Search results “Excel for data analysis and visualization”

In this workshop, get an introduction to the latest analysis and visualization capabilities in Excel 2016. See how to import data from different sources, create mash/ups between data sources, and prepare the data for analysis. After preparing the data, learn about how business calculations - from simple to more advanced - can be expressed using DAX, how the result can be visualized and shared.

Views: 29675
Microsoft Power BI

Download workbook:
http://people.highline.edu/mgirvin/ExcelIsFun.htm
Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012:
Topics in Video:
1. What is Data Analysis? ( 00:53 min mark)
2. How Data Must Be Setup ( 02:53 min mark)
Sort:
3. Sort with 1 criteria ( 04:35 min mark)
4. Sort with 2 criteria or more ( 06:27 min mark)
5. Sort by color ( 10:01 min mark)
Filter:
6. Filter with 1 criteria ( 11:26 min mark)
7. Filter with 2 criteria or more ( 15:14 min mark)
8. Filter by color ( 16:28 min mark)
9. Filter Text, Numbers, Dates ( 16:50 min mark)
10. Filter by Partial Text ( 20:16 min mark)
Pivot Tables:
11. What is a PivotTable? ( 21:05 min mark)
12. Easy 3 step method, Cross Tabulation ( 23:07 min mark)
13. Change the calculation ( 26:52 min mark)
14. More than one calculation ( 28:45 min mark)
15. Value Field Settings (32:36 min mark)
16. Grouping Numbers ( 33:24 min mark)
17. Filter in a Pivot Table ( 35:45 min mark)
18. Slicers ( 37:09 min mark)
Charts:
19. Column Charts from Pivot Tables ( 38:37 min mark)
Formulas:
20. SUMIFS ( 42:17 min mark)
21. Data Analysis Formula or PivotTables? ( 45:11 min mark)
22. COUNTIF ( 46:12 min mark)
23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark)
Getting Data Into Excel
24. Import from CSV file ( 51:21 min mark)
25. Import from Access ( 54:00 min mark)
Highline Community College Professional Development Day 2012
Buy excelisfun products:
https://teespring.com/stores/excelisfun-store

Views: 1514410
ExcelIsFun

This video is part of the Analyzing and Visualizing Data with Excel course available on EdX.
To sign up for the course, visit: http://aka.ms/edxexcelbi

Views: 400211
DAT206x

Learn how to stop wasting time in cleaning up data. Invest that time in understanding the hidden information and grow faster than you ever imagined. Create stunning charts in seconds. All this using the familiar Excel, along with the new Power BI toolkit (Power Query, Power Map).
Dr. Nitin Paranjape (Office MVP)

Views: 3867
Efficiency 365

Business Analytics with excel training has been designed to help initiate you to the world of analytics. For this we use the most commonly used analytics tool i.e. Microsoft Excel. The training will equip you with all the concepts and hard skills required to kick start your analytics career. If you already have some experience in the IT or any core industry, this course will quickly teach you how to understand data and take data driven decisions relative to your domain using Microsoft excel.
Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Data-Excel-W3vrMSah3rc&utm_medium=SC&utm_source=youtube
For a new-comer to the analytics field, this course provides the best required foundation. The training also delves into statistical concepts which are important to derive the best insights from available data and to present the same using executive level dashboards. Finally we introduce Power BI, which is the latest and the best tool provided by Microsoft for analytics and data visualization.
What are the course objectives?
This course will enable you to:
1. Gain a foundational understanding of business analytics
2. Install R, R-studio, and workspace setup. You will also learn about the various R packages
3. Master the R programming and understand how various statements are executed in R
4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R
5. Define, understand and use the various apply functions and DPLYP functions
6. Understand and use the various graphics in R for data visualization
7. Gain a basic understanding of the various statistical concepts
8. Understand and use hypothesis testing method to drive business decisions
9. Understand and use linear, non-linear regression models, and classification techniques for data analysis
10. Learn and use the various association rules and Apriori algorithm
11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering
Who should take this course?
There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals:
IT professionals looking for a career switch into data science and analytics
Software developers looking for a career switch into data science and analytics
Professionals working in data and business analytics
Graduates looking to build a career in analytics and data science
Anyone with a genuine interest in the data science field
Experienced professionals who would like to harness data science in their fields
Who should take this course?
There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
4. Graduates looking to build a career in analytics and data science
5. Anyone with a genuine interest in the data science field
6. Experienced professionals who would like to harness data science in their fields
For more updates on courses and tips follow us on:
- Facebook : https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
Get the android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Views: 23059
Simplilearn

We're constantly amazed at what customers do with charts in Excel. Come to learn more about tips and tricks for commonly requested visualizations scenarios in Excel.

Views: 270525
Microsoft Power BI

WATCH PART 2: https://www.youtube.com/watch?v=g530cnFfk8Y
Download file used in the video: http://www.excelcampus.com/pivot-table-checklist-yt
In this video series you will learn how to create an interactive dashboard using Pivot Tables and Pivot Charts.
Works with Excel 2003, 2007, 2010, 2013 for Windows & Excel 2011 for Mac
Don't worry if you have never created a Pivot Table before, I cover the basics of formatting your source data and creating your first Pivot Table as well. You will also get to see an add-in I developed named PivotPal that makes it easier to work with some aspects of Pivot Tables.
Download the files to follow along at the following link.
http://www.excelcampus.com/pivot-table-checklist-yt
I have another video that shows how to reformat the pivot chart in Excel 2010. In the video above I'm using Excel 2013 and the menus are different from Excel 2007/2010. Here is the link to that video. http://www.youtube.com/watch?v=Jt_QqG-vRRw
Get PivotPal: http://www.excelcampus.com/pivotpal
Free webinar on The 5 Secrets to Understanding Pivot Tables: https://www.excelcampus.com/pivot-webinar-yt
Subscribe to my free newsletter: http://www.excelcampus.com/newsletter

Views: 6430550
Excel Campus - Jon

Lecture Starts at: 8:25
Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation where many of the tools in the analyst's tool belt (e.g., regression) aren't ideal for the task. Despite these challenges, proper analysis of business data represents a fundamental skill required of Business/Data Analysts, Product/Program Managers, and Data Scientists. At this meetup presenter Dave Langer will show how to get started analyzing business data in a robust way using Excel – no programming or statistics required!
Dave will cover the following during the presentation:
• The types of business data and why business data is a unique analytical challenge.
• Requirements for robust business data analysis.
• Using histograms, running records, and process behavior charts to analyze business data.
• The rules of trend analysis.
• How to properly compare business data across time, organizations, geographies, etc.Where you can learn more about the tools and techniques.
*Excel spreadsheets can be found here:
https://github.com/datasciencedojo/meetup/tree/master/business_data_analysis_with_excel
**Find out more about David here:
https://www.meetup.com/data-science-dojo/events/236198327/
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https://hubs.ly/H0f8xWx0
See what our past attendees are saying here:
https://hubs.ly/H0f8xGd0
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Like Us: https://www.facebook.com/datasciencedojo/
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Also find us on:
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Instagram: https://www.instagram.com/data_science_dojo/
Vimeo: https://vimeo.com/datasciencedojo

