionals in a variety of job roles who receive Tableau data visualizations from data analysts or from data visualization engineers. These data report recipients want to take advantage of the many Tableau features and capabilities that enable them to explore the data behind the initial analysis, perform additional analysis to ask next-level questions of the data, and to customize visualizations and dashboards to share new insights and create compelling reports. Overview Explore Tableau reports. Analyze data to get answers and insights. Sort and group data for analysis and reporting. Filter views. Prepare reports. Troubleshoot, collaborate, and share views and analysis As data acquisition, access, analysis, and reporting are interwoven with our businesses and lives, more and more data is collected about business and personal activities. This abundance of data and the computing power to analyze it has increased the use of data analysis and data visualization across a broad range of job roles. Decision makers of all types, including managers and executives, must interact with, interpret, and develop reports based on data and analysis provided to them. Tableau© software is designed for data analysis and the creation of visualizations. Data analysts prepare data, perform initial analysis, and create visualizations that are then passed on to business data-driven decision makers. These decision makers can use Tableau's tools to explore the data, perform further analysis to find new insights, make decisions, and create customized reports to share their findings. Prerequisites To ensure your success in this course, you should have experience managing data with Microsoft© Excel© or Google Sheets? Lesson 1: Exploring Tableau Reports Topic A: Data Analysis Workflow with Tableau Topic B: Explore Views Topic C: Edit Workbooks Lesson 2: Analyzing Data to Get Answers and Insights Topic A: Configure Marks with the Marks Card Topic B: Ask New Questions by Changing Aggregation Topic C: Find Answers with Calculations Topic D: Answer Questions with Table Calculations Lesson 3: Sorting and Grouping Data for Analysis and Reporting Topic A: Sort Data Topic B: Group Data Lesson 4: Filtering Views Topic A: Filter Data to Refine Analysis Topic B: Create Interactive Filters for Reports Lesson 5: Preparing Reports Topic A: Format and Annotate Views to Tell Your Story Topic B: Emphasize Data in Reports Topic C: Animate Visualizations for Clarity Lesson 6: Troubleshooting, Sharing, and Collaborating Topic A: Troubleshoot Data Issues Topic B: Collaborate in Tableau Online Topic C: Collaborate with Non-Tableau Users

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Duration 1 Days 6 CPD hours This course is intended for This course is designed for professionals in a variety of job roles who receive Power BI data visualizations and reports from data analysts or from data visualization engineers. These data report recipients want to use the features and capabilities of Power BI to fully explore the visualizations and initial analyses provided to them in reports, perform additional analysis to ask next-level questions of the data, and to customize and create new visualizations and dashboards in order to share new insights and create compelling reports. Overview Explore Power BI reports. Analyze data to get answers and insights. Sort and group data for analysis and reporting. Filter visualizations. Prepare reports. Troubleshoot, collaborate, and share reports. As data acquisition, access, analysis, and reporting are interwoven with our businesses and lives, more and more data is collected about business and personal activities. This abundance of data and the computing power to analyze it has increased the use of data analysis and data visualization across a broad range of job roles. Decision makers of all types, including managers and executives, must interact with, interpret, and develop reports based on data and analysis provided to them. Microsoft Power BI software is designed for data analysis and the creation of visualizations. Data analysts prepare data, perform initial analysis, and create visualizations that are then passed to business data decision makers. These decision makers can use Power BI's tools to explore the data, perform further analysis to find new insights, make decisions, and create customized reports to share their findings. Prerequisites To ensure your success in this course, you have experience managing data with Microsoft Excel or Google Sheets 1. Exploring Power BI Reports Topic A: Data Analysis Workflow with Power BI Topic B: Explore Reports in the Power BI Service Topic C: Edit Reports 2. Analyzing Data to Get Answers and Insights Topic A: Configure Data Visualizations Topic B: Ask New Questions by Changing Aggregation Topic C: Find Answers with Calculations 3. Sorting and Grouping Data for Analysis and Reporting Topic A: Sort Data Topic B: Group Data 4. Filtering Visualizations Topic A: Filter Data to Refine Analysis Topic B: Create Slicers for Reports 5. Preparing Reports Topic A: Format and Annotate Reports Topic B: Emphasize Data in Reports 6. Troubleshooting, Sharing, and Collaborating Topic A: Troubleshoot Data Issues Topic B: Collaborate in Power BI Topic C: Collaborate with Non-Power BI Users

OVERVIEW DIAD is a one-day, hands-on workshop for business analysts, covering the breadth of Power BI capabilities. The course focuses on five practical Labs and at the end of the day, attendees will better understand how to: Connect and transform data from a variety of data sources. Define business rules and KPIs. Explore data with powerful interactive visuals. Build stunning reports. Share their dashboards with their team business partners and publish them to the web. The course content is managed by the Power BI engineering team at Microsoft. There is no exam associated with the course. COURSE BENEFITS: Learn how to clean, transform, and load data from various sources Create and manage a data model in Power BI consisting of multiple tables connected with relationships Build Measures and other calculations in the DAX language to plot in reports Manage and share report assets to the Power BI Service WHO IS THE COURSE FOR? Data Analysts and Management Consultants with little or no experience of Power BI who wish to upgrade their knowledge to include Business Intelligence Analysts looking for a quick introduction to Power BI who don’t have the time for the full three day PL-300 course Marketers in data-intensive organisations who need new tools to build visually appealing, dynamic charts for their stakeholders to use LAB OUTLINE Lab 1 Accessing & Preparing The Data Load data from Excel and CSV sources Manipulate the data to prepare it for reporting Prepare tables in Power Query and load them into the data model Lab 2 Data Modelling And Exploration Create a range of different charts Highlight and cross-filter Create new groups and hierarchies Add new measures to the model Lab 3 Data Visualization Add conditional formatting to a report Add logos to a filter Import a custom visual Apply a custom theme Add bookmarks to the report to tell a story Lab 4 Publishing A Report And Creating A Dashboard Create a Workspace in the Power BI Service Publish a report to the Service Create a Dashboard and pin visuals to it Generate and view insights Lab 5 Collaboration Share a Dashboard Access a Dashboard on a Mobile Device

Supercharge your skills and career and learn in-demand knowledge needed to build business intelligence dashboards. This beginner to intermediate level course will introduce you to all the Power BI technologies i.e. Power Query, DAX, Data Modelling (Power Pivot), M, types of visualizations, etc.

This course is designed for students who already have foundational knowledge and skills in Excel and who wish to perform robust and advanced data and statistical analysis with Microsoft Excel using PivotTables, use tools such as Power Pivot and the Data Analysis ToolPak to analyze data and visualize data and insights using advanced visualizations in charts and dashboards in Excel.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.