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Pandas provide tools and techniques to make data analysis easier in Python

We’ll discuss tips and tricks that will help you become a better and efficient analyst


Efficiency has become a key ingredient for the timely completion of work. One is not expected to spend more than a reasonable amount of time to get things done. Especially when the task involves basic coding. One such area where data scientists are expected to be the fastest is when using the Pandas library in Python.

Pandas is an open-source package. It helps to perform data analysis and data manipulation in Python language. Additionally, it provides us with fast and flexible data structures that make it easy to work with Relational and structured data.

If you’re new to Pandas then go ahead and enroll in this free course. It will guide you through all the in’s and out’s of this wonderful Python library. And set you up for your data analysis journey. This is the sixth part of my Data Science hacks, tips, and tricks series. I highly recommend going through the previous articles to become a more efficient data scientist or analyst.

I have also converted my learning into a free course that you can check out:

Also, if you have your own Data Science hacks, tips, and tricks, you can share it with the open community on this GitHub repository: Data Science hacks, tips and tricks on GitHub.

Table of Contents

Pandas Hack #1 – Conditional Selection of Rows

Pandas Hack #2 – Binning of data

Pandas Hack #3 – Grouping Data

Pandas Hack #4 – Pandas mapping

Pandas Hack #5 – Conditional Formatting Pandas DataFrame

Pandas Hack #1 – Conditional Selection of Rows

To begin with, data exploration is an integral step in finding out the properties of a dataset. Pandas provide a quick and easy way to perform all sorts of analysis. One such important analysis is the conditional selection of rows or filtering of data.

The conditional selection of rows can be based on a single condition or multiple conditions in a single statement separated by logical operators.

For example, I’m taking up a dataset on loan prediction. You can check out the dataset here.

We are going to select the rows of customers who haven’t graduated and have an income of less than 5400. Let us see how do we perform it.

Note: Remember to put each of the conditions inside the parenthesis. Else you’ll set yourself up for an error.

Try this code out in the live coding window below.

Pandas Hack #2 – Binning of data

The data can be of 2 types – Continuous and categorical depending on the requirement of our analysis. Sometimes we do not require the exact value present in our continuous variable. But the group it belongs to. This is where Binning comes into play.

For instance, you have a continuous variable in your data – age. But you require an age group for your analysis such as – child, teenager, adult, senior citizen. Indeed, Binning is perfect to solve our problem here.

To perform binning, we use the cut() function. This useful for going from a continuous variable to a categorical variable.

Let us check out the video to get a better idea!

Pandas Hack #3 – Grouping Data

This operation is frequently performed in the daily lives of data scientists and analysts. Pandas provide an essential function to perform grouping of data which is Groupby.

The Groupby operation involves the splitting of an object based on certain conditions, applying a function, and then combining the results.

Let us again take the loan prediction dataset, say I want to look at the average loan amount given to the people from different property areas such as Rural, Semiurban, and Urban. Take a moment to understand this problem statement and think about how can you solve it.

Well, pandas groupby can solve this problem very efficiently. Firstly we split the data according to the property area. Secondly, we apply the mean() function to each of the categories. Finally we combine it all together and print it as a new dataframe.

Pandas Hack #4 – Pandas mapping

This is yet another important operation that provides high flexibility and practical applications.

Pandas map() is used for mapping each value in a series to some other value-based according to an input correspondence. In fact, this input may be a Series, Dictionary, or even a function.

Note – Map is defined on Series only.

Pandas Hack #5 – Conditional Formatting Pandas DataFrame

This is one of my favorite Pandas Hacks. This hack provides me with the power to pinpoint the data visually which follows a certain condition.

You can use the Pandas style property to apply conditional formatting to your data frame. In fact, Conditional Formatting is the operation in which you apply visual styling to the dataframe based on some condition.

While Pandas provides an abundant number of operations, I’m going to show you a simple one here. For example, we have the sales data corresponding to each of the respective salespeople. I want to highlight the sales values as green that is higher than 80.

Note – We have applied the apply map function here since we want to apply our style function elementwise.

End Notes

To summarize, in this article, we covered seven useful Pandas hacks, tips, and tricks across various pandas modules and functions. I hope these hacks will help you with day-to-day niche tasks and save you a lot of time. In case you are completely new to python, I highly recommend this free course-


You're reading 5 Striking Pandas Tips And Tricks For Analysts And Data Scientists

Must For Data Scientists & Analysts: Brain Training For Analytical Thinking


Let’s start this article with a small exercise. Take a pen and paper and write the answer as it comes to your mind. No thinking twice and you shouldn’t take more than 15 seconds to do it.

