# Single variable linear regression: Calculating baseline prediction, SSE, SST, R2 & RMSE:

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Introduction:

This post is focused on basic concepts in linear regression and I will share how to calculate baseline prediction, SSE, SST, R2 and RMSE for a single variable linear regression.

Dataset:

The following figure shows three data points and the best-fit regression line: y = 3x + 2.

The x-coordinate, or “x”, is our independent variable and the y-coordinate, or “y”, is our dependent variable.

Baseline Prediction:

Baseline prediction is just the average of values of dependent variables. So in this case:

(2 + 2 + 8) / 3 = 4

It won’t take into account the independent variables and just predict the same outcome. We’ll see in a minute why baseline prediction is important.

Here’s what the baseline model would look like:

SSE:

SSE stands for Sum of Squared errors.

Error is the difference between actual and predicted values.

So SSE in this case:

= (2 – 2)^2 + (2 – 5)^2 + (8 – 5)^2

= 0 + 9 + 9

= 18

SST:

SST stands for Total Sum of Squares.

Step 1 is to take the difference between Actual values and Baseline values of the dependent variables.

Step 2 is to Square them each and add them up.

So in this case:

= (2 – 4)^2 + (2 – 4)^2 + (8 – 4)^2

= 24

R2:

Now R2 is 1 – (SSE/SST)

So in this case:

= 1 – (18/24)

= 0.25

RMSE:

RMSE is Root mean squared error. It can be computed using:

Square Root of (SSE/N) where N is the # of dependent variables.

So in this case, it’s:

SQRT (18/3) = 2.44

# SQL: How to add Hierarchical Levels in your Query?

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Tree-like structures are common in our world: Company Hierarchy, File System on your computer, Product category map among others so you might run into a task of creating a Hierarchical level in your SQL query — In this blog post, I will show you how you can do that using couple of approaches. These approaches can also be used to map “parent – child” relationships.

Two approaches are:

1. When you know the Tree Depth
2. When you don’t know the Tree Depth

### #1: When you know tree-depth:

When you know the tree-depth (and if it’s not too deep) then you could consider simple CTE’s to come up with the Hierarchical Levels field in your query.

Let’s take an example:

Input:

 EmployeeID FirstName LastName Title ManagerID 1 Ken Sánchez Chief Executive Officer NULL 16 David Bradley Marketing Manager 273 273 Brian Welcker Vice President of Sales 1 274 Stephen Jiang North American Sales Manager 273 285 Syed Abbas Pacific Sales Manager 273

Query: (On SQL Server)

``` with lvl1 as (select [EmployeeID] ,[FirstName] ,[LastName] ,[Title] ,[ManagerID] ,1 as Level FROM [dbo].[employees] where ManagerID is null ) , lvl2 as ( select [EmployeeID] ,[FirstName] ,[LastName] ,[Title] ,[ManagerID] ,2 as Level FROM [dbo].[employees] where ManagerID IN (Select EmployeeID from lvl1) ), lvl3 as ( select [EmployeeID] ,[FirstName] ,[LastName] ,[Title] ,[ManagerID] ,3 as Level FROM [dbo].[employees] where ManagerID IN (Select EmployeeID from lvl2) ) select * from lvl1 union select * from lvl2 union select * from lvl3 ```

Output:

 EmployeeID FirstName LastName Title ManagerID Level 1 Ken Sánchez Chief Executive Officer NULL 1 273 Brian Welcker Vice President of Sales 1 2 16 David Bradley Marketing Manager 273 3 274 Stephen Jiang North American Sales Manager 273 3 285 Syed Abbas Pacific Sales Manager 273 3

### #2: When you do NOT know tree-depth:

In other words, if the tree is N-level deep then you are out of luck using option #1. In this case, you should consider the RECURSIVE CTE approach. Here’s an example:

Input: (with the idea that this table will grow over time)

 EmployeeID FirstName LastName Title ManagerID 1 Ken Sánchez Chief Executive Officer NULL 16 David Bradley Marketing Manager 273 23 Mary Gibson Marketing Specialist 16 273 Brian Welcker Vice President of Sales 1 274 Stephen Jiang North American Sales Manager 273 275 Michael Blythe Sales Representative 274 276 Linda Mitchell Sales Representative 274 285 Syed Abbas Pacific Sales Manager 273 286 Lynn Tsoflias Sales Representative 285

Query: (On SQL Server that supports Recursive CTE)

