195 Data Skills courses in Belfast delivered Online

Overview With the ever-increasing demand for Data Analysis in personal & professional settings, this online training aims at educating, nurturing, and upskilling individuals to stay ahead of the curve - whatever their level of expertise in Data Analysis may be. Learning about Data Analysis or keeping up to date on it can be confusing at times, and maybe even daunting! But that's not the case with this course from Compete High. We understand the different requirements coming with a wide variety of demographics looking to get skilled in Data Analysis. That's why we've developed this online training in a way that caters to learners with different goals in mind. The course materials are prepared with consultation from the experts of this field and all the information on Data Analysis is kept up to date on a regular basis so that learners don't get left behind on the current trends/updates. The self-paced online learning methodology by compete high in this Data Analysis Basics course helps you learn whenever or however you wish, keeping in mind the busy schedule or possible inconveniences that come with physical classes. The easy-to-grasp, bite-sized lessons are proven to be most effective in memorising and learning the lessons by heart. On top of that, you have the opportunity to receive a certificate after successfully completing the course! Instead of searching for hours, enrol right away on this Data Analysis Basics course from Compete High and accelerate your career in the right path with expert-outlined lessons and a guarantee of success in the long run.   Who is this course for? While we refrain from discouraging anyone wanting to do this Data Analysis Basics course or impose any sort of restrictions on doing this online training, people meeting any of the following criteria will benefit the most from it: Anyone looking for the basics of Data Analysis, Jobseekers in the relevant domains, Anyone with a ground knowledge/intermediate expertise in Data Analysis, Anyone looking for a certificate of completion on doing an online training on this topic, Students of Data Analysis, or anyone with an academic knowledge gap to bridge, Anyone with a general interest/curiosity   Career Path This Data Analysis Basics course smoothens the way up your career ladder with all the relevant information, skills, and online certificate of achievements. After successfully completing the course, you can expect to move one significant step closer to achieving your professional goals - whether it's securing that job you desire, getting the promotion you deserve, or setting up that business of your dreams. Course Curriculum Module - 01 - Introduction to Data Analysis its Applications Introduction to Data Analysis its Applications 00:00 Module - 02 - Probability Probability Distributions Probability Probability Distributions 00:00 Module - 03 - Decision making and Factors to Account for Decision making and Factors to Account for 00:00 Module - 04 - Data Mining Data Mining 00:00 Module - 05 - Optimization Situation modelling Optimization Situation modelling 00:00

Overview With the ever-increasing demand for Big Data Analytics in personal & professional settings, this online training aims at educating, nurturing, and upskilling individuals to stay ahead of the curve - whatever their level of expertise in Big Data Analytics may be. Learning about Big Data Analytics or keeping up to date on it can be confusing at times, and maybe even daunting! But that's not the case with this course from Compete High. We understand the different requirements coming with a wide variety of demographics looking to get skilled in Big Data Analytics . That's why we've developed this online training in a way that caters to learners with different goals in mind. The course materials are prepared with consultation from the experts of this field and all the information on Big Data Analytics is kept up to date on a regular basis so that learners don't get left behind on the current trends/updates. The self-paced online learning methodology by compete high in this Big Data Analytics course helps you learn whenever or however you wish, keeping in mind the busy schedule or possible inconveniences that come with physical classes. The easy-to-grasp, bite-sized lessons are proven to be most effective in memorising and learning the lessons by heart. On top of that, you have the opportunity to receive a certificate after successfully completing the course! Instead of searching for hours, enrol right away on this Big Data Analytics course from Compete High and accelerate your career in the right path with expert-outlined lessons and a guarantee of success in the long run. Who is this course for? While we refrain from discouraging anyone wanting to do this Big Data Analytics course or impose any sort of restrictions on doing this online training, people meeting any of the following criteria will benefit the