Views: 46036
Data Science Dojo

Data Analysis and Visualization is a Richfull series Directed to all students interested in Analysing and Visualizing Data using Excel, MATLAB and Wolfram Mathematica. This Course has been made by an expert prophesors in University of Wistern Australia, and Contains the main folowing Topics:
1 Data Visualization in Excel
2 Array Formula in Excel
3 2D Array Formula in Excel
4 Excel Macros
5 Why Matlab
6 Problem Solving in MATLAB
7 MATLAB Orientation - Data Types and Expressions
8 MATLAB Scripts and Functions, Storing Instructions in Files, Getting Help on Build-in Functions
9 Matrices in MATLAB
10 MATLAB Scripts and Functions
11 Random Numbers, Gaussian Random Numbers, Complex Numbers
12 An Examples of Script and Function Files
13 Control Flow, Flow Chart, Relational Operators, Logical Operators, Truth Table, if clause, elseif, Nested if statments, Switch Structure,
14 MATLAB Loops, Nested Loops, Repetition, while, For,
15 Problems with Scripts, Workspace, Why Functions, How to Write a MATLAB Function, Anonymous Functions,
16 MATLAB Programs Input / Output, Escape Characters, Formatted Output, Syntax of Conversion Sequence,
17 Defensive Programming, error, warning, msg, isnumeric, ischar, nargin, nargout, nargchk, narginchk, all,
18 Cell Arrays, Array Types to Store data, Normal Arrays, Curly Brackets, Round Brackets,
19 MATLAB Structures, What is a Structure?, Adding a Field to a Structure, Struct Function, Manipulate the Fields, Preallocate Memory for a Structure Array
20 Basic 2D Plotting, title, xlabel, ylabel, grid, plot
21 Multiple Plots, figure, hold on, off, legend Function, String, Axis Scaling, Subplot,
22 Types of 2D Plots, Polar Plot, Logarithmic Plot, Bar Graphs, Pie Charts, Histograms, X-Y Graphs with 2 y Axes, Function Plots,
23 3D Plot, Line Plot, Surface Plot, Contour plots, Cylinder Plots, mesh, surf, contour, meshgrid,
24 Parametric Surfaces, Earth, Triangular Prism, Generating Points, Default Shading, Shading Flat, Shading Interp,
25 Arrays vs. Matrix Operations,
26 Dot Products, Example Calculating Center of Mass, Center of Gravity,
27 Matrix Multiplication and Division, Matrix Powers, Matrix Inverse, Determinatnts, Cross Products,
28 Applications of Matrix Operations, Solving Linear Equations, Linear Transformations, Eigenvectors
29 Engineering Application of Solving Systems of Linear Equations, Systems of Linear Equations, Kirchhoff's Circuit Laws,
30 Symbolic Differentiation, sym, syms, diff
31 Numerical Differentiation, fplot, Forward Difference, Backward Difference, Central Difference,
32 Numerical Integration, Engineering Applications, Integration, Trapezoid Rule, Simpson's Rule,
33 Monte Carlo Integration,
34 Introduction to ODE in System Biology
35 Introduction to System Biology, Gene Circuits,
36 Solving ODEs in Matlab, Repressilator, Programming steps
37 Interpolation, Cubic Spline Interpolation, Nearest Neighbor, Cubic, Two Dimensional, Three Dimensional,
38 Curve Fitting, Empirical Modelling, Linear Regression, Polynomial Regression, polyfit, polyval, Least Squres,
39 Introduction to Mathematica,
40 Programming in Mathematica
41 Basic Function in Mathematica, Strings, Characters, Polynomials, Solving Equations, Trigonometry, Calculus, 2D Ploting, Interactive Plots, Functions, Matlab vs. MAthematica
42 Numerical Data, Arthematic Operators, Data Types, Lists, Vectors, Matrices, String, Characters,
43 Mathematica Rule Based Programming, Functional Programming,
44 MAthematica Procedural Programming, Procedural Programs, Conditionals and Compositions, Looping Constructs, Errors, Modules,
45 Mathematica Predicates in Rule Based Programming, Patterns and Rules, Rules and Lists, Predicates, Blank, Blanksequence, BlackNullSequence, Number Puzzle,
46 Symbolic Mathematics and Programming, Rule Based Computation, Simplify, Expand, Solve, NSolve, Symbolic Visualisation,
47 Symbolic Computing in Matlab, Symbolic Algebra, sym, syms, Equations, Expressions, Systems of Equations, Calculus,

Views: 393
TO Courses

This webinar highlights how MATLAB can work with Excel.
Get a Free MATLAB Trial: https://goo.gl/C2Y9A5
Ready to Buy: https://goo.gl/vsIeA5
Learn MATLAB for Free: https://goo.gl/xIiHyG
Many technical professionals find that they run into limitations using Excel for their data analysis applications. This webinar highlights how MATLAB can supplement the capabilities of Excel by providing access to thousands of pre-built engineering and advanced analysis functions and versatile visualization tools.
Learn more about using MATLAB with Excel: http://goo.gl/3vkFMW
Learn more about MATLAB: http://goo.gl/YKadxi
Through product demonstrations you will see how to:
• Access data from spreadsheets
• Plot data and customize figures
• Perform statistical analysis and fitting
• Automatically generate reports to document your analysis
• Freely distribute your MATLAB functions as Excel add-ins
This webinar will show new features from the latest versions of MATLAB including new data types to store and manage data commonly found in spreadsheets.
Previous knowledge of MATLAB is not required.
About the Presenter: Adam Filion holds a BS and MS in Aerospace Engineering from Virginia Tech. His research involved nonlinear controls of spacecraft and periodic orbits in the three-body problem. After graduating he joined the MathWorks Engineering Development Group in 2010 and moved to Applications Engineering in 2012.

Views: 232327
MATLAB

Download File: http://people.highline.edu/mgirvin/excelisfun.htm
See how to use Power BI Desktop to import, clean and transform Sales Tables from Multiple Excel Files and consolidate into a Single Proper Data Set that can be linked in a Relationship to other tables, and then build DAX Calculated Columns & Measures for Gross Profit that can be used in a Dynamic Dashboard with Map, Column Chart, Line Chart, Card and Slicer visualizations. During the whole process we will compare and contrast how the process is similar and different from Excel’s Power Query and Power Pivot DAX.
The steps we will see in this video are:
1. (00:17) Introduction to entire process for Power BI Desktop, including looking at the finished Dashboard
2. (04:50) Import Multiple Excel Files From Folder
3. (05:44) Name Query
4. (06:02) Transform extension column to lowercase
5. (06:34) Filter Files to only include “.xlsx” file extensions
6. (07:05) Remove Columns
7. (07:18) November 2016 Power Query Update Problem
8. (08:05) Add Custom Column with Excel.Workbook Function to extract the Excel Objects from each File.
9. (09:40) Delete Content Column
10. (10:41) Filter to only include Excel Sheet Objects
11. (11:06) Filter to exclude sheets that contain the word “Sheet”
12. (11:40) Remove Columns
13. (11:51) Expand Data and Sheet Name Columns
14. (12:06) Change Field Names
15. (12:22) Change Data Types
16. (14:05) Add Custom Column to calculate Net Revenue Column then round Number.Round function. Then Add Fixed Decimal Data Type.
17. (15:59) Remove columns for Amount and Revenue Discount
18. (16:10) Close and Apply to add to Data Model
19. (17:05) Import Excel Manager Table. Change Data Types to Text. Close and Apply
20. (18:10) Create Relationship between Zip Code Columns
21. (19:03) Create DAX Calculated Column with the IF Function to Categorize Retail Data. Change Data Type.
22. (21:53) Create DAX Measures for: Total Revenue, Total COGS and Gross Profit. Add Currency Number Formatting with No Decimals Showing.
23. (24:28) Create DAX Measures for: Gross Profit Percentage. Add Percentage Number Formatting with Two Decimals Showing.
24. (25:35) Create Map Visualization for Zip Code & Gross Profit Data (Zip Code with relationship to Managers)
25. (26:20) Create Clustered Bar for Manager Names & Gross Profit Data (Zip Code with relationship to Managers)
26. (27:15) Create Clustered Column for Product & Gross Profit Data, with a Line Chart for Gross Profit Percentage
27. (28:19) Create Clustered Column for Payment Method & Gross Profit Data, with a Line Chart for Gross Profit Percentage
28. (28:45) Create Slicer for States.
29. (29:00) Create Card Visualization for Total Revenue, Total COGS, Gross Profit and Gross Profit Percentage.
30. (29:57) Summary
Learn Power BI Desktop Basics. Introduction to Power BI Desktop. Getting Started with Power BI Desktop. Create Impactful Reports With Power BI Desktop. Microsoft Power BI.