``` with HierarchyLvl as ( SELECT [EmployeeID] ,[FirstName] ,[LastName] ,[Title] ,[ManagerID] ,1 as Level FROM [dbo].[employees] where ManagerID is null UNION ALL SELECT e.[EmployeeID] ,e.[FirstName] ,e.[LastName] ,e.[Title] ,e.[ManagerID] ,Level + 1 FROM [dbo].[employees] e INNER JOIN HierarchyLvl d on e.ManagerID = d.EmployeeID ) select * from HierarchyLvl ```

Output:

 EmployeeID FirstName LastName Title ManagerID Level 1 Ken Sánchez Chief Executive Officer NULL 1 273 Brian Welcker Vice President of Sales 1 2 16 David Bradley Marketing Manager 273 3 274 Stephen Jiang North American Sales Manager 273 3 285 Syed Abbas Pacific Sales Manager 273 3 286 Lynn Tsoflias Sales Representative 285 4 275 Michael Blythe Sales Representative 274 4 276 Linda Mitchell Sales Representative 274 4 23 Mary Gibson Marketing Specialist 16 4

### Conclusion:

Even if I know tree-depth I will go with option #2 as it’s much easier to read and can accommodate future updates to the table. If you are interested in learning more about this and search for “Recursive Query using Common Table Expression” and you should find technical articles that talk about why it does what it does.

Hope this helps!

# Is the R data science course from datacamp worth the money?

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Question (on Quora) Is the R data science course from datacamp worth the money?

Answer:

It depends on your learning style.

If you like watching videos then coursera/udacity might be better.

If you like reading then a book/e-book might be better.

If you like hands-on then something like Data Camp is a great choice. I think they have monthly plans so it’s much cheaper to try them out. When I subscribed to it, it was like 30\$/Month or so. I found it was worth it. Also, if you want to see if “hands-on” is how you learn best. Try this: swirl: Learn R, in R. — it’s free! Also, Data Camp has a free course on R too so you could try that as well.

Also, if you want to have free unlimited access for 2-days then try this link: https://www.datacamp.com/invite/G8yVkTrwR3Khn

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# Data analytics vs. Data science vs. Business intelligence: what are the key differences/distinctions?

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They are used interchangeably since all of them involve working with data to find actionable insights. But I like to differentiate them based on the type of the question you’re asking:

• What:

What are my sales number for this quarter?

What is the profit for this year to date?

What are my sales number over the past 6 months?

What did the sales look like same quarter last year?

All of these questions are used to report on facts and tools that help you build data models and reports can be classified as “Business Intelligence” tools.

• Why:

Why is my sales number higher for this quarter compared to last quarter?

Why are we seeing increase in sales over the past 6 months?

Why are we seeing decrease in profit over the past 6 months?

Why does the profit this quarter less compared to same quarter last year?

All of these questions try to figure why something happened? A data analyst typically takes a stab at this. He might use existing Business Intelligence platform to pull data and/or also merge other data sets. He/she then applies data analysis techniques on the data to answer the “why” question and help business user get to the actionable insight.

• What’s next:

What will be my sales forecast for next year?

What will be our profit next year for Scenario A, B & C?

Which customers will cancel/churn next quarter?

Which new customers will convert to a high-value customer?

All of these questions try to “predict” what will happen next (based on historical data/patterns). Sometimes, you don’t know the questions in the first place so there’s a lot of pro-active thinking going on and usually a “data scientist” are doing that. Sometimes you start with a high level business problem and form “hypothesis” to drive your analysis. All of these can be classified under “data science”.

Now, as you can see as we progressed from What -> Why -> What’s next, the level of sophistication needed to do the analysis also increased. So you need a combination of people, process and technology platform in an organization to go from having a Business Intelligence maturity all the way to achieving data science capabilities.

