1129 Data Analysis courses in Glasgow delivered Online

Description This course is aimed at the excel user who already knows the basics of Excel 2007. This course will help you to go beyond the basics - to reach a higher-intermediate level. With shortcuts, tricks and tips - you will be able to work smarter and faster. If you want to be fairly competent on the software, then this course will be very handy. Its a lot quicker to be shown things, then to try and muddle through and work things out by yourself. Guaranteed, there'll be some items which we cover, that you have no idea that Excel was capable of doing! We don't want you to spend a day of your life in the classroom... learn 99 quick and very useful, practical things which you can apply on your job or on your projects. We'll cover: Working with Data - using series, symbols and special characters, hide rows and freeze panels Formulas and Functions - Calculate the duration between two dates/times, best loan terms, create conditional formula and conditional sums Copying Data - transposing rows into columns and paste specials Using Excel lists - sort and filter a list, remove duplicate records, count filtered records, look up information in a list Data Patterns - Pivot tables, pivot charts, what-if analysis Creating charts - histogram, trendlines, piecharts, error bars Presenting data - formatting columns and numbers Saving and printing worksheets - printing multiple worksheets, area, cell ranges, repeat headings of a row or column Extending excel - hyperlinks, embed a chart, importing a worksheet Customizing Excel - custom workspace, custom view, macros The target audience is those who have a basic level with Excel and want to learn other handy functions and features. We use Excel 2007 only in this course. If you have Excel 2003, this course will be difficult to follow as although the functions and features exist in 2003, the layout changed very dramatically between the two versions. Excel 2007 isn't too dissimilar from 2010. Where necessary, we provide a spreadsheet - but as long as you have Excel 2007, you'll be able to copy and do exactly what you see on the screen by pausing the video and following along. The course will take approx 7.5 hours to complete. Take this course if you want to take your basic understanding of Excel to a higher intermediate-level. What Will I Learn? Work with Formulas and Functions Copy data and formats Use excel lists Create charts Present worksheets nicely Save and print worksheets Who is the target audience? This is for those who are beginners in Excel and want to take it further to higher intermediate level - using Excel 2007 You should know the 'basics' in Excel and we take it from near-beginner, to higher intermediate Requirements Students will need to have Excel 2007 installed, as this is the system used in the teaching Introduction to the course and your tutor Data Enter numbers and fractions 00:05:00 Dates and Times 00:08:00 Name Cells and Ranges 00:04:00 Validate Data entry using a pick list FREE 00:07:00 Extend a series of dates with autofill 00:04:00 Add a symbol or special character 00:04:00 Compare multiple spreadsheets using freeze panels 00:06:00 Hide rows 00:04:00 Keyboard 00:10:00 Speak Cells 00:04:00 Find and replace data 00:10:00 Formulas and Functions Add values 00:03:00 Function wizard 00:06:00 Formulas with comments 00:04:00 OneNote 00:06:00 Define a constant 00:04:00 Apply names in functions 00:05:00 Figure out the best loan terms 00:04:00 Internal Rates of Return 00:04:00 Nth largest value 00:04:00 Large, Small, Max, Min 00:04:00 Conditional formula 00:03:00 Conditional formula with names 00:04:00 Conditional sum 00:03:00 Count If 00:02:00 Inner calculator 00:02:00 Square Roots 00:03:00 Calculate the duration between two times 00:04:00 Calculate days between two dates 00:04:00 Copying Data, Formats etc., Copy a range 00:03:00 Transpose a row into a column FREE 00:02:00 Chart formatting 00:07:00 Copy styles to another workbook 00:07:00 Paste special and copying worksheets 00:06:00 Track changes while Editing 00:06:00 Lists Enter list data using a form 00:05:00 Searching through a data list 00:04:00 Import a word list into excel 00:04:00 Sort a list 00:02:00 Filter a list 00:02:00 Sort by multiple criteria FREE 00:03:00 Find averages in a sorted group 00:05:00 Filter by multiple criteria 00:03:00 Remove duplicate records from a list 00:03:00 Count filtered records 00:07:00 Filter by multiple criteria in the same column 00:06:00 Chart a filtered list 00:02:00 Look up information in a List 00:05:00 Data Patterns Create a PivotTable 00:05:00 Modify a PivotTable and layout 00:03:00 Find the average of a field 00:04:00 Create a calculated field 00:03:00 Calculated fields and charts FREE 00:02:00 Hide rows and columns in a PivotTable 00:02:00 AutoFormat a PivotTable 00:01:00 Create a PivotChart 00:03:00 Turning on the Data Analysis function so that statistical information can be run 00:02:00 Describe Data with Statistics 00:03:00 Discover associations within your data 00:03:00 Product Numbers 00:02:00 What-if analysis 00:05:00 Goal seek 00:06:00 Creating Charts Create a Chart 00:06:00 Modify Chart details 00:04:00 Change the Chart Type 00:03:00 Add a Trendline to a Chart 00:06:00 Remove Chart Data 00:02:00 Add chart data 00:02:00 Missing chart data 00:04:00 Error bars 00:04:00 Pull a slice from a Pie Chart 00:03:00 Label slices of a Pie Chart 00:04:00 Histogram 00:06:00 Paste a chart into Word 00:03:00 Amending a chart in Word 00:02:00 Paste-link a Chart into Word 00:05:00 Worksheets Format Numbers as Percentages 00:04:00 Format Currencies in Currency and Accounting Formats 00:03:00 Format Column Headings 00:05:00 Format Quickly with Format painter FREE 00:02:00 Insert a background image into your worksheet 00:03:00 Create a Transparent image 00:03:00 Saving and Printing Worksheets Save a workbook as a Template 00:07:00 Save a workbook as an XML spreadsheet 00:05:00 Print multiple cell ranges on One Page 00:03:00 Print multiple worksheets of a workbook 00:01:00 Page set up, header, footer, margins - 1 page printing 00:04:00 Repeat Headings of a Row or Column 00:07:00 Print functions to show calculations & comments 00:07:00 Extending Excel Hyperlink a worksheet to another office document 00:03:00 Embed a chart within PowerPoint 00:05:00 Publish an Interactive Workbook 00:05:00 Import a Worksheet into Access 00:09:00 Use Excel Data to create labels in Word 00:10:00 Customizing Excel Launch a specific Workbook when Excel Opens 00:03:00 Save Time by Creating a Custom View 00:03:00 Create a Custom Number Format 00:06:00 Changing Text to Columns 00:05:00 Create a Macro to Format Numbers 00:06:00 Online Live Webinar Course-S3_W4_L1 - 42 - Excel Made simple 01:00:00