On this paper, please write the answer to “What are the skills required to become a successful data scientist?”

A lot of you would have written coding, knowledge of analytics tools, statistics etc. All of these are definitely required to be a successful data scientist, but they are not sufficient.

One of the most important skill differentiating a good analyst / data scientist from the bad one is the ability to take complex problems, put a framework around it, make simplifying assumptions, analyze the problem and then come up with solutions. And analytics tools are just a medium to do so.

In today’s article we will take a case study and see this process of problem solving in structured manner.

What you’ll learn ?

Here you’ll find practice problems to train your brain think analytically while solving complex problems. This brain training will not only introduce you to a new approach to solve problems but will also help you to think faster while dealing with numbers!

My previous article on how to train your mind for analytical thinking? should give you a good head start.

Practice Problem

Here’s is my daily routine:

I get ready and leave home for office at sharp 10:30 AM every working day. Considering the amount of work I got to finish on some days, I try to reach early by driving faster than other days (obviously in safe limits).

However, since last 5 days, I’ve observed that I reach office almost at the same time, irrespective of my average speed between traffic lights. This makes me wonder, whether the time taken from my home to office is dependent on my velocity or not? In other words, the total average velocity adjusted by the traffic lights to the same level, and does not depend on the velocity we drive the car!

Take the Test: Should I become a Data Scientist ?

To explain you better, consider a simplistic scenario:

Two cars start from point A which is the first traffic signal. Point B is a traffic signal with a halt time of 60 sec and drive time of 20 sec. The distance between A and B is 600m. Car1 starts at 5m/sec and Car2 starts at 6m/sec. Who will cross the traffic light first? Here are the assumptions:

1. Traffic lights are configured for average speeds, it becomes green 120 seconds (600 m / 5 m/sec) after the first signal turns green.

2. Traffic lights are green for 20 seconds and red for 60 seconds (20 * 3)

Assume both cars start at 0 sec.

Time taken for Car1 to reaches signal B = 600/6 = 100sec Time taken for Car2 to reaches signal B = 600/5 = 120sec Light is green at (40,60) ; (120,140) ; (200,220) ; (280,300)

Hence, cars reaching point B in 61 sec and one reaching at 140 second show no difference in terms of passing through the second signal. Let’s calculate the min and max speeds which will show no difference amongst the two lights scenario :

Minimum speed = 600m / 120sec = 5 m/sec = 18 km/hr Minimum speed = 600m / 61sec = 9.8m/sec= 35 km/hr

It does not matter whether you drive at 18 km/hr or 35 km/hr in this scenario, you will cross the second signal (B) at the same time. In general, it is difficult to drive in such wide range of speeds in peak time traffic and hence my concerns looks logical now. I probably have no control on the time I will take to reach office (obviously this is over simplification of the problem).

Let’s make it more complex…!

Now we have 4 signals A,B,C and D. Same two cars start from A at the time 0 sec. Distances between AB , BC and CD are same. The question is now, who will cross the signal D first.

Without going into mathematics, the answer is very straight forward.  If both will cross B at the same time, A – B pair is the same as B-C pair which is in turn same as C-D pair. Hence both the car will cross D at the same time. The scenario is actually more extreme, the car which maintains an average speed of 18 km/hr and the one at 35 km/hr will cross D at the same time. This further strengthens my hypothesis.

Question again boils down to :

“Am I just a helpless puppet in traffic police’s hand while driving to my office ? “

Let’s try to generalize it into a parametric equation

Actual scenario is too difficult to generalize in this article, so let’s ground a few assumptions :

1. Traffic lights turn green for time t sec and becomes red for time 3t sec

2. Average speed of a vehicle on road is v m/sec

3. The challenger to the average vehicle drives at a velocity x times v m/sec

By now, we already know, it hardly matters if we solve for one pair of traffic light or more. If the faster driver is able to sneak through the traffic light in a green signal before the average vehicle, it will make a difference or else not.

Hence, the difference in time required to make this happen will be 3t. Following is the final equation we are solving for :

Time taken by average vehicle : l/v sec

Time taken by faster vehicle : l/vx sec

It simplifies to ;

Given x , v, l and t are all positive, this can be further simplified to :

Here is a JACKPOT! We know that l is always positive, hence to make the above equation practical, both x and (l – 3tv) have to be positive. This means if 3tv becomes more than l, you have no chance of beating traffic lights. For instance, if t = 30 sec, v = 5 m/sec and l = 145 m, you simply cannot beat the odds, even if you ride on speed of GUN shot!