Views: 121745
ExcelIsFun

This video is part of the Analyzing and Visualizing Data with Excel course available on EdX.
To sign up for the course, visit: http://aka.ms/edxexcelbi

Views: 48604
DAT206x

In this 1 hour Webinar, Dr. Nitin Paranjape (Office MVP) will show you how to structure and analyze large amount of data in just a few seconds using Excel 2013's Power BI features: Pivot Table, Power Pivot, Power Query and Power View.
Topics covered:
- Purpose of data analysis (2:30)
- Data Analysis process (4:06)
- Good data vs Bad data (5:34)
- Rules for Good data (9:05)
- Common Examples of badly formatted data (10:41)
- How to handle cross-tab data with Power Query (14:44)
- Gather data from the web with (18:57)
- Power Query's Data Catalogue Search (20:00)
- Split Column by Delimiter (20:05)
- Refresh live data in Power Query (23:20)
- Summarize Data in Pivot Tables (24:26)
- Do Calculation right inside Pivot Tables with Calculated Fields (27:33)
- Avoid Wrong calculations with Get Pivot Data (29:10)
- Data Visualization with Conditional Formatting & Quick Analysis (31:12)
- Limitations of Pivot Table (34:38)
- Why use Power Pivot (35:19)
- Analyze 43 million rows of data within Power Pivot (36:40)
- Build interactive dashboards with Power View (41:42)
- Share Power View reports on Sharepoint (47:15)
- Manage viewing rights with Excel's Browser View option
- Create 3D map reports in Power Map (49:38)
- Power BI Preview (55:20)
- Summary: Which tool to use in which scenario? (59:03)
- Notes to Developers (59:17)
This is a recording of the Data Analytics Webinar for Microsoft, powered by Economic Times. The webinar was conducted by Dr. Nitin Paranjape, Office MVP and Microsoft Regional Director.

Views: 13687
Efficiency 365

Take this course for free on edX: https://www.edx.org/course/excel-data-analysis-visualization-microsoft-dat206x
↓ More info below. ↓
Follow on Facebook: https://www.facebook.com/edX
Follow on Twitter: https://www.twitter.com/edxonline
Follow on YouTube: https://www.youtube.com/user/edxonline
About this course
Microsoft Excel is one of the most widely used solutions for analyzing and visualizing data. Beginning with Excel 2010, new tools were introduced to enable the analysis of more data, resulting in less time spent creating and maintaining the solutions and enabling a better understanding of what the data means. This better understanding is facilitated by improved visualizations and more sophisticated business logics.
Do you want to take your advanced Excel skills to the next level? Are you exploring new ways to get and transform your data and create visualization?
Check out this practical new course, taught in short, lecture-based videos, complete with demos, quizzes, and hands-on labs, and skill up on many of the built-in business intelligence (BI) tools and features in Excel. Learn how to present the most relevant data with dynamic reports and presentations, as expert Dany Hoter walks you through all the important details.
In this course, get an introduction to the latest versions of these new tools in Excel 2016. See how to import data from different sources, create mashups between data sources, and prepare the data for analysis. After preparing the data, learn about how business calculations—from simple to more advanced—can be expressed using the DAX calculation engine. And see how these different technologies work together inside Excel. Learn how the data can be visualized and shared to the Power BI cloud service, after which it can be used in dashboards, queried using plain English sentences, and consumed on mobile devices.

Views: 13408
edX

Learn how MATLAB can supplement the capabilities of Microsoft Excel.
Get a Free Trial: https://goo.gl/C2Y9A5
Get Pricing Info: https://goo.gl/kDvGHt
Ready to Buy: https://goo.gl/vsIeA5
Many technical professionals find that they run into limitations using Excel for their data analysis applications. This webinar will show you how MATLAB can supplement the capabilities of Microsoft Excel by providing access to thousands of pre-built mathematical and advanced analysis functions, versatile visualization tools, and the ability to automate your analysis workflows.
With MATLAB, you can efficiently explore, analyze, and visualize your data. Through product demonstrations, you will see how to:
-Access data from files and Excel spreadsheets
-Visualize data and customize figures
-Perform statistical analysis and fitting
-Generate reports and automate workflows
-Share analysis tools as standalone applications or Excel add-ins
This session is intended for people who are new to MATLAB. Experienced users may also benefit from the session, as the engineer will be showing capabilities from recent releases of MATLAB including the new ways to store and manage data commonly found in spreadsheets.

Views: 55995
MATLAB

Python data analysis / data science tutorial. Let’s go!
For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata
Sample data and sample code: https://www.csdojo.io/data
My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8
Also, keep in touch on Twitter: https://twitter.com/ykdojo
And Facebook: https://www.facebook.com/entercsdojo
Outline - check the comment section for a clickable version:
0:37: Why data visualization?
1:05: Why Python?
1:39: Why Matplotlib?
2:23: Installing Jupyter through Anaconda
3:20: Launching Jupyter
3:41: DEMO begins: create a folder and download data
4:27: Create a new Jupyter Notebook file
5:09: Importing libraries
6:04: Simple examples of how to use Matplotlib / Pyplot
7:21: Plotting multiple lines
8:46: Importing data from a CSV file
10:46: Plotting data you’ve imported
13:19: Using a third argument in the plot() function
13:42: A real analysis with a real data set - loading data
14:49: Isolating the data for the U.S. and China
16:29: Plotting US and China’s population growth
18:22: Comparing relative growths instead of the absolute amount
21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata

Views: 174485
CS Dojo

When your data is available partially in Excel and Partially in Access we can bring it All together, then create calculations and functions using the data from multiple sources. We can then Export our data back to Excel and perform our analysis by creating nice looking impressive Charts in Excel.
You can download the exercise files and follow along by going to my website www.OfficeInstructor.ca and click on "Downloads" then select the exercise file to download.
To be notified when new videos are published, make sure you subscribe to this channel... Your comments are always appreciated.

Views: 749
Officeinstructor

Shows how to visualize climate data in Excel using EPW climate files.
This tutorial is intended for use with my class on Zero Energy Building. See playlist for details.

Views: 698
Brendon Levitt

Explore the art of data visualization with Excel. This webinar covers the following topics:
Types of Charts
Speedometer Charts
Thermometer Charts
Combinational Charts
Usage of Tables to make dynamically updated Charts
Learn more at: http://www.edureka.co/advanced-ms-excel

Views: 2731
edureka!