Overview In this age of technology, data science and machine learning skills have become highly demanding skill sets. In the UK a skilled data scientist can earn around £62,000 per year. If you are aspiring for a career in the IT industry, secure these skills before you start your journey. The Complete Machine Learning & Data Science Bootcamp 2023 course can help you out. This course will introduce you to the essentials of Python. From the highly informative modules, you will learn about NumPy, Pandas and matplotlib. The course will help you grasp the skills required for using python for data analysis and visualisation. After that, you will receive step-by-step guidance on Python for machine learning. The course will then focus on the concepts of Natural Language Processing.  Upon successful completion of the course, you will receive a certificate of achievement. This certificate will help you elevate your resume. So enrol 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? Anyone with an interest in learning about data science can enrol in this course. It will help aspiring professionals develop the basic skills to build a promising career. Professionals already working in this can take the course to improve their skill sets. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush.   Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst Course Curriculum 18 sections • 98 lectures • 23:48:00 total length •Welcome & Course Overview6: 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): 00:17:00 •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

Excel is everywhere — and so is the demand for people who can use it efficiently. This 20-in-1 Microsoft Excel bundle is your fast-track to becoming hireable across finance, administration, IT, and data-focused industries. If you want to future-proof your CV with practical, in-demand office skills, this is the place to start. 💼 Description From payroll and tax to data analysis and compliance — Excel is the go-to tool that employers expect you to know inside out. This bundle combines the most relevant tools and techniques needed in real-world job settings. Perfect for administrative professionals, finance assistants, analysts, and office support staff, this course package adds breadth to your technical profile while showcasing soft skills like problem-solving and communication. Skip the spreadsheets that go nowhere. Enrol now to unlock 20 practical courses — and give your next job application the clarity it deserves. ❓ FAQ Q: What roles is this bundle suitable for? A: Data analyst, finance assistant, admin support, payroll clerk, and office coordinator. Q: Is Excel really that important for jobs today? A: Yes — proficiency in Excel is one of the most listed requirements in admin and finance job ads. Q: Do I get certificates for each course? A: Yes — each course awards a certificate upon completion. Q: Is this good for remote or hybrid roles? A: Definitely — Excel and other MS tools are standard for remote workflows. Q: Will this help with entry-level finance jobs? A: Yes — it supports payroll, ledger, and compliance responsibilities. Q: Is this offer permanent? A: No — this bundle is part of a limited release and may be withdrawn soon.

