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Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Flash Sale 2024 II Discount II Bundle Course II Free CPD certificates

In this course, we will process massive streams of real-time data using Spark Streaming and create Spark applications using the Scala programming language (v2.12). We will also get our hands-on with some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models.

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

Embark on a journey to master Microsoft Excel with our 'Excel Essentials for Office Administrators' course. Designed to transform beginners into proficient users, this comprehensive course is divided into two main sections: Excel Beginner and Excel Intermediate. In the initial phase, learners are introduced to the Excel interface, where they explore and identify its various elements. This is followed by practical activities such as creating basic worksheets, using the help system, and crafting formulas. As the course progresses, participants will delve into more advanced features like manipulating data, applying styles and formats, and managing worksheets. The intermediate section elevates your skills further, covering a wide range of topics from applying range names and specialized functions to creating sophisticated charts and PivotTables. Learning Outcomes Gain proficiency in navigating and utilizing the Excel interface. Develop the ability to create and manage complex worksheets effectively. Master a variety of Excel functions and formulas for diverse data processing needs. Learn advanced data management techniques including sorting, filtering, and using PivotTables. Acquire skills in visual data presentation and customization of Excel workbooks. Why choose this Excel Essentials for Office Administrators course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Excel Essentials for Office Administrators course for? Office administrators and secretaries seeking to enhance their Excel skills. Professionals in various sectors requiring proficiency in Excel for data management. Students and individuals interested in learning Excel from a beginner to an intermediate level. Business owners needing to manage business data more efficiently. Anyone aiming to improve their productivity and data analysis skills through Excel. Career path Data Analyst: £25,000 - £50,000 Administrative Assistant: £18,000 - £30,000 Office Manager: £22,000 - £40,000 Business Analyst: £30,000 - £60,000 Financial Analyst: £28,000 - £55,000 Project Coordinator: £24,000 - £45,000 Prerequisites This Excel Essentials for Office Administrators does not require you to have any prior qualifications or experience. You can just enrol and start learning. This course was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Excel Beginner Identify the Elements of the Excel Interface 00:30:00 Activity - Identify the Elements of the Excel Interface 00:05:00 Create a Basic Worksheet 01:00:00 Activity - Create a Basic Worksheet 00:05:00 Use the Help System 00:30:00 Activity - Use the Help System 00:05:00 Create Formulas in a Worksheet 01:00:00 Activity - Create Formulas in a Worksheet 00:05:00 Insert Functions in a Worksheet 00:30:00 Activity - Insert Functions in a Worksheet 00:05:00 Reuse Formulas 00:30:00 Activity - Reuse Formulas 00:05:00 Manipulate Data 00:30:00 Activity - Manipulate Data 00:05:00 Insert, Manipulate, and Delete Cells, Columns, and Rows 00:30:00 Activity - Insert, Manipulate, and Delete Cells, Columns, and Rows 00:05:00 Search For and Replace Data 00:30:00 Activity - Search For and Replace Data 00:05:00 Spell Check a Worksheet 00:30:00 Activity - Spell Check a Worksheet 00:05:00 Modify Fonts 00:30:00 Activity - Modify Fonts 00:05:00 Add Borders and Colors to Cells 01:00:00 Activity - Add Borders and Colors to Cells 00:05:00 Apply Number Formats 00:30:00 Activity - Apply Number Formats 00:05:00 Align Cell Contents 00:30:00 Activity - Align Cell Contents 00:05:00 Apply Cell Styles 00:30:00 Activity - Apply Cell Styles 00:05:00 Define the Basic Page Layout for a Workbook 01:00:00 Activity - Define the Basic Page Layout for a Workbook 00:05:00 Refine the Page Layout and Apply Print Options 00:30:00 Activity - Refine the Page Layout and Apply Print Options 00:05:00 Format Worksheet Tabs 00:30:00 Activity - Format Worksheet Tabs 00:05:00 Manage Worksheets 00:30:00 Activity - Manage Worksheets 00:05:00 Manage the View of Worksheets and Workbooks 00:30:00 Activity - Manage the View of Worksheets and Workbooks 00:05:00 Customize General and Language Options 00:30:00 Activity - Customize General and Language Options 00:05:00 Customize Formula Options 00:30:00 Activity - Customize Formula Options 00:05:00 Customize Proofing and Save Options 01:00:00 Activity - Customize Proofing and Save Options 00:05:00 Customize the Ribbon and Quick Access Toolbar 00:30:00 Activity - Customize the Ribbon and Quick Access Toolbar 00:05:00 Customize the Functionality of Excel by Enabling Add-Ins 00:30:00 Activity - Customize the Functionality of Excel by Enabling Add-Ins 00:05:00 Customize Advanced and Trust Center Options 00:30:00 Activity - Customize Advanced and Trust Center Options 00:05:00 Activities and Exercise Files - Microsoft Excel 2016 for Beginners 00:00:00 Excel Intermediate Apply Range Names 00:30:00 Use Specialized Functions 00:30:00 Use Text Functions 00:30:00 Use Logical Functions 00:30:00 Use Lookup Functions 00:30:00 Use Date Functions 00:30:00 Use Financial Functions 00:30:00 Create and Modify Tables 00:30:00 Sort and Filter Data 00:30:00 Use Subtotal and Database Functions to Calculate Data 00:30:00 Create Charts 00:30:00 Modify and Format Charts 00:30:00 Create a Trendline 00:30:00 Create Advanced Charts 00:30:00 Create a PivotTable 00:30:00 Filter Data by Using Slicers 00:30:00 Analyze Data with PivotCharts 00:30:00 Insert and Modify Graphic Objects 00:30:00 Layer and Group Graphic Objects 00:30:00 Incorporate SmartArt 00:30:00 Customize Workbooks 00:30:00 Manage Themes 00:30:00 Create and Use Templates 00:30:00 Protect Files 00:30:00 Preparing a Workbook for Multiple Audiences 00:30:00 Activities and Exercise Files - Microsoft Excel 2016 Intermediate 00:00:00