Watch the full HD course: http://pluralsight.com/training/courses/TableOfContents?courseName=sql-big-data-convergence-big-picture
It can be tough to get a detailed view of big data because, well it's really big data. In this video excerpt from Andrew Brust's SQL Big Data Convergence - The Big Picture course, you'll see how to use Excel as a visualization tool on data stored in Apache's Hive Hadoop implementation. In the full course Andrew covers other implementations such as Cloudera Impala, Hadapt, Rainstor, and Terradata. He also covers the benefits of big data and what it can mean for developers.
-~-~~-~~~-~~-~-
Push your limits. Expand your potential. Smarter than yesterday-
https://www.youtube.com/watch?v=k2s77i9zTek
-~-~~-~~~-~~-~-

Views: 5092
Pluralsight

Learn the fundamentals of data analytics with DataSeer!
The explosion of business data has expanded opportunities for the use of data in business. Unfortunately, the vast majority of firms have neither the knowledge nor skills to fully exploit their information assets.
The first step is to begin with the fundamentals of data analytics. In this course, we start with core analytics skills in Microsoft Excel. In this tool, you will learn how to organize, analyze and interpret analytics using real business datasets. You will also learn how to become an Excel "Data Ninja" - I.e. One who can manipulate business datasets and create insights at speed using pivot tables and Excel functions.
In practice, most of the value in business data is derived by asking simple questions (hypotheses) that can be answered using basic data manipulation and common metrics (e.g. averages, totals, counts and percentages) in tools such as Excel. In this brand new course, we equip participants with the skills needed to develop such hypotheses, gather data to inform these hypotheses and finally present actionable results and conclusions.
About Your Lead Instructor: Mr Jay Manahan - Data Storytelling Expert
Your instructor, Mr Manahan, is a data visualization and data analysis thought leader. He was a winner of the 2017 Grab Data Visualization challenge and has trained over 1000 people in foundational and advanced data skills. He has delivered private training courses to companies including JP Morgan, Thomson Reuters and Pru Life UK. A widely sought-after trainer, Jay holds an MBA and BS in Mathematics from Ateneo de Manila University.

Views: 1166
DataSeer

Take this course for free on edX: https://www.edx.org/course/excel-data-analysis-visualization-microsoft-dat206x
↓ More info below. ↓
Follow on Facebook: https://www.facebook.com/edX
Follow on Twitter: https://www.twitter.com/edxonline
Follow on YouTube: https://www.youtube.com/user/edxonline
About this course
Microsoft Excel is one of the most widely used solutions for analyzing and visualizing data. Beginning with Excel 2010, new tools were introduced to enable the analysis of more data, resulting in less time spent creating and maintaining the solutions and enabling a better understanding of what the data means. This better understanding is facilitated by improved visualizations and more sophisticated business logics.
Do you want to take your advanced Excel skills to the next level? Are you exploring new ways to get and transform your data and create visualization?
Check out this practical new course, taught in short, lecture-based videos, complete with demos, quizzes, and hands-on labs, and skill up on many of the built-in business intelligence (BI) tools and features in Excel. Learn how to present the most relevant data with dynamic reports and presentations, as expert Dany Hoter walks you through all the important details.
In this course, get an introduction to the latest versions of these new tools in Excel 2016. See how to import data from different sources, create mashups between data sources, and prepare the data for analysis. After preparing the data, learn about how business calculations—from simple to more advanced—can be expressed using the DAX calculation engine. And see how these different technologies work together inside Excel. Learn how the data can be visualized and shared to the Power BI cloud service, after which it can be used in dashboards, queried using plain English sentences, and consumed on mobile devices.

Views: 5352
edX

Track and highlight important data trends with Sparklines. With new data analysis and visualization tools Sparklines are mini-charts placed inside cells so that you can view the data and the chart on the same table. http://www.trainsignal.com/Office-2010-Training-Package.aspx?utm_source=YouTube&utm_medium=Social%20Media&utm_campaign=YouTubeOffice%20Referral&utm_content=excel%202010

Views: 1974
TrainSignal is now Pluralsight

Learn how Visualize to Visualize Data in Excel 2016 with these 5 amazing Visualization Tips. In this tutorial our Excel instructor will have you teach you how to use Microsoft Excel 2016 to visualize your data using excel charts.
Please subscribe to your Youtube Channel today!

Views: 3381
Learn iT! Training

Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn the basic Guidelines for Visualizing Tables & Charts:
1) (00:13) Topics for video
2) (00:41) When to visualize with a Chart or a Table
3) (02:35): Effective Data Visualization makes our analysis easier for others to “see” and understand
4) (04:05) Edward R. Tufte: Data Ink Ration Rule and “No Chart Junk” Rule
5) (04:49) Data Visualization Golden Rule: No Extraneous Elements in Table or Chart
6) (07:25) Tables Vs. Charts: Tables show Exact Values and allow Exact Comparisons, Charts show Relative Comparisons
7) (08:16) Tables are good when the units or magnitudes are different for the numbers
8) (08:47) Table Design Principles
9) (12:30) Example 1 for implementing Table Design Principles for small table with “less than minimal formatting”
10) (12:45) Remove all formatting with “Eraser” button: Home Ribbon Tab, Edit group, Clear Formats. Keyboard: Alt, H, E, F or Alt, E, A, F
11) (13:48) Borders and Fill and Font Color
12) (14:02) Number Formatting for currency when the unit is implied in Field Name (Header)
13) (14:30) How to present Percentages without Percent Number Format: Times 100 and then Paste Special Values.
14) (16:51) Example 2 for implementing Table Design Principles for small table with “minimal formatting”
15) (17:35) Example 2 for implementing Table Design Principles for big table, where we shade every other column
16) (18:09) Custom Number Formatting for showing numbers in millions
17) (21:27) Overview of Charts
18) (23:12) Terms that Excel Charts use for numbers and categories. Numbers = Series. Categories = labels or criteria.
19) (24:12) Knowing when to use and how to create Column and Bar Charts
20) (27:06) Knowing when to use and how to create “Stacked Column or Bar Chart” and “Clustered column or Bar Chart”
21) (29:55) Line Charts
22) (31:00) Custom Number Formatting for showing numbers in thousands
23) (32:53) Look at Select Dialog Box to change range of cells that Chart points to for numbers and category labels.
24) (33:21) X-Y Scatter Charts
25) (35:15) Bubble Charts
26) (37:39) Conditional Formatting to create heat Map
27) (38:42) Using Excel 2016, see a basic Geographical Information System example that involves taking zip code and population data and plotting it on a map using the 3-D Mapping tool
28) (41:00) Summary and Conclusion
Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.