Here’s a related blog post that I wrote on this a while back: Business Analytics Continuum: – Insight Extractor – Blog

..And you can check out other stuff I write about here: Insight Extractor – Blog – Paras Doshi’s Blog on Analytics, Data Science & Business Intelligence.

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# Where can I find a data analyst mentor, be it in-person or online?

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Find a mentor, where do I. Hmmmmmm….

There are few options. 1) Paid online courses with Mentoring 2) Free Options

#1, Paid online courses with mentoring.

I am a mentor for an ed-tech startup Springboard – Learn Data Science & UX Design online — it’s similar to what you are asking for. If you see value in that, you should check it out.

#2. Free options:

a. Quora: You could ask questions here and A2A — Build a network and someone may offer to mentor you offline

b. Mooc: You could join courses on MOOC’s like coursera and udacity — they have good forum support so you could use it for getting your questions answered

c. Cold email: There are lot of analytics/data-science professionals active in the community (linkedin groups, blogs, etc) and if you cold email them, you might find one!

d. local meetups: go to local meetups, meet people and find your mentor.

Stepping back, having a mentor helps and accelerates your progress – but not having one, shouldn’t stop you from achieving what you want.

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# Looker vs Tableau: How would you compare them in terms of price & capabilities?

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Someone asked this on quora so here’s my response: This is a great question — one that I figured it out when I led Analytics at Kiva[.]Org last year so I am happy to add my perspective here on Looker vs Tableau:

Let’s talk about capabilities first and then price.

### Capabilities — Looker vs Tableau

Even though both of these tools are classified under Business Intelligence, they have some pretty clear product differentiation so in this section, I will share that. I will share the three main components of Business Intelligence platforms and then map it back to core strengths of each product.

Business Intelligence platforms typically has three main components:

1. Data Collection, Storage & Access
2. Data Modeling
3. Data Visualization

#1) Data Collection, Storage & Access: Both of these tools don’t do data collection & storage. You will need infrastructure to collect data and store it — typically it is stored in databases. And you can access data from databases using SQL. You will need to connect to these data sources from either of these tools and access data — Note that: On the surface, it might look like Tableau supports more data sources than Looker but there might be workaround to get your data into one of the data sources supported by Looker and take it from there and so I am not awarding extra points to Tableau for this. Also, I am personally a big proponent of using Analytic databases like redshift, vertica, bigquery & Azure DW for Analytics applications which Looker & Tableau both support so calling it a tie here!

#2) Data Modeling: This is Looker’s core strength by a wide margin! Why? This is because of their LookML which is their data modeling layer and I am super impressed by this after using it for a while now! So let’s chat about what data modeling layer means and why you should care.

Data modeling (in this context) means creating data models that take your raw data as input and then it’s cleaned, combined, curated & converted and made ready for data analysis.

Why is this important? Not everyone can clean, curate, combine & convert raw data into analysis-friendly data assets. That’s what data analysts are trained and specialize in. May in the future we will have tools that do that OR maybe we will see plug-and-play (aka turnkey) solutions for few key analysis needs but for now, you need data analysts that can create these data models.

Now there are two ways to create data models:

You can create them on-the-fly (ad-hoc) OR you can publish all of these data models on a platform (like Looker).

There are all sorts of issues with doing it on-the-fly — it works for small teams (<20–30 people) but more than that you need to have some process in place. For instance: You can’t automate data models that you need often so that’s wasted time, Also, you can’t share these models easily with others, creates a single point of failure and if the analyst person is sick or on vacation then no-one gets “insights” from data — the world stops spinning. Yada Yada Yada…So self-service is good after you have few business users who want to consume data.

So what does a self-service platform bring to the table? They help data analyst build these wonderful data-analysis friendly models and publish them so everyone who cares in an org can access it. So the consumer can focus on analysis part and not worry about doing the not-so-good part of making it ready for analysis. Also, this ensures all sorts of other benefits: standardized metric definitions, trusted data sources, better collaboration among analysts, speedier model-delivery process, get out of excel hell and what not!

