543 Data Analyst courses

Overview This comprehensive course on Excel Data Analysis for Beginner will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Excel Data Analysis for Beginner comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Excel Data Analysis for Beginner. It is available to all students, of all academic backgrounds. Requirements Our Excel Data Analysis for Beginner is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 3 sections • 11 lectures • 01:11:00 total length •Tracing Formulas: 00:04:00 •Using the Scenario Manager: 00:07:00 •Goal Seek: 00:03:00 •Solver: 00:03:00 •Data Tables: 00:11:00 •Data Analysis Tools: 00:16:00 •Forecast Sheet: 00:02:00 •Sumif, Countif,Averageif, Sumifs, and Countifs formulas: 00:09:00 •If, And, Or, and Nested If formulas: 00:16:00 •Resource - Excel Data Analysis for Beginner: 00:00:00 •Assignment - Excel Data Analysis for Beginner: 00:00:00

According to estimates, when businesses make decisions based on data and statistics, their productivity rises by 5%. As a result, the demand for analytical talents is growing as the world gets more and more data-driven. This Statistical Analysis course teaches you how to use data to make decisions, gain business insights, and forecast trends, giving you a competitive edge in any industry. Large-scale data collection, exploration, and presentation to identify underlying patterns and trends are known as statistical analysis. Every day, statistics are used in studies, business, and government to help make decisions more scientifically. For example, when introducing new products to the market, statistical analysis can offer helpful information for decision-making. Analysis can be performed to identify the product's trustworthy markets and forecast sales and demand. Additionally, it might be beneficial in choosing the ideal launch window. This course will improve your ability to make smarter, more impactful decisions in a fast-paced and uncertain world. It will help you to extract strategic business insights and use modelling to predict future trends. It will also help you with your data visualisation skills with which to communicate your findings. So enrol in the Statistical Analysis course and gain vital skills to start a successful career. Learning Outcomes: Understand how data-driven models can improve your decisions. Gain data analysis skills that you can apply in your role and organisation. Learn to assess the reliability of data, extract strategic business insights, and use modelling to predict future trends. Know about data visualisation skills with which to communicate your findings to all stakeholders. Learn about probability, binomial and normal distributions. Get to know the basic statistical terms. Why Prefer This Statistical Analysis Course? Opportunity to earn a certificate endorsed by the Quality Licence Scheme & another certificate accredited by CPD QS after completing the Statistical Analysis course Get a free student ID card! (£10 postal charges will be applicable for international delivery) Innovative and engaging content. Free assessments 24/7 tutor support. Take a step toward a brighter future! *** Course Curriculum *** Here is the curriculum breakdown of the Statistical Analysis course: Module 01: The Realm of Statistics Module 02: Basic Statistical Terms Module 03: The Center of the Data Module 04: Data Variability Module 05: Binomial and Normal Distributions Module 06: Introduction to Probability Module 07: Estimates and Intervals Module 08: Hypothesis Testing Module 09: Regression Analysis Module 10: Algorithms, Analytics and Predictions Module 11: Learning From Experience: The Bayesian Way Module 12: Doing Statistics: The Wrong Way Module 13: How We Can Do Statistics Better Assessment Process You have to complete the assignment questions given at the end of the course and score a minimum of 60% to pass each exam. Our expert trainers will assess your assignment and give you feedback after you submit the assignment. After passing the Diploma in Statistical Analysis at QLS Level 5 course exam, you will be able to request a certificate at an additional cost that has been endorsed by the Quality Licence Scheme. CPD 150 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone interested in learning more about the topic is advised to take this Statistical Analysis course. This course is open to everybody. Requirements You will not need any prior background or expertise to enrol in this course. Career path This Statistical Analysis course is meant to introduce statistical analysis. In the UK, statistical analysts make, on average, £35,817 per year. You will be able to significantly demonstrate your new skills and statistical knowledge. This can benefit you regarding job applications, professional advancement, and personal mastery. Certificates Certificate Accredited by CPD QS Digital certificate - £10 Diploma in Statistical Analysis at QLS Level 5 Hard copy certificate - £119 Show off Your New Skills with a Certificate of Completion Endorsed Certificate of Achievement from the Quality Licence Scheme After successfully completing the Diploma in Statistical Analysis at QLS Level 5, you can order an original hardcopy certificate of achievement endorsed by the Quality Licence Scheme. The certificate will be home-delivered, with a pricing scheme of - 119 GBP inside the UK 129 GBP (including postal fees) for International Delivery Certificate Accredited by CPD QS Upon finishing the Statistical Analysis course, you need to order to receive a Certificate Accredited by CPD QS that is accepted all over the UK and also internationally. The pricing schemes are: 10 GBP for Digital Certificate 29 GBP for Printed Hardcopy Certificate inside the UK 39 GBP for Printed Hardcopy Certificate outside the UK (International Delivery)

