558 Data Analyst courses in London delivered Online

Criminology: Criminology Become a successful criminal investigative analyst by enrolling in our Level 5 Diploma in Criminology and Profiling course! Do you want to work at the intersection of psychology and law enforcement? Or do you want to develop your present skills? If so, our distinctive Level 5 Diploma in Criminology and Profiling course could be the key to your success. This Level 5 Diploma in Criminology and Profiling is a carefully designed course that guarantees that you fully comprehend everything about the subject. Get top-notch instruction from the Level 5 Diploma in Criminology and Profiling course to increase your level of experience. The course for the Level 5 Diploma in Criminology and Profiling is divided into a number of in-depth modules. Beginning with a brief introduction, both forensic science and criminology are covered in the Level 5 Diploma in Criminology and Profiling course. As a result, you will learn about victimology and crime types in the Level 5 Diploma in Criminology and Profiling program. You can easily understand criminal psychology and the criminal justice system in England and Wales after completing the Level 5 Diploma in Criminology and Profiling program. Dedicate yourself to excellence by enrolling in our Level 5 Diploma in Criminology and Profiling today! Main Course: Level 5 Diploma in Criminology and Profiling program Free Courses are including with this Diploma in Level 5 Diploma in Criminology and Profiling program Course Along with The Criminology: Criminology Course, We Offer a free Criminal Intelligence Analyst Course Along with Criminology: Criminology Course, We Offer a free IT Security Course Special Offers of this Criminology: Criminology Course This Criminology: Criminology Course includes a FREE PDF Certificate. Lifetime access to this Criminology: Criminology Course Instant access to this Criminology: Criminology Course Get FREE Tutor Support to this Criminology: Criminology Course Criminology: Criminology In human societies, crimes and other wrongdoings are inevitable. Because of this, criminology—the study of crime—has developed to look into and explain the true reasons behind crimes, how they happen, and how to deal with them. The purpose of this Level 5 Criminology and Psychology course is to give you a thorough understanding of criminology and the criminal justice system. Who is this course for? Criminology: Criminology For people who are interested in learning about criminology and profiling and pursuing a career in these sectors, this Level 5 Diploma in Criminology: Criminology and Profiling program is the best option. Requirements Criminology: Criminology To enrol in this Criminology: Criminology Course, students must fulfil the following requirements. To join in our Criminology: Criminology Course, you must have a strong command of the English language. To successfully complete our Criminology: Criminology Course, you must be vivacious and self driven. To complete our Criminology: Criminology Course, you must have a basic understanding of computers. A minimum age limit of 15 is required to enrol in this Criminology: Criminology Course. Career path Criminology: Criminology You might be able to pursue a number of attractive job prospects after completing this Level 5 Diploma in Criminology and Profiling: Criminology course, including: Detectives, Crime Journalists, Crime Reporters, Attorneys, Psychologists, and Counselors.

Overview This comprehensive course on Python for Data Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python for Data Analysis comes with accredited certification, 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 Python for Data Analysis. It is available to all students, of all academic backgrounds. Requirements Our Python for Data Analysis 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 Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 19 sections • 99 lectures • 00:08:00 total length •Welcome & Course Overview: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:37:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method ..: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00 •Resources- Python for Data Analysis: 00:00:00

Overview This comprehensive course on Clinical Data Analysis with SAS will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Clinical Data Analysis with SAS comes with accredited certification, 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 Clinical Data Analysis with SAS. It is available to all students, of all academic backgrounds. Requirements Our Clinical Data Analysis with SAS 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 Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 5 sections • 30 lectures • 01:54:00 total length •Course Promo: 00:01:00 •1.1 Components of the Pharma Industry: 00:05:00 •1.2 Phases of Clinical Trials: 00:06:00 •1.3 Data and Reports in Clinical Trials: 00:04:00 •1.4 Types of Data: 00:05:00 •2.1 Clinical Study Protocol: 00:02:00 •2.2 Ethical Consent: 00:01:00 •2.3 Inclusion-Exclusion Criteria: 00:01:00 •2.4 Statistical Analysis Plan: SAP, Mockshell and CRF: 00:04:00 •3.1 General SAS Programming Steps: 00:02:00 •3.2 One Search Report: Demographics Table: 00:04:00 •3.3 Understanding the Demographics Table: 00:03:00 •3.4 Programming the Demographics Table: 00:05:00 •3.5 Importing Raw Demographic Data into the SAS: 00:04:00 •3.6 Deciding what Procedure to Use: 00:02:00 •3.7 Deriving the AGE variable: 00:10:00 •3.8 Obtaining Summary Statistics for AGE: 00:04:00 •3.9 Adding the 3rd Treatment Group using Explicit Output: 00:05:00 •3.10 Deriving the SEX variable: 00:03:00 •3.11 Obtaining Summary Statistics for SEX: 00:03:00 •3.12 Concatenating the COUNT and PERCENT Variables: 00:03:00 •3.13 Deriving the RACE Variable: 00:03:00 •3.14 Obtaining Summary Statistics for RACE: 00:03:00 •3.15 Stacking All the 3 Summary Statistics Together: 00:06:00 •3.16 Fixing the Precision Points: 00:04:00 •3.17 Transposing Data: 00:03:00 •3.18 Fixing the Order of Statistical Parameters: 00:05:00 •3.19 Building the Final Report: 00:02:00 •3.20 Putting the Final Touches to the Report: 00:11:00 •Resources - Clinical Data Analysis with SAS: 00:00:00