most from it: Anyone looking for the basics of Big Data Analytics , Jobseekers in the relevant domains, Anyone with a ground knowledge/intermediate expertise in Big Data Analytics , Anyone looking for a certificate of completion on doing an online training on this topic, Students of Big Data Analytics , or anyone with an academic knowledge gap to bridge, Anyone with a general interest/curiosity Career Path This Big Data Analytics course smoothens the way up your career ladder with all the relevant information, skills, and online certificate of achievements. After successfully completing the course, you can expect to move one significant step closer to achieving your professional goals - whether it's securing that job you desire, getting the promotion you deserve, or setting up that business of your dreams.     Course Curriculum Module 1_ Introduction to Big Data. Introduction to Big Data. 00:00 Module 2_ Hadoop and MapReduce. Hadoop and MapReduce. 00:00 Module 3_ NoSQL Databases. NoSQL Databases. 00:00 Module 4_ Data Storage and Retrieval. Data Storage and Retrieval. 00:00 Module 5_ Data Processing with Spark. Data Processing with Spark. 00:00 Module 6_ Data Analysis with Hadoop and Pig. Data Analysis with Hadoop and Pig. 00:00

Overview   With the ever-increasing demand for Tableau in personal & professional settings, this online training aims at educating, nurturing, and upskilling individuals to stay ahead of the curve - whatever their level of expertise in Tableau may be.   Learning about Tableau or keeping up to date on it can be confusing at times, and maybe even daunting! But that's not the case with this course from Compete High. We understand the different requirements coming with a wide variety of demographics looking to get skilled in Tableau. That's why we've developed this online training in a way that caters to learners with different goals in mind. The course materials are prepared with consultation from the experts of this field and all the information on Tableau is kept up to date on a regular basis so that learners don't get left behind on the current trends/updates.   The self-paced online learning methodology by Compete High in this Diploma in Data Analytics In Tableau course helps you learn whenever or however you wish, keeping in mind the busy schedule or possible inconveniences that come with physical classes. The easy-to-grasp, bite-sized lessons are proven to be most effective in memorising and learning the lessons by heart. On top of that, you have the opportunity to receive a certificate after successfully completing the course!   Instead of searching for hours, enrol right away on this Diploma in Data Analytics In Tableau course from Compete High and accelerate your career in the right path with expert-outlined lessons and a guarantee of success in the long run.   Who is this course for?   While we refrain from discouraging anyone wanting to do this Diploma in Data Analytics In Tableau course or impose any sort of restrictions on doing this online training, people meeting any of the following criteria will benefit the most from it: Anyone looking for the basics of Tableau, Jobseekers in the relevant domains, Anyone with a ground knowledge/intermediate expertise in Tableau, Anyone looking for a certificate of completion on doing an online training on this topic, Students of Tableau, or anyone with an academic knowledge gap to bridge, Anyone with a general interest/curiosity   Career Path   This Diploma in Data Analytics In Tableau course smoothens the way up your career ladder with all the relevant information, skills, and online certificate of achievements. After successfully completing the course, you can expect to move one significant step closer to achieving your professional goals - whether it's securing that job you desire, getting the promotion you deserve, or setting up that business of your dreams.    Course Curriculum Module 01_ Data Analytics Data Analytics 00:00 Module 02_ Why Use Tableau for Data Analytics Why Use Tableau for Data Analytics 00:00 Module 03_ Getting Started With Tableau Getting Started With Tableau 00:00 Module 04_ Tableau Data Source (TDS) Tableau Data Source (TDS) 00:00 Module 05_ Tableau Worksheets Tableau Worksheets 00:00 Module 06_ Tableau Calculations Tableau Calculations 00:00 Module 07_ Tableau Sort _ Filters Tableau Sort _ Filters 00:00 Module 08_ Tableau Charts Tableau Charts 00:00 Module 09_ Tableau Advanced Tableau Advanced 00:00