In an increasingly complex and interconnected world, intelligence analysis plays a crucial role in protecting national security. The demand for skilled intelligence analysts is growing, and there is a need for individuals with critical thinking skills and an understanding of the intelligence cycle. According to a report by the UK Government, the security and intelligence sector is expected to grow by 3.2% per year, creating around 42,000 jobs by 2020. By taking this course, individuals can equip themselves with the skills and knowledge necessary to contribute to national security efforts in the UK. Learning Outcomes: Understand the definition and purpose of intelligence analysis Develop an understanding of the intelligence cycle and best practices in analysis processes Learn the importance of critical thinking and how to structure analytical assessments Gain an understanding of legal issues and ethics in intelligence analysis Understand the roles, responsibilities, and functions of an intelligence analyst Be able to contribute to national security efforts through effective intelligence analysis Course Curriculum: Module 01: Defining Intelligence Analysis - In this module, you will learn the definition and purpose of intelligence analysis, including the types of intelligence and the role of the intelligence analyst. Module 02: Development of Intelligence Analysis - This module will cover the historical development of intelligence analysis, including the evolution of intelligence gathering and analysis techniques. Module 03: The Intelligence Cycle - In this module, you will learn about the intelligence cycle, including its stages and the role of the analyst in each stage. Module 04: Critical Thinking and Structuring - This module will cover critical thinking, including its importance in intelligence analysis and how to structure analytical assessments. Module 05: Analysis Process and Best Practice - In this module, you will learn best practices in the analysis process, including the use of analytical tools and techniques. Module 06: Intelligence and National Security - This module will cover the role of intelligence in national security, including its contribution to policy-making and the prevention of threats. Module 07: Legal Issues and Ethics - In this module, you will learn about legal issues and ethics in intelligence analysis, including privacy and human rights concerns. Module 08: Your Role, Responsibilities, and Functions as an Analyst - This module will cover the roles, responsibilities, and functions of an intelligence analyst, including effective communication, teamwork, and report writing. Certification Upon completion of the course, learners can obtain a certificate as proof of their achievement. You can receive a £4.99 PDF Certificate sent via email, a £9.99 Printed Hardcopy Certificate for delivery in the UK, or a £19.99 Printed Hardcopy Certificate for international delivery. Each option depends on individual preferences and locations. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Individuals interested in pursuing a career in intelligence analysis Current intelligence analysts seeking to enhance their skills and knowledge Career path Intelligence Analyst: £25,000-£50,000 per year Senior Intelligence Analyst: £40,000-£80,000 per year Intelligence Officer: £30,000-£60,000 per year Security Analyst: £35,000-£70,000 per year