Think of this way: If you have all key data model available on your self-service platform then your data analyst can focus on 1) advance stuff = more \$\$\$ 2) building more data models (and so eventually they can do more advanced stuff later and more \$\$\$!)

This is where Looker fits in. Looker is great at this data modeling thing — it’s platform is amazing for anyone looking to solve this problem. You can also do data visualization on top and build dashboards.

Alright, moving on:

#3) Data Visualization: This is Tableau’s forte! No one does data viz better than Tableau, at least right now. There are vendors that are investing significant resources on this and they are close but still Tableau is a leader in this space.

Having said that, let’s map it back to how it help business users & analysts:

Business users and self-service environments:

Tableau is not great at data modeling thing. Yes, you can do basic clean, combine, curate & convert thingy but it doesn’t work well with intermediate to advanced needs. So if you have a self-service data modeling layer already in place that Tableau can connect to and you are looking for a data visualization layer then go for Tableau! You would be able to create some amazing visuals, dashboards and stories that will WOW your business users! But to make sure this scales you need to seriously think about 1) how to overcome the limitations in tableau’s data modeling layer OR 2) use some other tool to build this data modeling layer and connect Tableau to it.

Pro Tip: I highly recommend trying out trials of these products and seeing what works best!

Analysts:

Tableau shines at data discovery! While this certainly helps business users, it’s best leveraged by analyst because whenever they are working on ad-hoc data analysis (one-time, strategic in nature) projects they can be much more effective and discover the underlying trends and patterns in their data by visualizing it using Tableau.

So with that context you might be wondering, What tool did I champion & Implement at Kiva?

This is public knowledge that Kiva is a Looker customer because it’s Logo is on their website so I can share this.

After evaluating about 30+ tools (including Tableau), I ended up championing and eventually leading the initial implementation sprints to implement Looker at Kiva because the goals & vision that we had for Kiva’s data & analytics platform aligned better with having the data modeling layer that met Kiva’s needs. So you need to figure out your goals and vision and then choose the tools with that framework.

Pro Tip #1: It’s insanely hard to figure out what your goals and vision for analytics in an org. To figure this out, you might want to chat with organizations in the same industry at the same size & stage and see what they use. Ask them about what they use and whether it worked for them. Ask them about their Return on investment. This is a great way to get external feedback but you still need to figure out internal needs and prioritize them.

Pro Tip #2: Both of these tools have amazing reviews! You will see them highly ranked in analyst reports too — this is great but it’s important that ever before to clearly define what your organization needs and then map it back to the core strengths of these products (or any other tool for that matter) and go from there!

[I am happy to help evaluate the right tool for you needs, feel free to contact me: Let’s Connect! – Insight Extractor – Blog ]

### Pricing — Looker vs Tableau

I can’t talk about Looker’s pricing because it’s not public, I apologize! You need to contact them to get the quote.

However, you can anchor that with Tableau’s pricing which is public: Buy Tableau | Tableau Webstore

Your analyst and power users will need Tableau Desktop/Professional which is \$1K and \$2K respectively (one-time thing) and then depending on your deployment model: cloud or self-hosted — the price varies:

*Note that Tableau online is a subscription model so you can definitely start small. Let’s say 5 business users in a department and take it from there. If you grow then you can later look at other tools like Looker. (If you are rapidly growing, account for the non-trivial time needed to migrate from one platform to another and so it might be worth it to pick the right tool from the get-go)

Pro Tip: I will encourage you to think about building a ROI model too. You know use some analytics for your analytics projects 😉 — I apologize, couldn’t resist! Anyhow, the point is that instead of just thinking about the “cost”, think about the value-add and anchor your investment figure to that. There’s a reason some analytics tool are priced at let’s say \$1000 vs some tools priced at \$100,000 — both of them have different value proposition and if you know how to extract value of the tool and can project it then you can get better ROI!

Hope that helps! If I can be of any further help, email me or comment here! Let’s Connect! – Insight Extractor – Blog

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