Monday, May 28, 2018

UrBizEdge Democracy Day Promo -- Free Excel Consulting And Highly Subsidized Beginner Excel Class

To celebrate the Democracy Day we (UrBizEdge Limited) are offering every Nigerian (and non-Nigerian too) free consulting services. If you've got any not too complex task or need a short virtual class/training we would be glad to help you for free. If the political and economic circumstances in the country are not making you happy, then let us be the silver-lining around the clouds. We want to be the rainbow -- to give you something memorable and refreshing -- this period.

The offer starts from today and expires on 30th of June 2018. And you are only permitted to contact Hannah on 0802-118-0874 and or Emmanuel on 0908-482-5064 and to take advantage of the offer. If you call or email Michael, he's going to bill you and pass the work to them. So get same results but pay nothing.

And the whole Democracy Day promo doesn't end there. We are organizing a hugely subsidized beginner Excel class to take place in the month of June 2018. A two days class but taken at a pace and level that is perfect for beginners. We will provide our usual high quality training materials, tea break, lunch break and after training support. But rather than charging the standard fee of N100,000 we do for our regular In-depth Excel master class (often with some discounts) or the N60,000 we charge the companies sending many participants for a custom beginner class; we will be charging a token sum of N25,000/participant without cutting down on the hard copy materials, lunch, tea break and after training support.

If you know someone who should take advantage of these offers, kindly forward them this details. 

Once again, happy democracy day! Long live Nigeria! Long live you! Long live me! Long live Excel!

Friday, May 25, 2018

Business Intelligence using Power BI and Business Data Analysis Using MS Excel Training in Lagos, Port Harcourt and Abuja

(You can help forward to anyone you feel would benefit from this. Thanks.) 

The business world is now data driven and every business professional must now be fluent in the language of data. William E. Deming had the most accurate way of portraying this new age: “In God we trust; all others must bring data.” Without data skills you will have a tough time influencing in the business world. You must learn to manipulate data, make compelling data stories, leverage data for insights and drive management decisions with rich actionable dashboards.

UrBizEdge Limited, Nigeria’s leading business data analysis company is putting together these special training for proactive business professionals. It's intended for Sales Managers, Financial Analysts, Project Managers, Business Analysts, Data Analysts, NGO program managers, MIS Analysts, Strategy Analysts, Business Intelligence Analysts, HR Executives and power Excel users.

The training will be facilitated by a Microsoft recognized Excel and Power BI Expert who is also a Microsoft Certified Trainer and the only Microsoft Excel Most Valuable Professional (MVP) in Africa (there are just about 125 in the whole world and it is the highest level of recognition from Microsoft to an industry expert). He has authored bestselling books on Data Analysis using Power BI and MS Excel. We have had participants of our training from Citi Bank, Standard Chartered Bank, Dalberg, SaveTheChildren, Mobil, TCN, Chevron, Vodacom, Nestle, Christian Aid UK, Guinness Nigeria, Nigerian Breweries, Delta Afrik, LATC Marine, Broll, Habanera (JTI), Custodian and Allied Insurance, SABMiller, IBM, Airtel, Diamond Bank, Promasidor, eHealth Africa, ECOWAS, Biofem Pharmaceuticals, Ministry of Finance, FMDQ, Schlumberger, Palladium Group, Nokia Siemens Networks and DDB.

To register reach Michael on 08089382423 and or Hannah on 08021180874 and or Emmanuel on 09084825064 and to register. There is a class size limit.

Power BI Training Date: Friday 15th June 2018 to Saturday 16th June 2018
Venue: Kristina Jade Learning Center, 70b Olorunlogbon street, after Banex Hotel, Anthony Village, Lagos. 

Abuja Venue: Hotel Rosebud, 33 Port Harcourt crescent, off Gimbiya street, Garki 11, Abuja.

Port Harcourt Date (MS Excel): Friday 22nd June 2018 to Saturday 23rd June 2018
Port Harcourt Venue: Aldgate Hotel, 20B King Perekule street, GRA Phase 2, Port Harcourt, Rivers state. 

Lagos Venue: Kristina Jade Learning Center, 70b Olorunlogbon street, after Banex Hotel, Anthony Village, Lagos. 