Let's build sophisticated visualizations and dashboards using Sankey diagrams and geospatial, sunburst, and circular charts and animate your visualizations. We will also cover advanced Tableau topics, such as Tableau parameters and use cases and Level of Detail (LOD) expressions, spatial functions, advanced filters, and table calculations.

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

Learn essential techniques for successful trading & investing through Technical Analysis Masterclass. Explore key indicators, chart patterns, and strategies to decipher market trends effectively.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for information workers and data science professionals who seek to use database reporting and analysis tools such as Microsoft SQL Server Reporting Services, Excel, Power BI, R, SAS and other business intelligence tools, and wish to use TSQL queries to efficiently retrieve data sets from Microsoft SQL Server relational databases for use with these tools. Overview After completing this course, students will be able to: - Identify independent and dependent variables and measurement levels in their own analytical work scenarios. - Identify variables of interest in relational database tables. - Choose a data aggregation level and data set design appropriate for the intended analysis and tool. - Use TSQL SELECT queries to produce ready-to-use data sets for analysis in tools such as PowerBI, SQL Server Reporting Services, Excel, R, SAS, SPSS, and others. - Create stored procedures, views, and functions to modularize data retrieval code. This course is about writing TSQL queries for the purpose of database reporting, analysis, and business intelligence. 1 - INTRODUCTION TO TSQL FOR BUSINESS INTELLIGENCE Two Approaches to SQL Programming TSQL Data Retrieval in an Analytics / Business Intelligence Environment The Database Engine SQL Server Management Studio and the CarDeal Sample Database Identifying Variables in Tables SQL is a Declarative Language Introduction to the SELECT Query Lab 1: Introduction to TSQL for Business Intelligence 2 - TURNING TABLE COLUMNS INTO VARIABLES FOR ANALYSIS: SELECT LIST EXPRESSIONS, WHERE, AND ORDER BY Turning Columns into Variables for Analysis Column Expressions, Data Types, and Built-in Functions Column aliases Data type conversions Built-in Scalar Functions Table Aliases The WHERE clause ORDER BY Lab 1: Write queries 3 - COMBINING COLUMNS FROM MULTIPLE TABLES INTO A SINGLE DATASET: THE JOIN OPERATORS Primary Keys, Foreign Keys, and Joins Understanding Joins, Part 1: CROSS JOIN and the Full Cartesian Product Understanding Joins, Part 2: The INNER JOIN Understanding Joins, Part 3: The OUTER JOINS Understanding Joins, Part 4: Joining more than two tables Understanding Joins, Part 5: Combining INNER and OUTER JOINs Combining JOIN Operations with WHERE and ORDER BY Lab 1: Write SELECT queries 4 - CREATING AN APPROPRIATE AGGREGATION LEVEL USING GROUP BY Identifying required aggregation level and granularity Aggregate Functions GROUP BY HAVING Order of operations in SELECT queries Lab 1: Write queries 5 - SUBQUERIES, DERIVED TABLES AND COMMON TABLE EXPRESSIONS Non-correlated and correlated subqueries Derived tables Common table expressions Lab 1: Write queries 6 - ENCAPSULATING DATA RETRIEVAL LOGIC Views Table-valued functions Stored procedures Creating objects for read-access users Creating database accounts for analytical client tools Lab 1: Encapsulating Data Retrieval Logic 7 - GETTING YOUR DATASET TO THE CLIENT Connecting to SQL Server and Submitting Queries from Client Tools Connecting and running SELECT queries from: Excel PowerBI RStudio Exporting datasets to files using Results pane from SSMS The bcp utility The Import/Export Wizard Lab 1: Getting Your Dataset to the Client Additional course details: Nexus Humans 55232 Writing Analytical Queries for Business Intelligence training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the 55232 Writing Analytical Queries for Business Intelligence course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.