67 Data Analysis Expressions courses

Data Analyst (Data Analytics) - CPD Certified Have you ever wondered how companies get insights from massive volumes of data to stay competitive and make wise decisions? If so, then participate in our exclusive Data Analytics Course. This Data Analytics Course describes the fundamentals of data, statistics, and an introduction to data analytics. How to get data and where to find it is explained in the Data Analytics Course. Moreover, this Data Analytics Course covers data cleansing, preprocessing, and exploratory data analysis (EDA). Additionally, the Data Analytics Course provides an introduction to Python and Excel for data analytics. This thorough Data Analytics Course includes lessons on data wrangling with Pandas (python) and data visualisation using Matplotlib and Seaborn (python). Enrol in our Data Analytics Course to study the fundamentals of statistical analysis and machine learning. Special Offers of this Data Analyst (Data Analytics) Course * Data Analyst (Data Analytics) Course includes a FREE PDF Certificate. * Lifetime access to this Data Analyst (Data Analytics) Course * Instant access to this Data Analyst (Data Analytics) Course * Get FREE Tutor Support from Monday to Friday in this Data Analyst (Data Analytics) Course Courses are included in this Data Analyst (Data Analytics) Course * Course 01: Cyber Security * Course 02: GDPR * Course 03: Business Administration [ Note: Free PDF certificate as soon as completing the Data Analyst (Data Analytics) Course] Course Curriculum of Data Analyst (Data Analytics) - CPD Certified * Module 1: Introduction to Data Analytics * Module 2: Basics of Data and Statistics * Module 3: Data Collection and Sources * Module 4: Data Cleaning and Preprocessing * Module 5: Exploratory Data Analysis (EDA) * Module 6: Introduction to Excel for Data Analytics * Module 7: Introduction to Python for Data Analytics * Module 8: Data Wrangling with Pandas (Python) * Module 9: Data visualisation with Matplotlib and Seaborn (Python) * Module 10: Introduction to Basic Statistical Analysis * Module 11: Introduction to Machine Learning * Module 12: Capstone Project - Exploratory Data Analysis Assessment Method After completing each module of the Data Analyst (Data Analytics) Course, you will find automated MCQ quizzes. To unlock the next module, you need to complete the quiz task and get at least 60% marks. Certification After completing the MCQ/Assignment assessment for this Data Analyst (Data Analytics) course, you will be entitled to a Certificate of Completion from Training Tale. The certificate is in PDF format, which is completely free to download. A printed version is also available upon request. It will also be sent to you through a courier for £13.99. WHO IS THIS COURSE FOR? Data Analyst (Data Analytics) - CPD Certified For business professionals, entrepreneurs, or anybody else looking to have a thorough grasp of data analysis in a commercial setting, this Data Analytics Course is ideal. REQUIREMENTS There are no specific requirements for Data Analyst (Data Analytics) Course because it does not require any advanced knowledge or skills. CAREER PATH Data Analyst (Data Analytics) - CPD Certified This Data Analytics Course will assist you in obtaining positions as a business analyst, marketing analyst, data analyst, and in related fields. CERTIFICATES CERTIFICATE OF COMPLETION Digital certificate - Included

OVERVIEW Are you ready to dive into data analysis and unlock valuable insights that can drive business growth? Introduction to Data Analysis is a comprehensive course designed to provide learners with a solid foundation in the principles and techniques of data analysis.  This course will equip you with the essential skills and knowledge to thrive in data analysis. With a comprehensive curriculum spanning 22 modules, you will delve deep into the world of data, from understanding its fundamentals to harnessing it for meaningful insights. Our expertly crafted curriculum covers data collection, process management, statistical tools, control charts, and strategies to deal with variation. Enrol today in this Introduction to Data Analysis course to succeed in all areas of your life. Let your skill become your greatest asset! KEY FEATURES OF THE COURSE * This course equips learners with the essential tools and knowledge to analyse and interpret data accurately. * The course is accredited, ensuring its recognition and credibility within the professional community. * Learners will have access to 24/7 learning assistance, allowing them to seek guidance and support whenever needed. WHO IS THIS COURSE FOR? This Introduction to Data Analysis course is suitable for students & professionals from various fields, including business, finance, marketing, healthcare, and more. It is also beneficial for individuals looking to enhance their data analysis skills for personal or professional development. WHAT YOU WILL LEARN