The Business Intelligence with Power BI training outline is:
1.     Power BI’s strength and weakness compared to the other popular BI tools
2.     Important concepts of Power BI: real-time insights, drilldown, drill through, management dashboards, live connection, data governance, real-time collaboration, going from data to insights and from insights to action, trigger alerts, controlled access etc.
3.     Connecting to any type of data source (from structured to unstructured which will require some transforming)
4.     Getting data from existing services, organizational content pack, flat files and live databases
5.     Data Transformation (very broad and requires some knowledge of data analysis) and we will use DAX and M formulas too.
6.     Creating relationships between the datasets and leveraging hierarchy (a.k.a. data modelling)
7.     Creating Reports
8.     Visualizations and the science behind choosing the right visuals
9.     Importing custom visuals (especially word cloud for sentiment analysis and other very useful non-native visuals)
10.   Creating dashboards
11.   Publishing Reports from the Power BI Desktop and pinning to dashboards
12.   Scheduling refresh and configuring data gateways
13.   Q & A Natural Language query
14.   Integrating with Cortana
15.   Live Dashboards
16.   Collaboration and sharing
17.   Printing and Export to PowerPoint
18.   Analyzing the dashboard data (report) from Power BI service in Excel (new feature)
19.   Value Proposition to corporate customers
20.   Use case scenarios for entire company or business unit or departments
21.   Access from mobile app and setting data alerts (automated notifications when something of note happens)
22.   Lots of interaction and practice

The Excel based Data Analysis training outline is:

1) Data Manipulation in Excel 
We’ll show you, from a consultant expertise level, how to manipulate data in Excel. From data preparation/cleaning to data formatting the professional way. We’ll cover both the science and the art of data manipulation in Excel, and share very useful keyboard shortcuts and expert tricks that will speed up your productivity in Excel.

2) Data Visualization and Presentation in Excel
A picture is worth a thousand words. And in the business world, it is often the only way to not bore your audience and pass the valuable message you’ve uncovered in your data analysis. We will teach you the foundations of data visualization – from the different types of charts to when to use each of them. Then we will work through business samples to learn the art part of doing data visualization right. You will learn the rules of business data reporting via charts and gain from our industry wealth of consulting for businesses in this vital area.

3) Large Data Analysis: Pivot Table, Pivot Chart and PowerPivot
You should never say you know Excel if you don’t know how to use Pivot Table. It is that important. It is Excel’s premium tool for analysing large data – sales data, inventory data, HR data, transaction data and most business operations data. We are going to cover from the basics to the very advanced use of Pivot Tables. We will show you how to create dynamic reports with Pivot Table; how to overcome some of its layout issues; how to turn off the distracting controls in your final report; how to create calculated fields; how to create Pivot Charts; and the special tricks only a full-time Excel consultant can show you that will turbo-charge you Pivot Table skills. Then we’ll show you how we analyse data of up to (and even above) 30 million rows in Excel. Yes, in Excel. Heard of PowerPivot?

4) Business Data Analysis 
In this section, we teach you the secrets that separate the analysis experts from the people with head/academic knowledge Excel. It is one thing to know the different tools in Excel and it is another completely different thing to know how to expertly mix them together to creatively deliver value at high speed. As full-time Excel chef, we will show you the secret ingredients that make companies consult us even when they have Excel super users in their organization.

5) Executive Dashboards and Reporting
This is the level self-knowledge will not get you to. How do you create an uncrowded insightful visualization for a report with 1000s of rows? Then how about for your sales analysis report of many products and regions? How do you show the different interactions in your data? In short, how do you bring your data to life and take it from a boring confusing mass of text to an interactive exciting visualization? You have to come to get the answers.

6) Excel to PowerPoint
Management level reports are best presented in PowerPoints. When you’ve got a delicate story to tell, you have to guide your audience through a one idea/insight a slide PowerPoint. We won’t teach you how to design slides but we will teach you how to make your chart slides speak very loud. You will also learn the tricks of linking your PowerPoint charts to Excel. It will help cut down the hours you spend on weekly/monthly PowerPoint reports. We will also show you how to embed Excel files in your PowerPoint slide. No more sending separate Excel files when you can embed them right on the very slide you reference their data.

