95 Data Analysis Expressions courses delivered Online

Level 1 Certificate in Understanding Data Protection and Data Security Unlock the world of Data Protection with our Level 1 Certificate course. Explore Data Protection Applications and delve into the essential Principles of GDPR. Learn about the Rights of Data Subjects, Data Protection Impact Assessments, Data Breaches, and the vital Exemptions in Data Protection. Learning Outcomes: Define Data Protection applications. Explain GDPR principles in Data Protection. Implement rights of a Data Subject. Evaluate Data Protection Impact Assessments (DPIA). Implement procedures for handling data breaches. Evaluate exemptions in Data Protection. More Benefits: LIFETIME access Device Compatibility Free Workplace Management Toolkit Level 1 Certificate in Understanding Data Protection and Data Security Course Syllabus Data Protection Applications: Explore the practical applications of data protection principles in various contexts, including business operations, information handling, and technology usage. Principles of GDPR: Gain a comprehensive understanding of the General Data Protection Regulation (GDPR) and its fundamental principles, including lawful processing, data subject rights, and accountability. Rights of Data Subject: Learn about the rights afforded to data subjects under data protection laws, such as the right to access, rectify, and erase personal data, and understand how to address these rights in compliance. Data Protection Impact Assessment (DPIA): Discover the importance of conducting DPIAs to assess and mitigate data protection risks associated with data processing activities, and learn how to perform effective DPIAs. Data Breaches: Understand the concept of data breaches, their implications, and the legal requirements for reporting and managing data breaches in accordance with data protection regulations. Exemptions in Data Protection: Explore specific exemptions and exceptions within data protection laws, including scenarios where data protection regulations may not apply or where limitations exist.

Imagine transforming your workplace into a thriving hub of motivated and engaged employees. 'Employee Engagement and Retention Strategies' offers a blueprint to achieving just that. This immersive course begins by laying the groundwork on the significance of employee engagement and its profound impact on organizational success. You will be guided through the nuances of fostering a motivating work environment, understanding that the heart of productivity and innovation lies in how valued and involved employees feel. As you progress, the course delves into the analytical aspect of engagement - understanding employee sentiment through surveys and data analysis. This insight is pivotal in crafting tailored engagement strategies. In the realms of leadership and team building, you will uncover techniques to inspire and manage teams for optimal engagement. The journey through this course further empowers you with the skills to support employee development and career growth, recognising that the growth of individuals fuels the advancement of the entire organisation. With a focus on recognition, rewards, and incentives, you will learn how to create a culture of appreciation and achievement, key elements in retaining top talent. Learning Outcomes Understand the essentials of employee engagement and its organisational impact. Create and maintain a motivating work environment. Effectively utilise employee engagement surveys for strategic insights. Apply leadership and management skills to boost employee engagement. Design and implement effective recognition and reward systems. Why choose this Employee Engagement and Retention Strategies course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Employee Engagement and Retention Strategies course for? HR professionals aiming to enhance engagement strategies. Team leaders and managers seeking to boost team morale. Organisational development consultants. Business owners focused on employee retention. Professionals in employee relations and personnel management. Career path Human Resources Manager - £30,000 to £50,000 Employee Engagement Specialist - £28,000 to £45,000 Organisational Development Consultant - £35,000 to £60,000 Team Leader/Manager - £32,000 to £48,000 Compensation and Benefits Manager - £33,000 to £55,000 Talent Acquisition Specialist - £27,000 to £42,000 Prerequisites This Employee Engagement and Retention Strategies does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Employee Engagement and Retention Strategies 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. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Module 01: Introduction to Employee Engagement and Its Impact Introduction to Employee Engagement and Its Impact 00:15:00 Module 02: Understanding Motivation and Creating a Motivating Work Environment Understanding Motivation and Creating a Motivating Work Environment 00:19:00 Module 03: Employee Engagement Surveys and Data Analysis Employee Engagement Surveys and Data Analysis 00:18:00 Module 04: Leadership and Management for Employee Engagement Leadership and Management for Employee Engagement 00:19:00 Module 05: Building Effective Teams for High Engagement Building Effective Teams for High Engagement 00:20:00 Module 06: Employee Development and Career Growth Employee Development and Career Growth 00:17:00 Module 07: Recognition, Rewards, and Incentives Recognition, Rewards, and Incentives 00:21:00