Views: 21598
ExcelIsFun

Data Analysis and Visualization is a Richfull series Directed to all students interested in Analysing and Visualizing Data using Excel, MATLAB and Wolfram Mathematica. This Course has been made by an expert prophesors in University of Wistern Australia, and Contains the main folowing Topics:
1 Data Visualization in Excel
2 Array Formula in Excel
3 2D Array Formula in Excel
4 Excel Macros
5 Why Matlab
6 Problem Solving in MATLAB
7 MATLAB Orientation - Data Types and Expressions
8 MATLAB Scripts and Functions, Storing Instructions in Files, Getting Help on Build-in Functions
9 Matrices in MATLAB
10 MATLAB Scripts and Functions
11 Random Numbers, Gaussian Random Numbers, Complex Numbers
12 An Examples of Script and Function Files
13 Control Flow, Flow Chart, Relational Operators, Logical Operators, Truth Table, if clause, elseif, Nested if statments, Switch Structure,
14 MATLAB Loops, Nested Loops, Repetition, while, For,
15 Problems with Scripts, Workspace, Why Functions, How to Write a MATLAB Function, Anonymous Functions,
16 MATLAB Programs Input / Output, Escape Characters, Formatted Output, Syntax of Conversion Sequence,
17 Defensive Programming, error, warning, msg, isnumeric, ischar, nargin, nargout, nargchk, narginchk, all,
18 Cell Arrays, Array Types to Store data, Normal Arrays, Curly Brackets, Round Brackets,
19 MATLAB Structures, What is a Structure?, Adding a Field to a Structure, Struct Function, Manipulate the Fields, Preallocate Memory for a Structure Array
20 Basic 2D Plotting, title, xlabel, ylabel, grid, plot
21 Multiple Plots, figure, hold on, off, legend Function, String, Axis Scaling, Subplot,
22 Types of 2D Plots, Polar Plot, Logarithmic Plot, Bar Graphs, Pie Charts, Histograms, X-Y Graphs with 2 y Axes, Function Plots,
23 3D Plot, Line Plot, Surface Plot, Contour plots, Cylinder Plots, mesh, surf, contour, meshgrid,
24 Parametric Surfaces, Earth, Triangular Prism, Generating Points, Default Shading, Shading Flat, Shading Interp,
25 Arrays vs. Matrix Operations,
26 Dot Products, Example Calculating Center of Mass, Center of Gravity,
27 Matrix Multiplication and Division, Matrix Powers, Matrix Inverse, Determinatnts, Cross Products,
28 Applications of Matrix Operations, Solving Linear Equations, Linear Transformations, Eigenvectors
29 Engineering Application of Solving Systems of Linear Equations, Systems of Linear Equations, Kirchhoff's Circuit Laws,
30 Symbolic Differentiation, sym, syms, diff
31 Numerical Differentiation, fplot, Forward Difference, Backward Difference, Central Difference,
32 Numerical Integration, Engineering Applications, Integration, Trapezoid Rule, Simpson's Rule,
33 Monte Carlo Integration,
34 Introduction to ODE in System Biology
35 Introduction to System Biology, Gene Circuits,
36 Solving ODEs in Matlab, Repressilator, Programming steps
37 Interpolation, Cubic Spline Interpolation, Nearest Neighbor, Cubic, Two Dimensional, Three Dimensional,
38 Curve Fitting, Empirical Modelling, Linear Regression, Polynomial Regression, polyfit, polyval, Least Squres,
39 Introduction to Mathematica,
40 Programming in Mathematica
41 Basic Function in Mathematica, Strings, Characters, Polynomials, Solving Equations, Trigonometry, Calculus, 2D Ploting, Interactive Plots, Functions, Matlab vs. MAthematica
42 Numerical Data, Arthematic Operators, Data Types, Lists, Vectors, Matrices, String, Characters,
43 Mathematica Rule Based Programming, Functional Programming,
44 MAthematica Procedural Programming, Procedural Programs, Conditionals and Compositions, Looping Constructs, Errors, Modules,
45 Mathematica Predicates in Rule Based Programming, Patterns and Rules, Rules and Lists, Predicates, Blank, Blanksequence, BlackNullSequence, Number Puzzle,
46 Symbolic Mathematics and Programming, Rule Based Computation, Simplify, Expand, Solve, NSolve, Symbolic Visualisation,
47 Symbolic Computing in Matlab, Symbolic Algebra, sym, syms, Equations, Expressions, Systems of Equations, Calculus,

Views: 49
TO Courses

Learn the basics of Excel, one of the most popular data analysis tools, to help visualize and gain insights from your data.
Take this course free on edX: https://www.edx.org/course/introduction-data-analysis-using-excel-microsoft-dat205x#!
ABOUT THIS COURSE
The ability to analyze data is a powerful skill that helps you make better decisions. Microsoft Excel is one of the top tools for data analysis and the built-in pivot tables are arguably the most popular analytic tool.
In this course, you will learn how to perform data analysis using Excel’s most popular features. You will learn how to create pivot tables from a range with rows and columns in Excel. You will see the power of Excel pivots in action and their ability to summarize data in flexible ways, enabling quick exploration of data and producing valuable insights from the accumulated data.
Pivots are used in many different industries by millions of users who share the goal of reporting the performance of companies and organizations. In addition, Excel formulas can be used to aggregate data to create meaningful reports. To complement, pivot charts and slicers can be used together to visualize data and create easy to use dashboards.
You don’t need any previous knowledge of Excel to take this course and you are welcome to use any supported version of Excel you have installed in your computer.
After taking this course you’ll be ready to continue to our more advanced Excel course, Analyzing and Visualizing Data with Excel.
WHAT YOU'LL LEARN
- Create flexible data aggregations using pivot tables
- Represent data visually using pivot charts
- Calculate margins and other common ratios using calculation on pivot table
- Filter data using slicers in multiple pivot tables
- Create aggregate reports using formula based techniques

Views: 15556
edX

Here I have shown the high-level view on how to visualize the excel data in Tableau.
Tableau Public 10.2
Data - Hollywood most profitable Movie

Views: 13882
visualizer bi

Join this session for an informative introduction to the latest visualization capabilities in Excel 2016. Learn how the result can be visualized and shared.
Follow us on Twitter - https://twitter.com/mspowerbi
More questions? Try asking the Power BI Community @ https://community.powerbi.com/

Views: 4276
Microsoft Power BI

In this video I show how to pull data from MySQL using MS Query and then explain best how to visualize the data. Oh, and you'll see a cool new Excel 2013 feature as well.

Views: 41741
Michael Herman

This is an introduction to data analysis using MATLAB. This will be particularly useful for people interested in analysis of data for research purposes. We will see data import, visualization, statistics, writing data, reading data, basic simulation, image analysis...and so on.
P.S: MATLAB is easy to learn and start with for those who are new to programming.
For programs, write me at [email protected]

Views: 837
Utpal Kumar

When you analyze your Excel data using cluster analysis, it often helps to visualize the clusters using an XY scatter chart. Find out how to visualize table data in a chart in this tutorial. Watch more at http://www.lynda.com/Excel-tutorials/Up-Running-Excel-Cluster-Analysis/165438-2.html?utm_campaign=55QCaggZ9Lw&utm_medium=social&utm_source=youtube-earned.
This tutorial is a single movie from the Up and Running with Excel Cluster Analysis course by lynda.com author Curt Frye. The complete course is 1 hour and 10 minutes and shows how to perform cluster analysis using Excel.
Connect with lynda.com:
Facebook: http://bit.ly/fbldc
Twitter: http://bit.ly/ldctw
Google Plus: http://bit.ly/gplusldc
LinkedIn: http://bit.ly/linkldc