Throughout the course, you will delve into various modules that cover essential concepts and tools for data analysis. Starting with an introduction to process management principles, you will progress through topics such as the voice of the process, customer feedback analysis, and data visualisation using tools like Pareto charts, histograms, and run charts. You will also gain a deep understanding of variation and control charts and learn how to interpret and use them effectively for process improvement. By the end of the course, learners will have a solid understanding of these topics and be able to apply their knowledge to real-world data analysis scenarios. WHY ENROL IN THIS COURSE Data analysis is one of the most sought-after skills in today's job market. According to LinkedIn, data analysis is among the top 10 most in-demand skills for 2023. Data analysis can help you: * Improve your decision-making skills and confidence * Enhance your problem-solving and critical-thinking abilities * Boost your creativity and innovation potential * Increase your productivity and efficiency * Advance your career and create earning potential REQUIREMENTS There are no specific prerequisites for this course. However, a basic understanding of mathematics and statistics will be beneficial. Access to a computer with an internet connection is also required to complete the course. CAREER PATH Completing this Introduction to Data Analysis course can lead to various career opportunities in data analysis. Some of the course-related professions in the UK, along with their average salaries, include: * Data Analyst (£35,000 - £45,000 per year) * Business Analyst (£40,000 - £55,000 per year) * Financial Analyst (£40,000 - £60,000 per year) * Market Research Analyst (£25,000 - £40,000 per year) * Operations Analyst (£35,000 - £50,000 per year) * Healthcare Data Analyst (£35,000 - £50,000 per year) * Quality Control Analyst (£25,000 - £35,000 per year) CERTIFICATION Upon successful completion of the course, learners will receive a CPD certificate, validating their achievement and demonstrating their commitment to professional development in the field of data analysis. COURSE CURRICULUM 3 sections • 24 lectures • 02:13:00 total length •Module 01: Introduction: 00:02:00 •Module 02: Agenda and Principles of Process Management: 00:06:00 •Module 03: The Voice of the Process: 00:05:00 •Module 04: Working as One Team for Improvement: 00:04:00 •Module 05: Exercise: The Voice of the Customer: 00:03:00 •Module 06: Tools for Data Analysis: 00:07:00 •Module 07: The Pareto Chart: 00:03:00 •Module 08: The Histogram: 00:03:00 •Module 09: The Run Chart: 00:04:00 •Module 10: Exercise: Presenting Performance Data: 00:05:00 •Module 11: Understanding Variation: 00:06:00 •Module 12: The Control Chart: 00:06:00 •Module 13: Control Chart Example: 00:04:00 •Module 14: Control Chart Special Cases: 00:06:00 •Module 15: Interpreting the Control Chart: 00:10:00 •Module 16: Control Chart Exercise: 00:07:00 •Module 17: Strategies to Deal with Variation: 00:06:00 •Module 18: Using Data to Drive Improvement: 00:14:00 •Module 19: A Structure for Performance Measurement: 00:06:00 •Module 20: Data Analysis Exercise: 00:06:00 •Module 21: Course Project: 00:03:00 •Module 22: Test your Understanding: 00:17:00 •Resources - Introduction to Data Analysis: 00:00:00 •Assignment - Introduction to Data Analysis: 00:00:00

"The best way to predict the future is to create it." In today's data-driven world, the demand for skilled Data Analysts is skyrocketing. From businesses harnessing vast amounts of information to predict market trends to policymakers leveraging insights for a better future, the Data Analyst stands at the epicentre of these transformations. As a Data Analyst, you'll have the tools to turn raw data into actionable insights, driving strategic decisions for businesses worldwide. With the right training, a Data Analyst can decipher the narratives behind numbers, revealing trends, and crafting effective business strategies. Immerse yourself in our carefully curated curriculum and transform from a novice to an in-demand Data Analyst, ready to navigate the ever-evolving data landscape. The future beckons, and it's data-rich. Become the Data Analyst the industry needs today! Learning Outcomes: Here is a glimpse of what you are going to learn with us: * Understand data analysis concepts and techniques. * Learn how to collect, clean and process data. * Learn how to visualise data using different tools and techniques. * Understand the principles of Google Data Studio. * Learn how to use machine learning algorithms to analyse data. * Learn how to use data mining techniques to discover patterns in forecasting. * Take your Excel skills to the next level. Our comprehensive Data Analyst (Data Analytics) Endorsed training is designed to equip you with the essential skills and knowledge needed to excel in this dynamic field. Course 01: Introduction to Data