Welcome to Microsoft sql server development for everyone Course. The world of data is a vast and complex one. But with the right tools, it can be yours to explore. SQL Server is one of those tools. It's a powerful database management system that can help you store, manage, and analyse data in ways that were never before possible.   In this Microsoft SQL Server Development for Everyone! course, you'll learn the basics of SQL Server development. You'll learn how to create and manage databases, tables, and data. You'll also learn how to write SQL queries to manipulate data.   Starting with a solid Introduction to SQL, you'll grasp core concepts and gain a strong foundation. We'll then dive into Manipulating Tables and Data, equipping you with effective skills for storing, retrieving, updating, and deleting information.   But we won't stop there! Our in-depth exploration of Relationships and Foreign Keys will ensure you understand the importance of data integrity and how to establish connections between tables. You'll create robust and reliable databases with enforced referential integrity. Next, we'll empower you with the tools of Group By and Aggregate Functions, enabling efficient data analysis and summarisation. Extracting meaningful insights from vast amounts of information will become second nature to you.   To become a well-rounded SQL Server developer, we'll conclude the course with Advanced SQL Server Objects and Concepts. You'll gain a deep understanding of essential elements like stored procedures, views, triggers, and indexes. This knowledge will optimise performance and streamline your development process.   By the end of this Microsoft SQL Server Development for Everyone! course, you'll have a strong understanding of how to use SQL Server to manage and manipulate data. You'll be able to build powerful applications that make better decisions. Become a sought-after SQL Server developer and master the art of data management. Join us today and unlock your data-driven potential!   Learning Outcomes: Through this comprehensive course, you should be able to:   Understand core concepts and gain a strong foundation in SQL Server development. Acquire skills to effectively manipulate tables and data. Establish relationships and enforce data integrity using foreign keys. Utilise group by and aggregate functions for efficient data analysis. Explore advanced SQL Server objects and concepts like stored procedures and views. Gain proficiency in implementing triggers and optimising database performance. Develop the ability to create robust and reliable databases.   Who is this course for? This Microsoft SQL Server Development for Everyone! course is perfect for:   Aspiring developers looking to enhance their programming skills. Professionals seeking to expand their knowledge of SQL Server development. Individuals interested in exploring the world of databases and data management. Students or graduates pursuing a career in database administration or development. Curious learners eager to harness the power of SQL Server.   Career Path Our course will help you to pursue a range of careers, including:   Database Administrator: £25,000 - £60,000 per year. SQL Developer: £30,000 - £65,000 per year. Data Analyst: £25,000 - £55,000 per year. Business Intelligence Developer: £35,000 - £70,000 per year. Software Engineer: £30,000 - £70,000 per year. Data Engineer: £40,000 - £80,000 per year. Database Architect: £45,000 - £90,000 per year. Certification After studying the course materials of the Microsoft SQL Server Development for Everyone! 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. Prerequisites This Microsoft SQL Server Development for Everyone! does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Microsoft SQL Server Development for Everyone! 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.   Course Curriculum Introduction Introduction to SQL Server and Databases 00:04:00 Download and Install SQL Server Express 00:05:00 Manipulating Tables and Data Connect to Database Instance and Create a Database 00:06:00 Drop or Remove Database 00:04:00 What is a Database Table? 00:02:00 Create an SQL Database Table 00:32:00 Insert Data into the Table with SQL Script 00:15:00 Insert Data into the Table with Management Studio 00:05:00 Select Data from the Table with SQL Script 00:09:00 More Advanced Select Queries Part 1 00:18:00 Update Data in the Table 00:10:00 Update Data in the Table with Management Studio GUI 00:02:00 Delete Data from the Table with SQL Script 00:06:00 Delete Data from the Table with Management Studio GUI 00:01:00 Relationships and Foreign Keys What are Relationships, Foreign Keys and Normalization 00:11:00 Create a Relationship in SQL Server 00:19:00 Insert Records in Related Tables 00:13:00 Run Queries on Related Tables 00:19:00 Left, Right and Full Joins 00:11:00 Group By and Aggregate Functions Grouping Data using SQL GROUP BY Clause 00:12:00 SQL AVG Aggregate Function 00:11:00 SQL COUNT Aggregate Function 00:04:00 SQL MAX and MIN Aggregate Functions 00:04:00 SQL SUM Aggregate Function 00:04:00 Advanced SQL Server Objects and Concepts Create and Manage Views 00:23:00 Scalar Valued Functions 00:27:00 Inline Table Valued Functions 00:09:00 Multi Statement Table Valued Functions 00:12:00 Stored Procedures 00:28:00 Table Triggers 00:19:00 Database Scripting, Backup and Restore 00:15:00 Assignment Assignment - Microsoft SQL Server Development for Everyone! 00:00:00

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

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.