7) Excel VBA
Forget about all you’ve heard about Macros or VBA. Let’s show you how easy and exciting it is to break into the Excel VBA world.

To register reach Michael on 08089382423 and or Hannah on 08021180874 and or Emmanuel on 09084825064 and to register. There is a class size limit.

For a taste of our high quality content, view our free tutorial videos at

Thursday, May 10, 2018

March 2018 Webinar: My Most Important Excel Formulas; Would Recommend You Master Them

As a full-time Microsoft Excel consultant, of all the over 450 formulas in Excel there are just a handful I focus on in my training classes because they provide the most value in practical day-to-day use of Excel. If you examine any reports or templates (even the most complex one you've ever come across), you will see that the these formulas are the ones powering those reports.

In this webinar, I will be taking you through those formulas in my usual very practical way and provide you with the practice file for your own use later on.

The formulas are:
5. IF
6. SUM
13. LEFT
15. MID
17. DAY
19. YEAR
23. RAND

The webinar was live on YouTube and the recording is made available at

Time: 3:00pm (GMT +1)
Date: 28 March 2018
VenueYouTube Live (

To be in loop of all our future webinars, then sign up to our Webinar Directory (

Thursday, May 3, 2018

How To Thoroughly Compare Two Different Files or Different Versions of Same File In Microsoft Excel

Ever suspected someone has changed your file or report in Excel or edited places you didn't want them touching? Or it is that you have two different files that should have some similarities and you want to know where the difference lie.

If you use Excel 2013 or Excel 2016 professional plus, then you should start celebrating. Microsoft has included a very helpful tool called Inquire that does that comparison of files for you on any level of detail you want. 

Below is an illustration of me using it to compare two different version of same loan repayment computation file. See how easy it is to use. (You'll first have to enable it, then use).


Thursday, April 26, 2018

Illustrated Steps To Creating Scheduled Shutdown and Startup for Azure Virtual Machines (Azure VM)

Creating my Nigerian Market Data app (stocks, economic and fx rates app) involved setting up an Azure VM that I run a couple of Python scripts on daily. 

I used Windows Task Scheduler to automate the Python scripts to run between 9:20 pm and 11:00 pm. It wouldn't make sense to keep the server (Azure VM) running all day. So I set up an automation on Azure to start up the server at 9:00 pm and shut it down by midnight. Below are the steps to doing this.

If you run a search online, you will come across multiple ways to doing this. Some require you writing Powershell scripts. The method I am going to illustrate is the least error prone and most straightforward way I have come across.

Step 1: Log into your Azure account portal

Click on Create a resource.

Step 2: Search for Automation

Click on Automation.

Step 3: Create a new Automation

Step 4: Complete the Automation Account Creation
Make sure to tick use existing resource group, and pick the resource group of the VM you want to auto shutdown/startup. Also best to match the location selection.

Step 5: Add Startup and Shutdown Runbooks to the Automation Account
Select the automation account you just created.

Select Runbook under Process Automation. And click on Browse gallery. This allows you to create the startup and shutdown automation runbooks.

Click on Edit in the created Runbook

Then publish the runbook.

Click on Schedule after publishing.

Set the Time to run the Start VM runbook by clicking on "Link a Schedule to your runbook"

Lastly, set the parameters which will link it to the VM you want to control. Make sure you match the resource group and VM name. You can leave the AzureConnectionAssetName blank so the default is used.

Now you repeat same steps for Shutdown runbook after picking from the runbook gallery.

Step 6: Sit back and watch the automation runbooks do their work

You can monitor the runbook activities from Jobs in the Runbook Overview.

And that's all!

Sunday, April 22, 2018

Easy Steps To Creating And Deploying A Predictive Model Using Azure Machine Learning Studio

Yesterday, I demoed how in 10 mins you can create a predictive model, deploy it as a web service and test it without having to install anything or pay any money. That is the awesomeness made possible by Microsoft with the super easy to use Azure Machine Learning Studio. The yesterday event was the Lagos edition of the Global Azure Bootcamp held at Microsoft office, Lagos.

Participants were able to follow along, created and deployed their own predictive models too. In today's post I will be guiding you with easy steps to follow on how you too can in a few minutes create and deploy a predictive model cost-free with Azure Machine Learning Studio.

