All articles with 'Analytics' Tag

Excel pivot tables are very useful and powerful feature of MS Excel. They are used to summarize, analyze, explore and present your data. In plain English, it means, you can take the sales data with columns like salesman, region and product-wise revenues and use pivot tables to quickly find out how products are performing in each region.

In this tutorial, we will learn what is a pivot table and how to make a pivot table using excel.

Do you want to simulate multiple cash-flow scenarios and calculate the rate of return? Then this article is for you. In this page, learn how to,

• Introduction to IRR & XIRR functions
• Calculate rate of return from a set of cash-flows with XIRR
• Simulating purchase or terminal value changes with data tables
• Apply conditional formatting to visualize the outputs
• Common issues and challenges faced when using XIRR

Can we predict cricket match score in Excel? Using machine learning, ensemble modeling, multiple regression and Excel formulas we can. This tutorial explains how.

Excel table is a series of rows and columns with related data that is managed independently. Excel tables, (known as lists in excel 2003) is a very powerful and supercool feature that you must learn if your work involves handling tables of data.

What is an excel table?

Table is your way of telling excel, “look, all this data from A1 to E25 is related. The row 1 has table headers. Right now we just have 24 rows of data. But I can add more later!”

Let’s say you are the people manager at ACME Inc. You are looking staff list for the months – January and February 2017. You see that we had 4,000 employees in Jan and 4,200 employees in Feb. So what is the churn?

• Is it just 200?
• Or is it the sum of people who left and who joined?
• What if you want to find out how many people moved to new designations / departments or groups?

You see, churn is tricky to figure out.

So why not invite the pros? ie Power Query. 

Last week, I asked you to share an analysis problem that you couldn’t solve in Excel. We got quite a few very interesting problems in comments and email. In this post, let me explain how to solve Sara’s copy shop problem using Excel.

What is Sara’s copy shop problem?
Thanks to Caroline who posted this problem.

Sara wants to open a copy shop. Each copier costs $5,000 per year to lease. The rent & other fixed costs per month are $300. There is a $0.02 variable cost per copy. Each copier can print up to 100,000 copies per year. She plans to charge $0.11 per copy from her customers. Sara estimates that the demand can be any of the 4 values – 500, 1000, 1500 or 2000 copies per day.

1. Build a model to estimate profit per given number of copiers & demand values
2. Find the mix of copiers & demand values that can make maximum profit for Sara (copiers – 1 to 6, demand – 500 to 2000)

Time for a quick show & tell.

Tell me about an analysis problem that you couldn’t solve with Excel?

It can be because you didn’t know how to solve the problem or Excel isn’t the tool for it or any other reason.

Go ahead and speak up. Post your tricky analysis problems in the comments section.

Every week, we read news about failed analysis projects. If you listen carefully, you can hear the grunts, screams and curses of thousands of analysts all over the world about their analysis nightmares.

At Chandoo.org, we talk a lot about best practices for data analytics. So today, let’s peek in to the dark side and understand the mistakes that can turn your analysis project into a nightmare.

There are 3 parts in any analysis project

To understand these worst practices in analysis world, first let’s break analysis projects in to 3 parts.

• Requirements
• Data Structure
• Tools & Construction

Let’s deep dive in to each area of the analysis projects to see what can go wrong.

Over the weekend, I got an email from Mr. E, one of my students. Mr. E works at a police department in California and as part of his work, he was looking at calls received by police. Whenever police get a call for help, multiple teams can respond to the call and go to the location. All of these dispatches are recorded. So a single call can have several such dispatches. And Mr. E wanted to findout which team responded the first. The problem?

Finding the first responded team is tricky.

Today let’s take up this problem as a case study and understand various methods to solve it.  We are going to learn about writing better lookups, pivot tables, power pivot and optimization. Put on your helmets, cause this is going to be mind blowingly awesome.

So we moved to Wellington, New Zealand few weeks back (on 17th of July 2016, to be precise). After spending first 3 weeks in Jeff’s house and a hotel, we moved in to our rental home over the weekend (on 6th of August). Around the same time, the worst of Wellington winter waved welcome to us. We quickly learned how to stay warm indoors (layers, hot water bottles, rugs and more layers). Kids started going to school few days back and they are loving it. I bought a bike and managed to go out on few rides on the hilly roads of Wellington and found a strange for sale sign too.

Anyhow, Since we didn’t have internet connection until today, I thought I will start by sharing a few Excel links with you. Check them out to get your fix of spreadsheets.

Read on…

How would you analyze data when you have lots of it? That is the inspiration for this series.

Let’s meet our data – Finance Industry Consumer Complaints

As part of open data initiatives, US government & Consumer Financial Protection Bureau maintain a list of all consumer complaints made against financial institutions (banks, credit unions etc.) You can download this data from the catalog page here. I have obtained the data on 1st of February, 2016. The download has 513,824 records. Each row contains one complaint.

In this and next two parts of the series, we are going to analyze these half a million complaints to find insights.

Here is a tricky problem often faced by accountants and finance professionals: Let’s say you have 5 customers. Each of them need to pay you some money. Instead of paying the total amount in one go, they paid you in 30 small transactions. The total amount of these transactions matches how much they need to pay you. But you don’t know which customer paid which amounts. How would you reconcile the books?

If you match the transactions manually, it can take an eternity – after all there are more than 931 zillion combinations (5^30).

This is where solver can be handy. Solver can find optimal solution for problems like this before you finish your first cup of coffee.

Podcast: Play in new window | Download

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In the 30th session of Chandoo.org podcast, let’s learn how to uncover fraud in data.

What is in this session?

In the wake of hedge fund scams, accounting frauds and globalization, We, analysts are constantly second guessing every source of data. So how do you answer a simple question like, “am I being lied to?” while looking at a set of numbers your supplier has sent you.

That is our topic for this podcast session.

In this podcast, you will learn

• Quick announcements about 50 ways & 200k BRM
• Introduction to fraud detection
• 5 techniques for detecting fraud
  • Benford’s law
  • Auto correlation
  • Discontinuity at zero
  • Analysis of distribution
  • Learning systems & decision trees
• Implementing these techniques in Excel
• A word of caution

Who is the most consistent of all?

Imagine you are a category manager at a large e-commerce company. Your site offers various products, but you don’t really make these products. You list products made by other vendors on your site. Every day, these vendors would send you invoices for the amount of product they have sold. Above is a snapshot of such invoices.

Looking at this list, you have a few questions.

1. Who is the best seller?
2. Who is the most active seller?
3. Who is the most consistent seller?
4. Which seller has fewest invoices?

Let’s go ahead and answer these using Excel. Shall we?

Podcast: Play in new window | Download

Subscribe: Apple Podcasts | Spotify | RSS

In the 20th session of Chandoo.org podcast, lets save some time.

What is in this session?

We all want to save time and stay productive. The obvious answer seems like using keyboard shortcuts. But they can only get you so far. So what about the real productive strategies? That is what we address in this podcast.

In this podcast, you will learn,

• Announcements
• 5 key areas of business analyst work – tracking, analysis, reporting, data management & modeling
• Time saving strategies for tracking
• for analysis
• for reporting
• for data management
• for modeling
• Conclusions