IT Security & Data Analysis Course - CPD Certified Open up your IT passion by unlocking our IT security course! Do you want to begin a career as a professional in IT security? Do you want to expand your knowledge about IT security? With the help of this IT Security course, you'll be more determined than ever to advance your professional career and broaden your knowledge in this IT security area. Although they sound similar, information security and IT security refer to different types of security. Information security refers to the practices and tools used to prevent unauthorised access to sensitive corporate data, whereas IT security is the protection of digital data through computer network security. Even though maintaining IT security may be expensive, a significant breach may cost a company far more. This IT Security Course will provide you with a solid foundation so that you can develop the confidence to become an expert in IT Security and acquire more sophisticated skills to fill in the gaps for increased effectiveness and productivity. If you think you have what it takes to enter this IT security field, an IT security course can help you with your initial training and job preparation. IT Security is ready with all the necessary data that is meant to instruct and direct people in the requirements for this position. Don't wait any longer. Enrol in our IT security course to become a certified IT security professional. IT Security & Data Analysis Course - CPD CertifiedCourse This IT Security: IT Security Course includes a FREE PDF Certificate. Lifetime access to this IT Security: IT Security Course Instant access to this IT Security: IT Security Course Get FREE Tutor Support to this IT Security: IT Security Course IT Security & Data Analysis Course - CPD Certified Unlock your career potential with our IT Security course! Ready to dive into the world of IT Security? Our comprehensive IT Security course equips you with essential skills to protect digital data and secure networks. You’ll gain expertise in areas like information security vs. IT Security, learning the differences and critical tools needed for each. This IT Security course prepares you to tackle real-world challenges, understand the financial impacts of security breaches, and become a trusted expert. Don’t miss out—enrol in our IT Security course today and set yourself on the path to becoming a certified IT Security professional! Who is this course for? IT Security & Data Analysis Course - CPD Certified Anyone who wants to work in the IT industry can take our It Security: IT Security course. Requirements IT Security & Data Analysis Course - CPD Certified To enrol in this IT Security Course, students must fulfil the following requirements: Good Command over English language is mandatory to enrol in our IT Security: IT Security Course. Be energetic and self-motivated to complete our IT Security: IT Security Course. Basic computer Skill is required to complete our IT Security: IT Security Course. If you want to enrol in our IT Security: IT Security Course, you must be at least 15 years old. Career path IT Security & Data Analysis Course - CPD Certified Many doors in the job market will be made available by the IT Security course. For instance, an IT technician, a cyber security analyst, or a penetration tester. The average salary for IT security professionals in the UK ranges between £60,000 and £100,000 per annum.

Overview This comprehensive course on Data Analysis and Forecasting in Excel will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Analysis and Forecasting in Excel 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 Data Analysis and Forecasting in Excel. It is available to all students, of all academic backgrounds. Requirements Our Data Analysis and Forecasting in Excel 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 10 sections • 31 lectures • 04:43:00 total length •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 •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 •Apply Intermediate Conditional Formatting: 00:07:00 •Apply Advanced Conditional Formatting: 00:05:00 •Create Charts: 00:13:00 •Modify and Format Charts: 00:12:00 •Use Advanced Chart Features: 00:12:00 •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 •Use Links and External References: 00:12:00 •Use 3-D References: 00:06:00 •Consolidate Data: 00:05:00 •Use Lookup Functions: 00:12:00 •Trace Cells: 00:09:00 •Watch and Evaluate Formulas: 00:08:00 •Apply Data Validation: 00:13:00 •Search for Invalid Data and Formulas with Errors: 00:04:00 •Work with Macros: 00:18:00 •Create Sparklines: 00:07:00 •MapData: 00:07:00 •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

Overview Build a professional profile and boost your career by enrolling in the Complete Introduction to Business Data Analysis Level 3. The world is nowadays run by data. Data analysis is one of the most crucial methods used in business worldwide. This course will help you learn the art of practical business analysis, along with its functions and objectives from a contemporary corporate perspective. You will be able to apply a data-driven approach with the knowledge learned from the course and become a successful business analyst. So, what are you waiting for? Start learning and get the benefits by enrolling today! 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 Complete Introduction to Business Data Analysis Level 3. It is available to all students, of all academic backgrounds. Requirements Our Complete Introduction to Business Data Analysis Level 3 is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible on tablets and smartphones so you can access your course on wifi, 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 Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management , Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 2 sections • 14 lectures • 04:55:00 total length •Module 1: Statistics Fundamentals: 00:15:00 •Module 2: Data Analysis: 00:30:00 •Module 3: Probability: 00:30:00 •Module 4: Random Variables and Discrete Distributions: 00:25:00 •Module 5: Continuous Distributions: 00:15:00 •Module 6: Sampling Distributions: 00:15:00 •Module 7: Confidence Interval: 00:35:00 •Module 8: Hypothesis Testing with One Sample: 00:25:00 •Module 9: Hypothesis Testing with Two Samples: 00:15:00 •Module 10: The Chi-Square Distribution: 00:25:00 •Module 11: F Distribution and One-Way ANOVA: 00:25:00 •Module 12: Correlation analysis: 00:20:00 •Module 13: Simple Linear Regression Analysis: 00:20:00 •Assignment - Complete Introduction to Business Data Analysis Level 3: 00:00:00

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)

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