Analysis Every aspiring Data Analyst must begin with a solid foundation. "Introduction to Data Analysis" is the first step for budding Data Analysts, designed to equip you with the foundational concepts integral to the field. Understand the significance and role of a Data Analyst in today's data-driven world and grasp the methodologies that drive actionable insights from vast datasets. Course 02: Google Data Studio: Data Analytics In the toolbox of every proficient Data Analyst is a mastery of data visualisation tools. This course dives deep into Google Data Studio, an essential platform for Data Analysts. Learn how to transform complex data into compelling visual narratives, making your insights accessible and actionable for stakeholders. Course 03: Data Analysis & Forecasting The mark of an expert Data Analyst lies in prediction. "Data Analysis & Forecasting" equips Data Analysts with the skills to anticipate future trends from historical data. Delve into forecasting techniques, ensuring you stand out as a forward-thinking Data Analyst, always one step ahead in strategic decision-making. This unparalleled bundle provides the skills and knowledge you need to become a proficient data analyst. So, don't wait any longer. Enrol now and start learning. Quality Licence Scheme Endorsed Certificate of Achievement: Upon successful completion of the course, you will be eligible to order an original hardcopy certificate of achievement. This prestigious certificate, endorsed by the Quality Licence Scheme, will be titled 'Diploma in Data Analyst (Data Analytics) at QLS Level 5'. Your certificate will be delivered directly to your home. The pricing scheme for the certificate is as follows: * £109 GBP for addresses within the UK. Please note that delivery within the UK is free of charge. Please Note: NextGen Learning is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. CPD 150 CPD hours / points Accredited by CPD Quality Standards COURSE 01: INTRODUCTION TO DATA ANALYSIS 2:09:44 * 1: Module 01: Introduction Preview 02:22 * 2: Module 02: Agenda and Principles of Process Management Preview 06:28 * 3: Module 03: The Voice of the Process 04:35 * 4: Module 04: Working as One Team for Improvement 04:18 * 5: Module 05: Exercise: The Voice of the Customer 03:05 * 6: Module 06: Tools for Data Analysis 07:10 * 7: Module 07: The Pareto Char 02:51 * 8: Module 08: The Histogram 02:45 * 9: Module 09: The Run Chart 03:47 * 10: Module 10: Exercise: Presenting Performance Data 04:42 * 11: Module 11: Understanding Variation 06:05 * 12: Module 12: The Control Chart 07:22 * 13: Module 14: Control Chart Special Cases 06:13 * 14: Module 15: Interpreting the Control Chart 09:42 * 15: Module 16: Control Chart Exercise 07:22 * 16: Module 17: Strategies to Deal with Variation 05:38 * 17: Module 18: Using Data to Drive Improvement 13:48 * 18: Module 19: A Structure for Performance Measurement 05:59 * 19: Module 20: Data Analysis Exercise 05:39 * 20: Module 21: Course Project 03:17 * 21: Module 22: Test your Understanding 16:36 COURSE 02: GOOGLE DATA STUDIO: DATA ANALYTICS 2:39:59 * 22: Module 01: Introduction to GDS 35:33 * 23: Module 02: Data Visualization 1:29:03 * 24: Module 03: Geo-visualization 15:36 * 25: Module 04: A Socio-Economic Case Study 19:47 COURSE 03: DATA ANALYSIS & FORECASTING 1:10:51 * 26: Excel Data Analysis - 1 03:49 * 27: Excel Data Analysis - 2 06:35 * 28: Excel Data Analysis - 3 03:23 * 29: Excel Data Analysis - 4 03:14 * 30: Excel Data Analysis - 5 10:52 * 31: Excel Data Analysis - 6 15:51 * 32: Excel Data Analysis - 7 02:00 * 33: Excel Data Analysis - 8 09:14 * 34: Excel Data Analysis - 9 15:53 ORDER YOUR CPD QUALITY STANDARD CERTIFICATE (OPTIONAL) 01:00 * 35: CPD Quality Standard Certificate (Optional) 01:00 PDF ORDER YOUR QLS ENDORSED CERTIFICATE (OPTIONAL) 01:00 * 36: QLS Endorsed Certificate (Optional) 01:00 PDF WHO IS THIS COURSE FOR? Besides, our Data Analyst (Data Analytics) Endorsed training can empower employees, business owners, students, and anyone interested in data analysis to make data-driven decisions. * Aspiring Data Analysts * Data enthusiasts * Business professionals * Data specialists * Managers and decision-makers * Career-driven individuals REQUIREMENTS Without any formal requirements, you can delightfully enrol in this Data Analyst (Data Analytics) Endorsed training. CAREER PATH Get edges over others after completing our endorsed training. There are thousands of career opportunities available. Such as: * Data Analyst: £30,000 - £55,000 * Business Intelligence Analyst: £35,000 - £65,000 * Data Scientist: £45,000 - £90,000 * Big Data Analyst: £35,000 - £80,000 * Marketing Data Analyst: £25,000 - £55,000 CERTIFICATES CPD QUALITY STANDARD CERTIFICATE Digital certificate - £4.99 HARDCOPY CERTIFICATE (FREE UK DELIVERY) Hard copy certificate - £9.99 Hardcopy Transcript: £9.99

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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