Views: 5716
LinkedIn Learning

Data Analysis and Visualization is a Rich-full series Directed to all students interested in Analyzing and Visualizing Data using Excel, MATLAB and Wolfram Mathematica. This Course has been made by an expert prophesiers in University of Western Australia, and Contains the main flowing Topics:
1 Data Visualization in Excel
2 Array Formula in Excel
3 2D Array Formula in Excel
4 Excel Macros
5 Why Matlab
6 Problem Solving in MATLAB
7 MATLAB Orientation - Data Types and Expressions
8 MATLAB Scripts and Functions, Storing Instructions in Files, Getting Help on Build-in Functions
9 Matrices in MATLAB
10 MATLAB Scripts and Functions
11 Random Numbers, Gaussian Random Numbers, Complex Numbers
12 An Examples of Script and Function Files
13 Control Flow, Flow Chart, Relational Operators, Logical Operators, Truth Table, if clause, elseif, Nested if statments, Switch Structure,
14 MATLAB Loops, Nested Loops, Repetition, while, For,
15 Problems with Scripts, Workspace, Why Functions, How to Write a MATLAB Function, Anonymous Functions,
16 MATLAB Programs Input / Output, Escape Characters, Formatted Output, Syntax of Conversion Sequence,
17 Defensive Programming, error, warning, msg, isnumeric, ischar, nargin, nargout, nargchk, narginchk, all,
18 Cell Arrays, Array Types to Store data, Normal Arrays, Curly Brackets, Round Brackets,
19 MATLAB Structures, What is a Structure?, Adding a Field to a Structure, Struct Function, Manipulate the Fields, Preallocate Memory for a Structure Array
20 Basic 2D Plotting, title, xlabel, ylabel, grid, plot
21 Multiple Plots, figure, hold on, off, legend Function, String, Axis Scaling, Subplot,
22 Types of 2D Plots, Polar Plot, Logarithmic Plot, Bar Graphs, Pie Charts, Histograms, X-Y Graphs with 2 y Axes, Function Plots,
23 3D Plot, Line Plot, Surface Plot, Contour plots, Cylinder Plots, mesh, surf, contour, meshgrid,
24 Parametric Surfaces, Earth, Triangular Prism, Generating Points, Default Shading, Shading Flat, Shading Interp,
25 Arrays vs. Matrix Operations,
26 Dot Products, Example Calculating Center of Mass, Center of Gravity,
27 Matrix Multiplication and Division, Matrix Powers, Matrix Inverse, Determinatnts, Cross Products,
28 Applications of Matrix Operations, Solving Linear Equations, Linear Transformations, Eigenvectors
29 Engineering Application of Solving Systems of Linear Equations, Systems of Linear Equations, Kirchhoff's Circuit Laws,
30 Symbolic Differentiation, sym, syms, diff
31 Numerical Differentiation, fplot, Forward Difference, Backward Difference, Central Difference,
32 Numerical Integration, Engineering Applications, Integration, Trapezoid Rule, Simpson's Rule,
33 Monte Carlo Integration,
34 Introduction to ODE in System Biology
35 Introduction to System Biology, Gene Circuits,
36 Solving ODEs in Matlab, Repressilator, Programming steps
37 Interpolation, Cubic Spline Interpolation, Nearest Neighbor, Cubic, Two Dimensional, Three Dimensional,
38 Curve Fitting, Empirical Modelling, Linear Regression, Polynomial Regression, polyfit, polyval, Least Squres,
39 Introduction to Mathematica,
40 Programming in Mathematica
41 Basic Function in Mathematica, Strings, Characters, Polynomials, Solving Equations, Trigonometry, Calculus, 2D Ploting, Interactive Plots, Functions, Matlab vs. MAthematica
42 Numerical Data, Arthematic Operators, Data Types, Lists, Vectors, Matrices, String, Characters,
43 Mathematica Rule Based Programming, Functional Programming,
44 MAthematica Procedural Programming, Procedural Programs, Conditionals and Compositions, Looping Constructs, Errors, Modules,
45 Mathematica Predicates in Rule Based Programming, Patterns and Rules, Rules and Lists, Predicates, Blank, Blanksequence, BlackNullSequence, Number Puzzle,
46 Symbolic Mathematics and Programming, Rule Based Computation, Simplify, Expand, Solve, NSolve, Symbolic Visualisation,
47 Symbolic Computing in Matlab, Symbolic Algebra, sym, syms, Equations, Expressions, Systems of Equations, Calculus,

Views: 68
TO Courses

Data Analysis and Visualization is a Richfull series Directed to all students interested in Analysing and Visualizing Data using Excel, MATLAB and Wolfram Mathematica. This Course has been made by an expert prophesors in University of Wistern Australia, and Contains the main folowing Topics:
1 Data Visualization in Excel
2 Array Formula in Excel
3 2D Array Formula in Excel
4 Excel Macros
5 Why Matlab
6 Problem Solving in MATLAB
7 MATLAB Orientation - Data Types and Expressions
8 MATLAB Scripts and Functions, Storing Instructions in Files, Getting Help on Build-in Functions
9 Matrices in MATLAB
10 MATLAB Scripts and Functions
11 Random Numbers, Gaussian Random Numbers, Complex Numbers
12 An Examples of Script and Function Files
13 Control Flow, Flow Chart, Relational Operators, Logical Operators, Truth Table, if clause, elseif, Nested if statments, Switch Structure,
14 MATLAB Loops, Nested Loops, Repetition, while, For,
15 Problems with Scripts, Workspace, Why Functions, How to Write a MATLAB Function, Anonymous Functions,
16 MATLAB Programs Input / Output, Escape Characters, Formatted Output, Syntax of Conversion Sequence,
17 Defensive Programming, error, warning, msg, isnumeric, ischar, nargin, nargout, nargchk, narginchk, all,
18 Cell Arrays, Array Types to Store data, Normal Arrays, Curly Brackets, Round Brackets,
19 MATLAB Structures, What is a Structure?, Adding a Field to a Structure, Struct Function, Manipulate the Fields, Preallocate Memory for a Structure Array
20 Basic 2D Plotting, title, xlabel, ylabel, grid, plot
21 Multiple Plots, figure, hold on, off, legend Function, String, Axis Scaling, Subplot,
22 Types of 2D Plots, Polar Plot, Logarithmic Plot, Bar Graphs, Pie Charts, Histograms, X-Y Graphs with 2 y Axes, Function Plots,
23 3D Plot, Line Plot, Surface Plot, Contour plots, Cylinder Plots, mesh, surf, contour, meshgrid,
24 Parametric Surfaces, Earth, Triangular Prism, Generating Points, Default Shading, Shading Flat, Shading Interp,
25 Arrays vs. Matrix Operations,
26 Dot Products, Example Calculating Center of Mass, Center of Gravity,
27 Matrix Multiplication and Division, Matrix Powers, Matrix Inverse, Determinatnts, Cross Products,
28 Applications of Matrix Operations, Solving Linear Equations, Linear Transformations, Eigenvectors
29 Engineering Application of Solving Systems of Linear Equations, Systems of Linear Equations, Kirchhoff's Circuit Laws,
30 Symbolic Differentiation, sym, syms, diff
31 Numerical Differentiation, fplot, Forward Difference, Backward Difference, Central Difference,
32 Numerical Integration, Engineering Applications, Integration, Trapezoid Rule, Simpson's Rule,
33 Monte Carlo Integration,
34 Introduction to ODE in System Biology
35 Introduction to System Biology, Gene Circuits,
36 Solving ODEs in Matlab, Repressilator, Programming steps
37 Interpolation, Cubic Spline Interpolation, Nearest Neighbor, Cubic, Two Dimensional, Three Dimensional,
38 Curve Fitting, Empirical Modelling, Linear Regression, Polynomial Regression, polyfit, polyval, Least Squres,
39 Introduction to Mathematica,
40 Programming in Mathematica
41 Basic Function in Mathematica, Strings, Characters, Polynomials, Solving Equations, Trigonometry, Calculus, 2D Ploting, Interactive Plots, Functions, Matlab vs. MAthematica
42 Numerical Data, Arthematic Operators, Data Types, Lists, Vectors, Matrices, String, Characters,
43 Mathematica Rule Based Programming, Functional Programming,
44 MAthematica Procedural Programming, Procedural Programs, Conditionals and Compositions, Looping Constructs, Errors, Modules,
45 Mathematica Predicates in Rule Based Programming, Patterns and Rules, Rules and Lists, Predicates, Blank, Blanksequence, BlackNullSequence, Number Puzzle,
46 Symbolic Mathematics and Programming, Rule Based Computation, Simplify, Expand, Solve, NSolve, Symbolic Visualisation,
47 Symbolic Computing in Matlab, Symbolic Algebra, sym, syms, Equations, Expressions, Systems of Equations, Calculus,