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: A technical audience at an intermediate level Overview Using Amazon SageMaker, this course teaches you how to: Prepare a dataset for training. Train and evaluate a machine learning model. Automatically tune a machine learning model. Prepare a machine learning model for production. Think critically about machine learning model results In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment. Day 1 Business problem: Churn prediction Load and display the dataset Assess features and determine which Amazon SageMaker algorithm to use Use Amazon Sagemaker to train, evaluate, and automatically tune the model Deploy the model Assess relative cost of errors Additional course details: Nexus Humans Practical Data Science with Amazon SageMaker 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 Practical Data Science with Amazon SageMaker course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This is an introductory- level course appropriate for those who are developing applications using relational databases, or who are using SQL to extract and analyze data from databases and need to use the full power of SQL queries. Overview This course combines expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our expert practitioner, attendees will learn to: Maximize the potential of SQL to build powerful, complex and robust SQL queries Query multiple tables with inner joins, outer joins and self joins Construct recursive common table expressions Summarize data using aggregation and grouping Execute analytic functions to calculate ranks Build simple and correlated subqueries Thoroughly test SQL queries to avoid common errors Select the most efficient solution to complex SQL problems A company?s success hinges on responsible, accurate database management. Organizations rely on highly available data to complete all sorts of tasks, from creating marketing reports and invoicing customers to setting financial goals. Data professionals like analysts, developers and architects are tasked with creating, optimizing, managing and analyzing data from databases ? with little room for error. When databases aren?t built or maintained correctly, it?s easy to mishandle or lose valuable data. Our SQL Programming and Database Training Series provides students with the skills they require to develop, analyze and maintain data and in correctly structured, modern and secure databases. SQL is the cornerstone of all relational database operations. In this hands-on course, you learn to exploit the full potential of the SELECT statement to write robust queries using the best query method for your application, test your queries, and avoid common errors and pitfalls. It also teaches alternative solutions to given problems, enabling you to choose the most efficient solution in each situation. Introduction: Quick Tools Review Introduction to SQL and its development environments Using SQL*PLUS Using SQL Developer Using the SQL SELECT Statement Capabilities of the SELECT statement Arithmetic expressions and NULL values in the SELECT statement Column aliases Use of concatenation operator, literal character strings, alternative quote operator, and the DISTINCT keyword Use of the DESCRIBE command Restricting and Sorting Data Limiting the Rows Rules of precedence for operators in an expression Substitution Variables Using the DEFINE and VERIFY command Single-Row Functions Describe the differences between single row and multiple row functions Manipulate strings with character function in the SELECT and WHERE clauses Manipulate numbers with the ROUND, TRUNC and MOD functions Perform arithmetic with date data Manipulate dates with the date functions Conversion Functions and Expressions Describe implicit and explicit data type conversion Use the TO_CHAR, TO_NUMBER, and TO_DATE conversion functions Nest multiple functions Apply the NVL, NULLIF, and COALESCE functions to data Decode/Case Statements Using the Group Functions and Aggregated Data Group Functions Creating Groups of Data Having Clause Cube/Rollup Clause SQL Joins and Join Types Introduction to JOINS Types of Joins Natural join Self-join Non equijoins OUTER join Using Subqueries Introduction to Subqueries Single Row Subqueries Multiple Row Subqueries Using the SET Operators Set Operators UNION and UNION ALL operator INTERSECT operator MINUS operator Matching the SELECT statements Using Data Manipulation Language (DML) statements Data Manipulation Language Database Transactions Insert Update Delete Merge Using Data Definition Language (DDL) Data Definition Language Create Alter Drop Data Dictionary Views Introduction to Data Dictionary Describe the Data Dictionary Structure Using the Data Dictionary views Querying the Data Dictionary Views Dynamic Performance Views Creating Sequences, Synonyms, Indexes Creating sequences Creating synonyms Creating indexes Index Types Creating Views Creating Views Altering Views Replacing Views Managing Schema Objects Managing constraints Creating and using temporary tables Creating and using external tables Retrieving Data Using Subqueries Retrieving Data by Using a Subquery as Source Working with Multiple-Column subqueries Correlated Subqueries Non-Correlated Subqueries Using Subqueries to Manipulate Data Using the Check Option Subqueries in Updates and Deletes In-line Views Data Control Language (DCL) System privileges Creating a role Object privileges Revoking object privileges Manipulating Data Overview of the Explicit Default Feature Using multitable INSERTs Using the MERGE statement Tracking Changes in Data

