All articles with 'downloads' Tag

Recently, we discussed about the case of unwieldy data and how we lookup what we want using formulas like SUMIFS. Today, let us learn few more ways to solve the same problem.

Suitable structure spawns simple solutions

Poorly structured is the 2nd biggest problem of analysts. The first one is not enough coffee. That is why there is a dictum in the data analytics world.

Structure is everything

So, we can easily solve our lookup problem, if our data were to magically re-arranged in 2 column fashion – Data & Value.

Occasionally we deal with data that is so uncooperative that we might as well give up and go back to calculators & ledger books.

Recently I found myself in such a situation and learned something new.

Introducing … data that won’t play nice

Drum roll please. Here is a data-set that I got from somewhere.

The problem – build a lookup formula

And the problem. Oh, simple. Write a lookup formula to find how many customer walk-ins we have on any given day.

But how?

This is a guest post by Sohail Anwar.

Let’s not bore you with an intro. You are about to learn a VLOOKUP trick that Lucifer himself would not want you to know. It’s so absurdly powerful that it was developed in a lab and had to be tested on Rocky’s arch nemesis Ivan Drago.

Presenting the Multiple criteria VLOOKUP!

…boring…pass, we’ve seen it.

Oh, have you? Not like this you haven’t. This will change the way you work with Excel.

Let me start with an easy example. Here’s some data and we would love to know what Bb and Dd is.

Recently, I saw this chart on Economist website.

It is trying to depict how various cities rank on livability index and how they compare to previous ranking (2014 vs 2009).

As you can see, this chart is not the best way to visualize “Best places to live”.

Few reasons why,

• The segregated views (blue, gray & red) make it hard to look for a specific city or region
• The zig-zag lines look good, but incredibly hard to understand % changes (or absolute changes)
• Labels are all over the place, thus making data interpretation hard.
• Some points have no labels (or ambiguous labels) leading to further confusion.

After examining the chart long & hard, I got thinking.

Its no fun criticizing someones work. Creating a better chart from this data, now thats awesome.

Here is a charming little problem to kick start your day.

Lets say you run a cute little bakery around the corner. Since you want your prices to look charming, you have a policy to round them down or up based on below rule.

If the price ends with 0, 1 or 2 cents, round it down to 9 cents.

If the price ends with 3, 4 or 5 cents, round it up to 5 cents.

If the price ends with 6, 7, 8 or 9 cents, round it up to 9 cents.

For example,

So how do you round to nearest charmed price? You could do it manually. But you would rather bake a few more of those Tiny Cup Cakes than waste time rounding the prices. So you want an automatic way to round prices. This is where Excel helps.

ABC analysis is a popular technique to understand and categorize inventories. Imagine you are handling inventory at a plant that manufactures high-end super expensive cars. Each car requires several parts (4,693 to be exact) to assemble. Some of these parts are very costly (say few thousand dollars per part), while others are cheap (50 cents per part). So how do you make sure that your inventory tracking efforts are optimized so that you waste less time on 50 cent parts & spend more time on costly ones?

This is where ABC analysis helps.

We group the parts in to 3 classes.

• Class A: High cost items. Very tight control & tracking.
• Class B: Medium cost items. Tight control & moderate tracking.
• Class C: Low cost items. No or little control & tracking.

Given a list of items (part numbers, unit costs & number of units needed for assembly), how do we automatically figure which class each item belongs to?

And how do we generate above ABC analysis chart from it?

We all know about the MAX formula. But do you know about 3D Max?

Sounds intriguing? Read on.

Lets say you are the sales analyst at ACME Inc. Your job involves drinking copious amounts of coffee, creating awesome reports & helping ACME Inc. beat competition.

For one of the reports, you need to find out the maximum transactions by any customer across months.

But there is a twist in the story.

Your data is not in one sheet. It is in multiple sheets, one per month.

Podcast: Play in new window | Download

Subscribe: Apple Podcasts | Spotify | RSS

In the 19th session of Chandoo.org podcast, lets talk about modeling best practices.

What is in this session?

I am very happy to interview my good friend, blogger, author, excel trainer & business-women – Danielle Stein Fairhurst for this session. I first met Danielle when I went to Sydney, Australia in April 2012. Our friendship & collaboration grew a lot in the last 2.5 years. She is a great speaker & trainer. This episode is loaded with her trademark style commentary, explanation & tips for better modeling. I hope you will enjoy it.

In this podcast, you will learn,

• Introduction to Danielle & her work
• 6 Tips for Best Practice Modeling
  • Write consistent formulas
  • Avoid hard-coding
  • Smart referencing
  • Ditch the bad habits
  • Document assumptions
  • Format & label things
• Resources for learning more

Last week, we had an interesting homework problem – What is the average speed of this road trip?

We received more than 150 answers. But to my surprise, 57 of them are wrong. So today, lets learn how to calculate the average speed correct way.

Today, lets learn how to create an interesting chart. This, called as network chart helps us visualize relationships between various people.

Demo of interactive network chart in Excel

First take a look at what we are trying to build.

Looks interesting? Then read on to learn how to create this.

This is a guest post by Krishna, a football lover & one of our readers.

The wait for lifting the most valued priced in football for Germans was finally over. For a football fan, world cup is best time that is scheduled every four years and that if your favorite team lifting the trophy is like your crush is going on a date with you. 🙂

A sneak-peek at the final dashboard

Here is the final dashboard (it has more functionality than depicted). Click on it to enlarge.

Ok people. Let me tell you that this post is almost not about Excel. It is about how one Excel blogger’s (yours truly) dream of long distance cycling came true. So sit back, grab your favorite drink and read between sips.

So what is this all about?

Last Sunday (27th July) & Monday (28th), I finished my first ever 200KM bicycle ride. I rode for a little more than 12 hours, burned 5,179 calories & rode 206 kilometers.

It is definitely one of the most memorable, tiresome & uplifting experiences in my life. So naturally, I want to share the story with you.

On July 4th this year, I published an animated fireworks chart for you. Many of you liked it. Quite a few wanted to know how its made.

So here is a video explaining the construction of fireworks.

(You can see this video on our YouTube Channel too)

Today lets learn about Excel CHOOSE() function.
CHOOSE eh? What does it do?
To understand CHOOSE() and appreciate its uses, lets invent an imaginary boos-subordinate pair.

Jasmine is the boss. She is, well, lets call her peculiar. She likes olives, Tuesdays & color Red. She hates potatoes.

Martin is the faithful butler of Jasmine. He is obedient, quirky and tall. He likes lotuses, Fridays & color blue. He hates potassium.

Enter Jasmine’s scarf problem:

Jasmine likes to wear a different colored scarf every weekday. She likes to wear Red colored scarf on Mondays & Tuesdays. She likes to put on the blue polka dot scarf on Wednesdays. On Thursdays, she wears her olive colored scarf. On Fridays & Saturdays, she prefers the lovely orange blue scarf. Sundays are no scarf days.

No wonder she is peculiar.

Finally the wait is over. Here are the winners of our 2014 dashboard contest. Check them out.