Let’s assume a few parameters and understand the equation further:

Say, l = 600 m. The equation becomes :

So, here are a few thumb rules to make it possible to beat the Traffic signals :

1. Minimize t (cycle of traffic light) : It is possible to beat traffic light in quick traffic localities where it turns Green – Red in quick time.

2. Minimize v (the average velocity of the road ) :  If the average velocity on road is exceptionally low, we can beat these slow drivers if we drive fast (Duh!)

3. Maximize x (Faster multiplier) : If we drive super fast, we can still win the race. But notice if v*t becomes more than 200, you have no chance of getting

Don’t miss: Introducing the art of structured thinking and analyzing

Let’s try to visualize a few relations

Average t in Bangalore is about 20 seconds and average speed is 5m/sec. Hence the equation becomes :

As seen from the above graph, if x and l are high enough to fall into the shaded region, we have a chance to beat the traffic light.

Let’s summarize our findings

1. There is no point of driving fast on a lane where 3 * Green light time * average velocity is more than the length of the road.

2. Beating traffic is possible if following are in our favor :

a. High x . We drive really fast (not a safe option)

b. High l. For instance driving fast on a highway makes sense

c. Low t : No point of driving fast on a high timer traffic signals road

d. Low v : If the average velocity on the road is really low, we can beat them. We already knew that!

End Notes

I hope, you enjoyed solving this traffic problem. I’m sure it would have challenged your thinking which was our motive. Right ?

In this article, using a case of traffic light and some elementary physics concepts I have explained the necessary skill required to build a unshakable foundation to become a data scientist.

Did you enjoy reading this article? Have you wondered over this question before? Do you think you can improvise these calculations further to make it more realistic?

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.


Netflix Tips And Tricks For Iphone, Ipad, And Mac

Netflix is undoubtedly one of the best streaming services that you can enjoy anywhere, anytime. Recently, we wrote about Apps for Netflix that will add a spark to your viewing experience. In addition, we also have an ever-updating list of movies on Netflix to get everything on one page.

Anyways, it’s no mystery that we all struggle to find content that we like on Netflix and that’s just because Netflix has so much to offer. To reduce the surfing and utilize your maximum time in watching the shows you like, below are some handy Netflix tips and tricks that will surely enhance your viewing experience.

Netflix Tips and Tricks for iPhone, iPad, and Mac #1. Like and Dislike feature

Netflix changed their rating system in 2023, and maybe it was a good move. Users can now rate content with Thumbs Up/Down. Make sure you give your thumb up or down if you like or dislike any show or movie.

This will enable Netflix algorithm to sort your feed accordingly. Whether you believe it or not, these thumbs up and down does matter to you. After giving specific numbers of feedbacks, Netflix algorithms start their work and show you shows and movies relevant to the content you have liked.

It’s a general behavior to quit the app once we have completed watching, but next time make sure you do give your thumb to the content you watched.

#2. Profiling

A simple solution to this problem is to create a different profile for different people. Doing so will sort recommendations to respective profile. Combine profiling with thumbs up/down, and you’ll see perfect recommendations suiting your preferences.

#3. Manually making the list

No one understands you better than yourself, not even Netflix’s algorithms. Keeping that thought in mind, Netflix has an option where you can make your list, and it will show you just that. Also, any changes you make in the list will sync across all devices, meaning you won’t need to the hard work again.

#4. Delete viewing history

As mentioned above, most of us share our Netflix with family and friends and the viewing history is visible to everyone. If you care about your privacy, you may be looking for a way to remove this history so other won’t find out what you are watching.

Just in case if you fail to find what exactly you are looking for, it is the time to invoke the lord, the Netflix itself.

You can feed in three title request at a time.

Of course, there’s no guarantee your request will be fulfilled, but at least there’s a ray of hope.

#6. Play the Spin Game

It will randomly select a show/movie to help you decide what to watch and save your time searching. It’s a third-party free service, which also allows you to select shows from Prime, Hulu, Netflix, HBO, etc.

#7. Netflix Keyboard Shortcuts for Mac

If Mac is your primary device for watching shows and movies, mastering the keyboard shortcuts is a must. Below are some handy ones for your help:

Press “F” to quickly have a full-screen mode and to exit press “Esc.”