Duration 2 Days 12 CPD hours This course is intended for This course is relevant to anyone who needs to work with and understand data including: Business Analysts, Data Analysts, Reporting and BI professionals Marketing and Digital Marketing professionals Digital, Web, e-Commerce, Social media and Mobile channel professionals Business managers who need to interpret analytical output to inform managerial decisions Overview This course will cover the basic theory of data visualization along with practical skills for creating compelling visualizations, reports and dashboards from data using Tableau. Outcome: After attending this course delegates will understand - How to move from business questions to great data visualizations and beyond How to apply the fundamentals of data visualization to create informative charts How to choose the right visualization type for the job at hand How to design and develop basic dashboards in Tableau that people will love to use by doing the following: Reading data sources into Tableau Setting up the roles and data types for your analysis Creating new data fields using a range of calculation types Creating the following types of charts - cross tabs, pie and bar charts, geographic maps, dual axis and combo charts, heat maps, highlight tables, tree maps and scatter plots Creating Dashboards that delight using the all of the features available in Tableau. The use of analytics, statistics and data science in business has grown massively in recent years. Harnessing the power of data is opening actionable insights in diverse industries from banking to tourism. From Business Questions to Data Visualisation and Beyond The first step in any data analysis project is to move from a business question to data analysis and then on to a complete solution. This section will examine this conversion emphasizing: The use of data visualization to address a business need The data analytics process ? from business questions to developed dashboards Introduction to Tableau ? Part 1 In this section, the main functionality of Tableau will be explained including: Selecting and loading your data Defining data item properties Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations Creating basic visualizations Creating a basic dashboard Introduction to Tableau ? Part 2 In this section, the main functionality of Tableau will be explained including: Selecting and loading your data Defining data item properties Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations Creating basic visualizations Creating a basic dashboard Key Components of Good Data Visualisation and The Visualisation Zoo In this section the following topics will be covered: Colour theory Graphical perception & communication Choosing the right chart for the right job Data Exploration with Tableau Exploring data to answer business questions is one of the key uses of applying good data visualization techniques within Tableau. In this section we will apply the data visualization theory from the previous section within Tableau to uncover trends within the data to answer specific business questions. The types of charts that will be covered are: Cross Tabs Pie and bar charts Geographic maps Dual axis and combo charts with different mark types Heat maps Highlight tables Tree maps Scatter plots Introduction to Building Dashboards with Tableau In this section, we will implement the full process from business question to final basic dashboard in Tableau: Introduction to good dashboard design Building dashboards in Tableau

Duration 2 Days 12 CPD hours This course is intended for Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek techniques for working with complex types of data resources. Overview Use Connector stages to read from and write to database tables Handle SQL errors in Connector stages Use Connector stages with multiple input links Use the File Connector stage to access Hadoop HDFS data Optimize jobs that write to database tables Use the Unstructured Data stage to extract data from Excel spreadsheets Use the Data Masking stage to mask sensitive data processed within a DataStage job Use the Hierarchical stage to parse, compose, and transform XML data Use the Schema Library Manager to import and manage XML schemas Use the Data Rules stage to validate fields of data within a DataStage job Create custom data rules for validating data Design a job that processes a star schema data warehouse with Type 1 and Type 2 slowly changing dimensions This course is designed to introduce you to advanced parallel job data processing techniques in DataStage v11.5. In this course you will develop data techniques for processing different types of complex data resources including relational data, unstructured data (Excel spreadsheets), and XML data. In addition, you will learn advanced techniques for processing data, including techniques