Views: 50
TO Courses

Data Analysis and Visualization is a Rich-full series Directed to all students interested in Analyzing and Visualizing Data using Excel, MATLAB and Wolfram Mathematica. This Course has been made by an expert prophesiers in University of Western Australia, and Contains the main flowing Topics:
1 Data Visualization in Excel
2 Array Formula in Excel
3 2D Array Formula in Excel
4 Excel Macros
5 Why Matlab
6 Problem Solving in MATLAB
7 MATLAB Orientation - Data Types and Expressions
8 MATLAB Scripts and Functions, Storing Instructions in Files, Getting Help on Build-in Functions
9 Matrices in MATLAB
10 MATLAB Scripts and Functions
11 Random Numbers, Gaussian Random Numbers, Complex Numbers
12 An Examples of Script and Function Files
13 Control Flow, Flow Chart, Relational Operators, Logical Operators, Truth Table, if clause, elseif, Nested if statments, Switch Structure,
14 MATLAB Loops, Nested Loops, Repetition, while, For,
15 Problems with Scripts, Workspace, Why Functions, How to Write a MATLAB Function, Anonymous Functions,
16 MATLAB Programs Input / Output, Escape Characters, Formatted Output, Syntax of Conversion Sequence,
17 Defensive Programming, error, warning, msg, isnumeric, ischar, nargin, nargout, nargchk, narginchk, all,
18 Cell Arrays, Array Types to Store data, Normal Arrays, Curly Brackets, Round Brackets,
19 MATLAB Structures, What is a Structure?, Adding a Field to a Structure, Struct Function, Manipulate the Fields, Preallocate Memory for a Structure Array
20 Basic 2D Plotting, title, xlabel, ylabel, grid, plot
21 Multiple Plots, figure, hold on, off, legend Function, String, Axis Scaling, Subplot,
22 Types of 2D Plots, Polar Plot, Logarithmic Plot, Bar Graphs, Pie Charts, Histograms, X-Y Graphs with 2 y Axes, Function Plots,
23 3D Plot, Line Plot, Surface Plot, Contour plots, Cylinder Plots, mesh, surf, contour, meshgrid,
24 Parametric Surfaces, Earth, Triangular Prism, Generating Points, Default Shading, Shading Flat, Shading Interp,
25 Arrays vs. Matrix Operations,
26 Dot Products, Example Calculating Center of Mass, Center of Gravity,
27 Matrix Multiplication and Division, Matrix Powers, Matrix Inverse, Determinatnts, Cross Products,
28 Applications of Matrix Operations, Solving Linear Equations, Linear Transformations, Eigenvectors
29 Engineering Application of Solving Systems of Linear Equations, Systems of Linear Equations, Kirchhoff's Circuit Laws,
30 Symbolic Differentiation, sym, syms, diff
31 Numerical Differentiation, fplot, Forward Difference, Backward Difference, Central Difference,
32 Numerical Integration, Engineering Applications, Integration, Trapezoid Rule, Simpson's Rule,
33 Monte Carlo Integration,
34 Introduction to ODE in System Biology
35 Introduction to System Biology, Gene Circuits,
36 Solving ODEs in Matlab, Repressilator, Programming steps
37 Interpolation, Cubic Spline Interpolation, Nearest Neighbor, Cubic, Two Dimensional, Three Dimensional,
38 Curve Fitting, Empirical Modelling, Linear Regression, Polynomial Regression, polyfit, polyval, Least Squres,
39 Introduction to Mathematica,
40 Programming in Mathematica
41 Basic Function in Mathematica, Strings, Characters, Polynomials, Solving Equations, Trigonometry, Calculus, 2D Ploting, Interactive Plots, Functions, Matlab vs. MAthematica
42 Numerical Data, Arthematic Operators, Data Types, Lists, Vectors, Matrices, String, Characters,
43 Mathematica Rule Based Programming, Functional Programming,
44 MAthematica Procedural Programming, Procedural Programs, Conditionals and Compositions, Looping Constructs, Errors, Modules,
45 Mathematica Predicates in Rule Based Programming, Patterns and Rules, Rules and Lists, Predicates, Blank, Blanksequence, BlackNullSequence, Number Puzzle,
46 Symbolic Mathematics and Programming, Rule Based Computation, Simplify, Expand, Solve, NSolve, Symbolic Visualisation,
47 Symbolic Computing in Matlab, Symbolic Algebra, sym, syms, Equations, Expressions, Systems of Equations, Calculus,

Views: 118
TO Courses

A common task for scientists and engineers is to analyze data from an external source. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. See http://apmonitor.com/che263/index.php/Main/PythonDataAnalysis

Views: 161386
APMonitor.com

In this Statistics Using Python Tutorial, Learn Exploratory Data Analysis In python Using data set from gapminder.org . We will code interactive graphs in Python using matplotlib and pandas within Jupyterlab.
🔷🔷🔷🔷🔷🔷🔷
Jupyter Notebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python
🔷🔷🔷🔷🔷🔷🔷
Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA
Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8
Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw
Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ
🔷🔷🔷🔷🔷🔷🔷
*** Complete Python Programming Playlists ***
* Python Data Science
https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK
* NumPy Data Science Essential Training with Python 3
https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b
* Python 3.6.4 Tutorial can be fund here:
https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ
* Python Smart Programming in Jupyter Notebook:
https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2
* Python Coding Interview:
https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g

Views: 1379
TheEngineeringWorld

( Advanced MS Excel 2010 - https://www.edureka.co/advanced-ms-excel-self-paced )
"Watch Sample Class recording: http://www.edureka.co/advanced-ms-excel-self-paced?utm_source=youtube&utm_medium=referral&utm_campaign=visualizing_data
Graphic representation of spreadsheet data that uses columns, points, pie wedges and other forms to represent numbers from a range. As the data in the spreadsheet changes, the chart also changes to reflects the new numbers.
Topics covered in this video are:
1) Plot area.
2) The category axis.
3) The value axis.
4) The Grid lines.
5) Data Values.
Related Blog :
http://www.edureka.co/blog/advanced-ms-excel?utm_source=youtube&utm_medium=referral&utm_campaign=visualizing_data
Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would
prefer a hassle free and self paced learning environment, accessible from any part of the world.
The topics related to MS Excel 2010 have extensively been covered in our course'Advanced MS Excel 2010'
For more information, please write back to us at [email protected]
Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004"

Views: 2034
edureka!