Our Aim Is Your Satisfaction! Offer Ends Soon; Hurry Up!! Are you looking to improve your current abilities or make a career move? Our unique Data Analytics with Tableau course might help you get there! Expand your expertise with high-quality training - study the Data Analytics with Tableau course and get an expertly designed, great-value training experience. Learn from industry professionals and quickly equip yourself with the specific knowledge and skills you need to excel in your chosen career through theonline training course. The Data Analytics with Tableau course is broken down into several in-depth modules to provide you with the most convenient and rich learning experience possible. Upon successful completion of the Data Analytics with Tableau course, an instant e-certificate will be exhibited in your profile that you can order as proof of your skills and knowledge. Add these amazing new skills to your resume and boost your employability by simply enrolling in this course. This Data Analytics with Tableau training can help you to accomplish your ambitions and prepare you for a meaningful career. So, join us today and gear up for excellence! Why Prefer Us? Opportunity to earn a certificate accredited by CPDQS. Get a free student ID card!(£10 postal charge will be applicable for international delivery) Innovative and Engaging Content. Free Assessments 24/7 Tutor Support. Take a step toward a brighter future! *** Course Curriculum *** Here is the curriculum breakdown of the Data Analytics with Tableau course: Data Analytics with Tableau Module 01: Introduction to the Course Module 02: Project 1: Discount Mart (Sales and Profit Analytics) Module 03: Project 2: Green Destinations (HR Analytics) Module 04: Project 3: Superstore (Sales Agent Tracker) Module 05: Northwind Trade (Shipping Analytics) Module 06: Project 5: Tesla (Stock Price Analytics) Module 07: Bonus: Introduction to Database Concepts Module 08: Tableau Stories Assessment Process Once you have completed all the modules in the Data Analytics with Tableau course, you can assess your skills and knowledge with an optional assignment. Certificate of Completion The learners have to complete the assessment of this Data Analytics with Tableau course to achieve the CPDQS accredited certificate. Digital Certificate: £10 Hard Copy Certificate: £29 (Inside UK) Hard Copy Certificate: £39 (for international students) CPD 150 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone interested in learning more about the topic is advised to take this Data Analytics with Tableau course. This course is open to everybody. Requirements You will not need any prior background or expertise to enrol in this course. Career path After completing this course, you are to start your career or begin the next phase of your career.

Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:04:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:06:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Data Science & Machine Learning with Python 00:00:00

Data Analysis In Excel is suitable for anyone aspiring to or already working in this field or simply want to learn deeper into data analysis. You will be able to add this qualification to your CV by downloading your certificate instantly without any cost. To make this course more accessible for you, we have designed it for both part-time and full-time students. This course is packed into the bite-size module for your convenience. You can study at your own pace or become accredited within hours! If you require support, our experienced tutors are always available to help you throughout the comprehensive syllabus of this course and answer all your queries through email. This Data Analysis In Excel aims to introduce Data Analysis in Excel 2016. You'll learn the best way to enter and organize data, sort and map data, perform calculations with simple functions, format the appearance of your data and cells, and build charts and PivotTables for data analysis. You will know how to validate data, search and remove invalid data. The course covers Lookup information using VLOOKUP, and INDEX-MATCH, data forecasting and cleansing, providing external and 3D references and inserting sparklings, so that you have a great foundation in the world's most popular spreadsheet programme. Finally, you will learn different features to visualise or analyse your data in the most convenient way, which allows you to take the right business decisions for your company Who is this Course for? Data Analysis In Excel is perfect for anyone trying to learn potential professional skills. As there is no experience and qualification required for this course, it is available for all students from any academic background. Requirements Our Data Analysis In Excel is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation. CPD Certificate from Course Gate  At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £9 and the hard copy for £15. Also, you can order both PDF and hardcopy certificates for £22. Career path This course opens a new door for you to enter the relevant job market and also gives you the opportunity to acquire extensive knowledge along with required skills to become successful. You will be able to add our qualification to your CV/resume which will help you to stand out in the competitive job industry. Course Curriculum Modifying a Worksheet Insert, Delete, and Adjust Cells, Columns, and Rows 00:10:00 Search for and Replace Data 00:09:00 Use Proofing and Research Tools 00:07:00 Working with Lists Sort Data 00:10:00 Filter Data 00:10:00 Query Data with Database Functions 00:09:00 Outline and Subtotal Data 00:09:00 Analyzing Data Create and Modify Tables 00:15:00 Apply Intermediate Conditional Formatting 00:07:00 Apply Advanced Conditional Formatting 00:05:00 Visualizing Data with Charts Create Charts 00:13:00 Modify and Format Charts 00:12:00 Use Advanced Chart Features 00:12:00 Using PivotTables and PivotCharts Create a PivotTable 00:13:00 Analyze PivotTable Data 00:12:00 Present Data with PivotCharts 00:07:00 Filter Data by Using Timelines and Slicers 00:11:00 Working with Multiple Worksheets and Workbooks Use Links and External References 00:12:00 Use 3-D References 00:06:00 Consolidate Data 00:05:00 Using Lookup Functions and Formula Auditing Use Lookup Functions 00:12:00 Trace Cells 00:09:00 Watch and Evaluate Formulas 00:08:00 Automating Workbook Functionality Apply Data Validation 00:13:00 Search for Invalid Data and Formulas with Errors 00:04:00 Work with Macros 00:18:00 Creating Sparklines and Mapping Data Create Sparklines 00:07:00 MapData 00:07:00 Forecasting Data Determine Potential Outcomes Using Data Tables 00:08:00 Determine Potential Outcomes Using Scenarios 00:09:00 Use the Goal Seek Feature 00:04:00 Forecasting Data Trends 00:05:00 Certificate and Transcript Order Your Certificates or Transcripts 00:00:00