for masking data and techniques for validating data using data rules. Finally, you will learn techniques for updating data in a star schema data warehouse using the DataStage SCD (Slowly Changing Dimensions) stage. Even if you are not working with all of these specific types of data, you will benefit from this course by learning advanced DataStage job design techniques, techniques that go beyond those utilized in the DataStage Essentials course. Accessing databases Connector stage overview - Use Connector stages to read from and write to relational tables - Working with the Connector stage properties Connector stage functionality - Before / After SQL - Sparse lookups - Optimize insert/update performance Error handling in Connector stages - Reject links - Reject conditions Multiple input links - Designing jobs using Connector stages with multiple input links - Ordering records across multiple input links File Connector stage - Read and write data to Hadoop file systems Demonstration 1: Handling database errors Demonstration 2: Parallel jobs with multiple Connector input links Demonstration 3: Using the File Connector stage to read and write HDFS files Processing unstructured data Using the Unstructured Data stage in DataStage jobs - Extract data from an Excel spreadsheet - Specify a data range for data extraction in an Unstructured Data stage - Specify document properties for data extraction. Demonstration 1: Processing unstructured data Data masking Using the Data Masking stage in DataStage jobs - Data masking techniques - Data masking policies - Applying policies for masquerading context-aware data types - Applying policies for masquerading generic data types - Repeatable replacement - Using reference tables - Creating custom reference tables Demonstration 1: Data masking Using data rules Introduction to data rules - Using the Data Rules Editor - Selecting data rules - Binding data rule variables - Output link constraints - Adding statistics and attributes to the output information Use the Data Rules stage to valid foreign key references in source data Create custom data rules Demonstration 1: Using data rules Processing XML data Introduction to the Hierarchical stage - Hierarchical stage Assembly editor - Use the Schema Library Manager to import and manage XML schemas Composing XML data - Using the HJoin step to create parent-child relationships between input lists - Using the Composer step Writing Hierarchical data to a relational table Using the Regroup step Consuming XML data - Using the XML Parser step - Propagating columns Topic 6: Transforming XML data - Using the Aggregate step - Using the Sort step - Using the Switch step - Using the H-Pivot step Demonstration 1: Importing XML schemas Demonstration 2: Compose hierarchical data Demonstration 3: Consume hierarchical data Demonstration 4: Transform hierarchical data Updating a star schema database Surrogate keys - Design a job that creates and updates a surrogate key source key file from a dimension table Slowly Changing Dimensions (SCD) stage - Star schema databases - SCD stage Fast Path pages - Specifying purpose codes - Dimension update specification - Design a job that processes a star schema database with Type 1 and Type 2 slowly changing dimensions Demonstration 1: Build a parallel job that updates a star schema database with two dimensions Additional course details: Nexus Humans KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing 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 KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing 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 2 Days 12 CPD hours This course is intended for New users of IBM SPSS Statistics Users who want to refresh their knowledge about IBM SPSS Statistics Anyone who is considering purchasing IBM SPSS Statistics Overview Introduction to IBM SPSS Statistics Review basic concepts in IBM SPSS Statistics Identify the steps in the research process Review basic analyses Use Help Reading data and defining metadata Overview of data sources Read from text files Read data from Microsoft Excel Read data from databases Define variable properties Selecting cases for analyses Select cases for analyses Run analyses for groups Apply report authoring styles Transforming variables Compute variables Recode values of categorical and scale variables Create a numeric variable from a string variable Using functions to transform variables Use statistical functions Use logical functions Use missing value functions Use conversion functions Use system variables Use the Date and Time Wizard Setting the unit of analysis Remove duplicate cases Create aggregate datasets Restructure datasets Merging data files Add cases from one dataset to another Add variables from one dataset to another Enrich a dataset with aggregated information Summarizing individual variables Define levels of measurement Summarizing categorical variables Summarizing scale variables Describing the