Views: 99
TO Courses

Data Analysis and Visualization is a Rich-full series Directed to all students interested in Analyzing and Visualizing Data using Excel, MATLAB and Wolfram Mathematica. This Course has been made by an expert prophesiers in University of Western Australia, and Contains the main flowing Topics:
1 Data Visualization in Excel
2 Array Formula in Excel
3 2D Array Formula in Excel
4 Excel Macros
5 Why Matlab
6 Problem Solving in MATLAB
7 MATLAB Orientation - Data Types and Expressions
8 MATLAB Scripts and Functions, Storing Instructions in Files, Getting Help on Build-in Functions
9 Matrices in MATLAB
10 MATLAB Scripts and Functions
11 Random Numbers, Gaussian Random Numbers, Complex Numbers
12 An Examples of Script and Function Files
13 Control Flow, Flow Chart, Relational Operators, Logical Operators, Truth Table, if clause, elseif, Nested if statments, Switch Structure,
14 MATLAB Loops, Nested Loops, Repetition, while, For,
15 Problems with Scripts, Workspace, Why Functions, How to Write a MATLAB Function, Anonymous Functions,
16 MATLAB Programs Input / Output, Escape Characters, Formatted Output, Syntax of Conversion Sequence,
17 Defensive Programming, error, warning, msg, isnumeric, ischar, nargin, nargout, nargchk, narginchk, all,
18 Cell Arrays, Array Types to Store data, Normal Arrays, Curly Brackets, Round Brackets,
19 MATLAB Structures, What is a Structure?, Adding a Field to a Structure, Struct Function, Manipulate the Fields, Preallocate Memory for a Structure Array
20 Basic 2D Plotting, title, xlabel, ylabel, grid, plot
21 Multiple Plots, figure, hold on, off, legend Function, String, Axis Scaling, Subplot,
22 Types of 2D Plots, Polar Plot, Logarithmic Plot, Bar Graphs, Pie Charts, Histograms, X-Y Graphs with 2 y Axes, Function Plots,
23 3D Plot, Line Plot, Surface Plot, Contour plots, Cylinder Plots, mesh, surf, contour, meshgrid,
24 Parametric Surfaces, Earth, Triangular Prism, Generating Points, Default Shading, Shading Flat, Shading Interp,
25 Arrays vs. Matrix Operations,
26 Dot Products, Example Calculating Center of Mass, Center of Gravity,
27 Matrix Multiplication and Division, Matrix Powers, Matrix Inverse, Determinatnts, Cross Products,
28 Applications of Matrix Operations, Solving Linear Equations, Linear Transformations, Eigenvectors
29 Engineering Application of Solving Systems of Linear Equations, Systems of Linear Equations, Kirchhoff's Circuit Laws,
30 Symbolic Differentiation, sym, syms, diff
31 Numerical Differentiation, fplot, Forward Difference, Backward Difference, Central Difference,
32 Numerical Integration, Engineering Applications, Integration, Trapezoid Rule, Simpson's Rule,
33 Monte Carlo Integration,
34 Introduction to ODE in System Biology
35 Introduction to System Biology, Gene Circuits,
36 Solving ODEs in Matlab, Repressilator, Programming steps
37 Interpolation, Cubic Spline Interpolation, Nearest Neighbor, Cubic, Two Dimensional, Three Dimensional,
38 Curve Fitting, Empirical Modelling, Linear Regression, Polynomial Regression, polyfit, polyval, Least Squres,
39 Introduction to Mathematica,
40 Programming in Mathematica
41 Basic Function in Mathematica, Strings, Characters, Polynomials, Solving Equations, Trigonometry, Calculus, 2D Ploting, Interactive Plots, Functions, Matlab vs. MAthematica
42 Numerical Data, Arthematic Operators, Data Types, Lists, Vectors, Matrices, String, Characters,
43 Mathematica Rule Based Programming, Functional Programming,
44 MAthematica Procedural Programming, Procedural Programs, Conditionals and Compositions, Looping Constructs, Errors, Modules,
45 Mathematica Predicates in Rule Based Programming, Patterns and Rules, Rules and Lists, Predicates, Blank, Blanksequence, BlackNullSequence, Number Puzzle,
46 Symbolic Mathematics and Programming, Rule Based Computation, Simplify, Expand, Solve, NSolve, Symbolic Visualisation,
47 Symbolic Computing in Matlab, Symbolic Algebra, sym, syms, Equations, Expressions, Systems of Equations, Calculus,

Views: 75
TO Courses

Views: 48
TO Courses

Views: 87
TO Courses

In this video tutorial, we will give you a brief overview of all the visual tools in MAXQDA 2018. Except MAXMaps, which is so comprehensive, that it gets its own tutorial.
If you want more information about each visual tool, you can also check out our tutorials about Visual Tools in MAXQDA 12, until we have produced new ones. You can find them in our MAXQDA 12 playlist, right here:
https://www.youtube.com/playlist?list=PLeKBk3lzrGUyHlaNZ7dX60u4_5sw5Mow6

Views: 7398
MAXQDA VERBI

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations, none of these tools can compete with R in terms of the sheer breadth of, and control over, crafted data visualizations.
As an example, R’s ggplot2 package provides the R programmer with dozens of print-quality visualizations – where any visualization can be heavily customized with a minimal amount of code.
In this webinar Dave Langer will provide an introduction to data visualization with the ggplot2 package. The focus of the webinar will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business.
Attendees will learn how to:
• Craft ggplot visualizations, including customization of rendered output.
• Choose optimal visualizations for the type of data and the nature of the analysis at hand.
• Leverage ggplot2’s powerful segmentation capabilities to achieve “visual drill-in of data”.
• Export ggplot2 visualizations from RStudio for use in documents and presentations.
Github:
https://github.com/datasciencedojo/IntroDataVisualizationWithRAndGgplot2
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Views: 94534
Data Science Dojo

- Introduction to Tableau Interface
- Concept of Dimensions and Measures in Tableau
- Introduction to Rows and Columns, Tool-kit in Tableau
- Creating meaningful Business Visualisations in Tableau
- Exploring data visually in Tableau
- Using Cross-tab's in Tableau
- 2D Data visualisations based on Geo - dimensions in Tableau
- Exploring data for Business issues of profitability and more

Views: 1094
Equiskill Insights LLP

Views: 44
TO Courses

Views: 40
TO Courses

Download Files: https://people.highline.edu/mgirvin/AllClasses/218_2016/218Excel2016.htm
In this video learn about:
(00:28) Define Charts. What do Charts do?
(01:12) Effective Charts: No Chart Junk. Example of Chart Junk.
(02:54) Types of Charts: 11 different chart types.
(06:55) Terminology for Chart Elements:
1. (08:15) Column Charts
2. (12:15) Bar Charts
3. (13:41) Pie Charts
4. (14:25) Stacked Column Charts
5. (14:25) Clustered Column Charts
6. (17:44) Histogram. Two Examples: 1) Retail Sales Data Histogram with FREQUENCY Array Function. 2) Count Web Transactions by Hour with PivotTable.
7. (28:45) Line Charts. Time Series Chart.
8. (31:54) X-Y Scatter for Sample Data.
9. (34:45) Break Even Chart X-Y Scatter
10. (46:05) Combo Charts
11. (47:30) Bubble Chart
(51:19) Summary

Views: 76008
ExcelIsFun

Learn Excel in 6 Weeks
Earn a verified certificate. Get started, sign up today.
https://www.edx.org/course/excel-data-analysis-visualization-microsoft-dat206x
About this course
Microsoft Excel is one of the most widely used solutions for analyzing and visualizing data. Beginning with Excel 2010, new tools were introduced to enable the analysis of more data, resulting in less time spent creating and maintaining the solutions and enabling a better understanding of what the data means. This better understanding is facilitated by improved visualizations and more sophisticated business logics.
Do you want to take your advanced Excel skills to the next level? Are you exploring new ways to get and transform your data and create visualization?
Check out this practical new course, taught in short, lecture-based videos, complete with demos, quizzes, and hands-on labs, and skill up on many of the built-in business intelligence (BI) tools and features in Excel. Learn how to present the most relevant data with dynamic reports and presentations, as expert Dany Hoter walks you through all the important details.
In this course, get an introduction to the latest versions of these new tools in Excel 2016. See how to import data from different sources, create mashups between data sources, and prepare the data for analysis. After preparing the data, learn about how business calculations—from simple to more advanced—can be expressed using the DAX calculation engine. And see how these different technologies work together inside Excel. Learn how the data can be visualized and shared to the Power BI cloud service, after which it can be used in dashboards, queried using plain English sentences, and consumed on mobile devices.

Views: 51
Bobby News Hub

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Current Dividend Preference. Participating Preferred Stock. Convertible Preferred Stock. Cumulative preferred stock includes a provision that requires the company to pay preferred shareholders all dividends, including those that were omitted in the past, before the common shareholders are able to receive their dividend payments. Non-cumulative preferred stock does not issue any omitted or unpaid dividends. If the company chooses not to pay dividends in any given year, the shareholders of the non-cumulative preferred stock have no right or power to claim such forgone dividends at any time in the future. Participating preferred stock provides its shareholders with the right to be paid dividends in an amount equal to the generally specified rate of preferred dividends, plus an additional dividend based on a predetermined condition. This additional dividend is typically designed to be paid out only if the amount of dividends received by common shareholders is greater than a predetermined per-share amount. If the company is liquidated, participating preferred shareholders may also have the right to be paid back the purchasing price of the stock as well as a pro-rata share of remaining proceeds received by common shareholders. Significance to Investors. Shareholder. Preferred Stock.