In today's data-driven world, the demand for skilled analysts is rising. Our Intelligence Analyst Certification course provides the knowledge and expertise to succeed in this critical field. With eight modules covering essential topics, this Intelligence Analyst course equips you with the theoretical foundations needed to excel as an intelligence analyst. Statistics reveal an increasing demand for skilled intelligence analysts in both the public and private sectors. In a complex world, gathering, analysing, and interpreting information is crucial for informed decision-making and safeguarding national security. Enrolling in our Intelligence Analyst Certification course positions you for a rewarding and impactful career in this Intelligence Analyst field. Learning Outcomes: By completing this Intelligence Analyst course, you will be able to: Define the role and principles of intelligence analysis, including its purpose, scope, and ethics, with our Intelligence Analyst course Understand its historical context and importance in modern security settings with our Intelligence Analyst course Use the intelligence cycle to effectively gather, evaluate, analyse, and share intelligence with our Intelligence Analyst course Apply critical thinking and structured techniques to interpret complex information accurately through our Intelligence Analyst course Follow best practices in intelligence analysis, including data collection, source evaluation, and report synthesis. Consider legal and ethical aspects such as privacy, data protection, and responsible intelligence use. "Intelligence Analyst Certification" Our comprehensive Intelligence Analyst Certification course covers eight modules essential for success in this field. You'll learn to define intelligence analysis and understand its development and the intelligence cycle. Gain critical thinking skills, and master the analysis process and best practices. Explore intelligence's role in national security, legal issues, and ethics. Finally, discover your responsibilities and functions as an analyst. Start your journey towards becoming a skilled intelligence analyst today. CPD 10 CPD hours / points Accredited by CPD Quality Standards Module 01: Defining Intelligence Analysis 17:29 1: Defining Intelligence Analysis Preview 17:29 Module 02: Development of Intelligence Analysis 18:04 2: Development of Intelligence Analysis 18:04 Module 03: The Intelligence Cycle 12:19 3: The Intelligence Cycle 12:19 Module 04: Critical Thinking and Structuring 17:12 4: Critical Thinking and Structuring 17:12 Module 05: Analysis Process and Best Practice 25:15 5: Analysis Process and Best Practice 25:15 Module 06: Intelligence and National Security 17:02 6: Intelligence and National Security 17:02 Module 07: Legal Issues and Ethics 17:40 7: Legal Issues and Ethics 17:40 Module 08: Your Role, Responsibilities, and Functions as an Analyst 21:28 8: Your Role, Responsibilities, and Functions as an Analyst 21:28 Who is this course for? The target audience for the Intelligence Analyst Certification course is: Aspiring intelligence analysts seeking theoretical knowledge. Law enforcement professionals who are eager to deepen their understanding of intelligence. Students of political science or international relations who are aiming to expand their career horizons. Policymakers who are looking to leverage intelligence in strategic decision-making. Requirements There is no formal qualification for Intelligence Analyst Certification. Anyone from any background can enrol in this Intelligence Analyst Certification. Career path Some career paths related to this Intelligence Analyst Certification in the UK are: Intelligence Analyst National Security Analyst Law Enforcement Intelligence Officer Corporate Intelligence Consultant Political Risk Analyst Counterterrorism Analyst The average salary for these roles is £28K to £70k per year.