relationship between variables Choose the appropriate procedure Summarize the relationship between categorical variables Summarize the relationship between a scale and a categorical variable Creating presentation ready tables with Custom Tables Identify table layouts Create tables for variables with shared categories Create tables for multiple response questions Customizing pivot tables Perform Automated Output Modification Customize pivot tables Use table templates Export pivot tables to other applications Working with syntax Use syntax to automate analyses Create, edit, and run syntax Shortcuts in the Syntax Editor Controlling the IBM SPSS Statistics environment Set options for output Set options for variables display Set options for default working folders This course guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis. Students will learn the basics of reading data, data definition, data modification, data analysis, and presentation of analytical results. In addition to the fundamentals, students will learn shortcuts that will help them save time. This course uses the IBM SPSS Statistics Base; one section presents an add-on module, IBM SPSS Custom Tables. Introduction to IBM SPSS Statistics Review basic concepts in IBM SPSS Statistics Identify the steps in the research process Review basic analyses Use Help Reading data and defining metadata Overview of data sources Read from text files Read data from Microsoft Excel Read data from databases Define variable properties Selecting cases for analyses Select cases for analyses Run analyses for groups Apply report authoring styles Transforming variables Compute variables Recode values of categorical and scale variables Create a numeric variable from a string variable Using functions to transform variables Use statistical functions Use logical functions Use missing value functions Use conversion functions Use system variables Use the Date and Time Wizard Setting the unit of analysis Remove duplicate cases Create aggregate datasets Restructure datasets Merging data files Add cases from one dataset to another Add variables from one dataset to another Enrich a dataset with aggregated information Summarizing individual variables Define levels of measurement Summarizing categorical variables Summarizing scale variables Describing the relationship between variables Choose the appropriate procedure Summarize the relationship between categorical variables Summarize the relationship between a scale and a categorical variable Creating presentation ready tables with Custom Tables Identify table layouts Create tables for variables with shared categories Create tables for multiple response questions Customizing pivot tables Perform Automated Output Modification Customize pivot tables Use table templates Export pivot tables to other applications Working with syntax Use syntax to automate analyses Create, edit, and run syntax Shortcuts in the Syntax Editor Controlling the IBM SPSS Statistics environment Set options for output Set options for variables display Set options for default working folders Additional course details: Nexus Humans 0G53BG IBM SPSS Statistics Essentials (V26) 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 0G53BG IBM SPSS Statistics Essentials (V26) 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 2 Days 12 CPD hours This course is intended for This course is intended for those who need to use Tableau Desktop to build complex visuals and dashboards to present information or to monitor data about their organization. Overview Upon completion of this course, participants will be able to:Select the best method to work with multiple data sourcesCreate complex visuals using calculations and parametersApply best practices to improve the layout and aesthetics of dashboards This course enables participants to create complex visualizations and to combine them into interactive dashboards to share with others using Tableau Desktop. The Data Data Interpreter Data Joins Same Database Cross Databases Spatial Join New! Data Blending New Union Custom SQL Tableau Extract TDE Hyper Clipboard Database Changes Automatic Updates Calculations Regular Calculations Quick Table Calculations Table Calculations Level of Detail (LOD) Expressions Complex Visualizations Custom Background Map Web Map Servers Dual Maps Bar in Bar Graph Bullet Graph Pareto Chart Sparkline Report Top N Within a Category Report Waterfall Chart Funnel Chart Pattern Analysis using the Path Shelf Building Better Dashboards Best Practices for Design Best Practices for Performance Creating a Template Workbook Using Layout Containers Dashboard Extenders New! Generating A Performance Summary Additional course details: Nexus Humans Tableau Advanced v10.3 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 Tableau Advanced v10.3 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.

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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.