Step 1
Download the sample data we would use: Bank Marketing data from UCI Machine Learning Repository. If you download from UCI Machine Learning Repository directly, then it is the bank-additional-full.csv file in the zip file you end up with. Then you have to make sure that you break the data into separate columns rather than leave them comma separated, using Excel's Text to Columns. For you ease, I have shared a cleaned version you can directly use without any extra work by you: Bank Marketing data download

The sample data is a marketing campaign data of a Portuguese bank from May 2008 to November 2010 recording the details of prospects reached via phone calls and whether they eventually took up the service the bank was trying to sell them.
The cleaned sample data
Below is the explanation of the different fields in the data records.

Input variables:
# bank client data:
1 - age (numeric)
2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')
3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)
4 - education (categorical: 'basic.4y','basic.6y','basic.9y','','illiterate','professional.course','','unknown')
5 - default: has credit in default? (categorical: 'no','yes','unknown')
6 - housing: has housing loan? (categorical: 'no','yes','unknown')
7 - loan: has personal loan? (categorical: 'no','yes','unknown')
# related with the last contact of the current campaign:
8 - contact: contact communication type (categorical: 'cellular','telephone') 
9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
# other attributes:
12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
14 - previous: number of contacts performed before this campaign and for this client (numeric)
15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')
# social and economic context attributes
16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)
17 - cons.price.idx: consumer price index - monthly indicator (numeric) 
18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 
19 - euribor3m: euribor 3 month rate - daily indicator (numeric)
20 - nr.employed: number of employees - quarterly indicator (numeric)

Output variable (desired target):
21 - y - has the client subscribed a term deposit? (binary: 'yes','no')

Step 2
Sign up for Azure ML studio. It is easy and free: 

Step 3
Upload the Bank Marketing dataset. From Datasets section on the left menu pane, click New at the bottom left.

Step 4
Create a new experiment. From Experiments section on the left menu pane, click New at the bottom left. Choose a blank experiment, as we are creating ours from scratch.

Step 5
Now we start dragging the tasks we want to carry out into the Experiment workspace, after renaming the Experiment.

Drag in the dataset we uploaded, it is in the Saved Dataset section on the left.

Next, we need to isolate the fields that would be useful for our predictive model. If you look at the description of all the fields in the dataset, it is obvious that some are not practically useful in creating a prediction of whether a prospect will take up the marketed service or not. Example is the length of the call, there is no way you would know that until the end of the call -- so not useful for profiling who to call (targeted marketing). By my thinking, the fields I that would be of real world use in creating an actionable predictive model are -- age, job, marital, education, default, housing, loan.

Drag Select Columns in Dataset in the Manipulation subsection of Data Transformation section. Connect the dataset previously dragged in to the select columns task. Then click on Launch column selector, and select the columns needed (including the outcome we want to predict, so as to be able to train the model).

Next, split the data into training set and testing set for building our predictive model. Drag Split Data, connect to the select columns task and on the settings pane on the right, set the training set to 0.75 (75%) of the entire dataset.

Drag in the model algorithm to use. It's under the Initialize Model. I chose to use the Two-Class Decision Forest. In the end, you would evaluate the model to see if it fits well or you should try another algorithm.

Drag in Train Model. Connect to both the already dragged in algorithm and the left side of the Split Data (training set). Select the outcome to predict.

Drag in Score Model. Connect to the Train Model and the testing set of the Split Data.

Lastly, drag in Evaluate Model. Connect to Score Model.

Now run the entire experiment.

Wait for it to finish running.

Right click on Evaluate Model and visualize the evaluation result to see the fitness/accuracy of the algorithm.

If you are okay with the fit, then what's left is to publish. Otherwise, you can change the algorithm, re-run and re-evaluate the fit.

Step 6
Now you set up the model as a web service that can be deployed online.

Change the input connector to point to the Score Model. 

Also, remove the predicted column from the Selected Column as it was only needed for training the model.

Now re-run and deploy as web service.

You are presented with the web service details to use for integrating with any app or online tool. You can even test the API directly.

And that's how you create and deploy a predictive model in Azure Machine Learning Studio without installing anything on your computer and without paying a cent/kobo.