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Course Overview This comprehensive course offers a deep dive into three essential technologies for data science: Python, JavaScript, and Microsoft SQL. Learners will gain foundational knowledge and practical skills in each of these key areas, which are crucial for handling data, creating interactive websites, and working with databases. By the end of the course, students will be proficient in writing Python code for data analysis, creating dynamic web content with JavaScript, and managing data with Microsoft SQL. The course is designed to equip learners with the technical skills needed to succeed in data science, making it a valuable investment for anyone looking to excel in this growing field. Course Description In this course, learners will explore the core principles of Python, JavaScript, and Microsoft SQL, all tailored to the needs of data science professionals. The curriculum covers Python’s data structures, functions, and libraries essential for data analysis, while JavaScript introduces students to web development skills, including client-side validation and data visualisation. The Microsoft SQL section focuses on data management, including filtering, joining, and structuring queries. Learners will develop a solid understanding of these technologies, which will enable them to manipulate data, automate processes, and design interactive applications. The course also includes real-world applications, ensuring learners are well-prepared for future opportunities in data science and web development. Course Modules: Module 01: JavaScript Getting Started Module 02: JavaScript Fundamentals Module 03: JavaScript Strings Module 04: JavaScript Operators Module 05: JavaScript Conditional Statements Module 06: JavaScript Control Flow Statements Module 07: JavaScript Functions Module 08: Data Visualization (Google Charts) Module 09: JavaScript Error Handling Module 10: JavaScript Client-Side Validations Module 11: Python Introduction Module 12: Python Basic Module 13: Python Strings Module 14: Python Operators Module 15: Python Data Structures Module 16: Python Conditional Statements Module 17: Python Control Flow Statements Module 18: Python Core Games Module 19: Python Functions Module 20: Python Args, KW Args for Data Science Module 21: Python Project Module 22: Publish Your Website for Live Module 23: MS SQL Statements Module 24: MS SQL Filtering Data Module 25: MS SQL Functions Module 26: MS SQL Joins Module 27: MS SQL Advanced Commands Module 28: MS SQL Structure and Keys Module 29: MS SQL Queries Module 30: MS SQL Structure Queries Module 31: MS SQL Constraints Module 32: MS SQL Backup and Restore (See full curriculum) Who is this course for? Individuals seeking to enhance their skills in data science. Professionals aiming to expand their knowledge in programming and database management. Beginners with an interest in Python, JavaScript, and SQL. Anyone looking to enter the field of data science or web development. Career Path Data Scientist Web Developer Database Administrator Data Analyst Front-End Developer Full Stack Developer Data Engineer

Duration 3 Days 18 CPD hours This course is intended for This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll: Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights. Develop the ability to transform raw text into a structured format that machines can understand and analyze. Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects. Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends. Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction. Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback. The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots. Launch into the Universe of Natural Language Processing The journey begins: Unravel the layers of NLP Navigating through the history of NLP Merging paths: Text Analytics and NLP Decoding language: Word Sense Disambiguation and Sentence Boundary Detection First steps towards an NLP Project Unleashing the Power of Feature Extraction Dive into the vast ocean of Data Types Purification process: Cleaning Text Data Excavating knowledge: Extracting features from Texts Drawing connections: Finding Text Similarity through Feature Extraction Engineer Your Text Classifier The new era of Machine Learning and Supervised Learning Architecting a Text Classifier Constructing efficient workflows: Building Pipelines for NLP Projects Ensuring continuity: Saving and Loading Models Master the Art of Web Scraping and API Usage Stepping into the digital world: Introduction to Web Scraping and APIs The great heist: Collecting Data by Scraping Web Pages Navigating through the maze of Semi-Structured Data Unearth Hidden Themes with Topic Modeling Embark on the path of Topic Discovery Decoding algorithms: Understanding Topic-Modeling Algorithms Dialing the right numbers: Key Input Parameters for LSA Topic Modeling Tackling complexity with Hierarchical Dirichlet Process (HDP) Delving Deep into Vector Representations The Geometry of Language: Introduction to Vectors in NLP Text Manipulation: Generation and Summarization Playing the creator: Generating Text with Markov Chains Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank Peering into the future: Recent Developments in Text Generation and Summarization Solving real-world problems: Addressing Challenges in Extractive Summarization Riding the Wave of Sentiment Analysis Unveiling emotions: Introduction to Sentiment Analysis Tools Demystifying the Textblob library Preparing the canvas: Understanding Data for Sentiment Analysis Training your own emotion detectors: Building Sentiment Models Optional: Capstone Project Apply the skills learned throughout the course. Define the problem and gather the data. Conduct exploratory data analysis for text data. Carry out preprocessing and feature extraction. Select and train a model. ? Evaluate the model and interpret the results. Bonus Chapter: Generative AI and NLP Introduction to Generative AI and its role in NLP. Overview of Generative Pretrained Transformer (GPT) models. Using GPT models for text generation and completion. Applying GPT models for improving autocomplete features. Use cases of GPT in question answering systems and chatbots. Bonus Chapter: Advanced Applications of NLP with GPT Fine-tuning GPT models for specific NLP tasks. Using GPT for sentiment analysis and text classification. Role of GPT in Named Entity Recognition (NER). Application of GPT in developing advanced chatbots. Ethics and limitations of GPT and generative AI technologies.

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