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

Are you looking to enhance your Data Analysis with Excel skills? If yes, then you have come to the right place. Our comprehensive course on Data Analysis with Excel will assist you in producing the best possible outcome by mastering the Data Analysis with Excel skills. The Data Analysis with Excel is for those who want to be successful. In the Data Analysis with Excel, you will learn the essential knowledge needed to become well versed in Data Analysis with Excel. Our Data Analysis with Excel starts with the basics of Data Analysis with Excel and gradually progresses towards advanced topics. Therefore, each lesson of this Data Analysis with Excel is intuitive and easy to understand. Why would you choose the Data Analysis with Excel from Compliance Central: Lifetime access to Data Analysis with Excel materials Full tutor support is available from Monday to Friday with the Data Analysis with Excel Learn Data Analysis with Excel skills at your own pace from the comfort of your home Gain a complete understanding of Data Analysis with Excel Accessible, informative Data Analysis with Excel learning modules designed by expert instructors Get 24/7 help or advice from our email and live chat teams with the Data Analysis with Excel bundle Study Data Analysis with Excel in your own time through your computer, tablet or mobile device. A 100% learning satisfaction guarantee with your Data Analysis with Excel Improve your chance of gaining in demand skills and better earning potential by completing the Data Analysis with Excel Data Analysis with Excel Curriculum Breakdown of the Data Analysis with Excel Modifying a Worksheet Insert, Delete, and Adjust Cells, Columns, and Rows Search for and Replace Data Use Proofing and Research Tools Working with Lists Sort Data Filter Data Query Data with Database Functions Outline and Subtotal Data Analyzing Data Apply Intermediate Conditional Formatting Apply Advanced Conditional Formatting Visualizing Data with Charts Create Charts Modify and Format Charts Use Advanced Chart Features Using PivotTables and PivotCharts Create a PivotTable Analyze PivotTable Data Present Data with PivotCharts Filter Data by Using Timelines and Slicers Working with Multiple Worksheets and Workbooks Use Links and External References Use 3-D References Consolidate Data Using Lookup Functions and Formula Auditing Use Lookup Functions Trace Cells Watch and Evaluate Formulas Automating Workbook Functionality Apply Data Validation Search for Invalid Data and Formulas with Errors Work with Macros Creating Sparklines and Mapping Data Create Sparklines MapData Forecasting Data Determine Potential Outcomes Using Data Tables Determine Potential Outcomes Using Scenarios Use the Goal Seek Feature Forecasting Data Trends CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Data Analysis with Excel helps aspiring professionals who want to obtain the knowledge and familiarise themselves with the skillsets to pursue a career in Data Analysis with Excel. It is also great for professionals who are already working in Data Analysis with Excel and want to get promoted at work. Requirements To enrol in this Data Analysis with Excel, all you need is a basic understanding of the English Language and an internet connection. Career path The Data Analysis with Excel will enhance your knowledge and improve your confidence in exploring opportunities in various sectors related to Data Analysis with Excel. Certificates CPD Accredited PDF Certificate Digital certificate - Included CPD Accredited PDF Certificate CPD Accredited Hard Copy Certificate Hard copy certificate - £10.79 CPD Accredited Hard Copy Certificate Delivery Charge: Inside the UK: Free Outside of the UK: £9.99

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.