Use “Spacebar” to Play/Pause easily

To fast forward movie or show, press “Shift + Right Arrow” and “Shift + Left Arrow” to rewind

You can use “M” key to mute or unmute quickly

To increase or decrease the volume, press Shift + Up arrow or Shift + Down arrow respectively

#8. Shortcuts for iPhone and iPad

There aren’t many shortcuts for iPhone or iPad, but one of the handiest one is fast forward and rewinding using the screen taps.

Just double tap on the right side of the screen while watching on Netflix to fast-forward and double tapping on the left will rewind.

#9. Subtitle Customization

Watching shows or movies in a language that isn’t your primary language is a real tough job. There are subtitles available, but at times they get mixed up with the background color of the movie or show you are watching and maybe you miss the most important line.

#10. Turn off autoplay

You can check/uncheck autoplay option to turn it on/off.

Besides, you can also customize the default quality on this page. If you are viewing Netflix on data, switching to medium or low quality is recommended.

#11. Chrome Extensions for Netflix FindFlix Extension

Install FindFlix

Netflix Party Extension

Let love be in the air, especially if you are in a long distance relationship. With this extension you can sync video playback with friends or that special one; also it adds a group chat option to shares your heart out while watching. It’s free and light on the browser and perfectly does the job for what it is made for.

Install Netflix Party

Netflix Enhancer

No need to open a number of tabs on your browser just find the IMDb ratings or information about the title you are viewing. This one extension does exactly that. It shows you relevant information about the title you are viewing along with IMDb ratings and trailers in same tabs.

Install Netflix Enhancer

#12. Download feature

Alternately, you can swipe right to left and tap on Delete button.

#14. Create your Flixtape

By visiting chúng tôi you can create your own Flixtape. Flixtape is customized playlist made from a theme, or feeling, or mood. Also, you can also share it with your friends and family. To add a little spice, you can even customize the cover of your Flixtape. It’s free and worth a try.

#15. Become Netflix Beta Tester

If you are madly in love with Netflix and would like to have your hands on the new features before anyone else, you may consider getting yourself enrolled as a Test Participant.

To do so, just head over to Accounts → Test Participation and turn ON the switch.

That’s all folks!

Video: Best Netflix Tips, Ticks, and Hacks

Signing Off

You may also like to refer:

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The founder of iGeeksBlog, Dhvanesh, is an Apple aficionado, who cannot stand even a slight innuendo about Apple products. He dons the cap of editor-in-chief to make sure that articles match the quality standard before they are published.

Jupyter Notebook Tips And Tricks

Here is a list of useful Jupyter Notebook tips and tricks. The list is in no particular order.

1. Shell Commands

Do you exit your notebook to run a shell command?

You can run shell commands in your Jupyter Notebook by placing an exclamation mark before a command.

For example:

!pip install Tkinter 2. View a List of Shortcuts

Learning useful shortcut keys for working with Jupyter Notebooks can streamline your workflow over time.

The list of shortcuts is huge and there is no way to remember everything at once. This is where viewing a list of shortcuts comes in handy:

Open up a Jupyter Notebook.

Activate the command mode (press Esc).

Press the H key.

See the list of all the shortcuts.

3. Magic Commands

In Jupyter, there are a bunch of magic commands to make your life easier.

A magic command is a shortcut to solve common problems, such as listing all the files in the current directory.

A magic command is useful, as it can be embedded directly into Python code. A magic command has a % prefix.

Here are a bunch of useful magic commands:

# Print the current working directory %pwd # Show all the files in the current directory %ls # Change the working directory %ls [PATH_TO_DIR] # List all the variables %Who View all the magic commands

The list of useful magic commands is long. View the list of all the available magic commands with:

%lsmagic Help with magic commands

To get more info about a specific magic command, highlight it and press Shift + Tab:

4. Measure Cell Execution Time

Use %%time to get the elapsed time of running a cell of code.

5. Add Multiple Cursors

Save time when editing code by working with multiple cursors.

6. Set Alarm for Program Completion

Ring an alarm when your program has completed execution.


On Windows, you can produce a beeping alarm. For example, let’s set an alarm of 440HZ for one second (1000ms):

import winsound duration = 1000 freq = 440 winsound.Beep(freq, duration) Mac

Instead of a beeping alarm, you can use the built-in say command to make your Mac say something when your program completes:

import os os.system('say "Your program has now finished"') 7. Extensions to Jupyter Notebooks

Jupyter Notebook is a great tool, but a bare notebook lacks useful features. This is where the extensions kick in.

Run the following command in the command line to install the extensions:

pip install jupyter_contrib_nbextensions && jupyter contrib nbextension install

Then start a Jupyter Notebook and go to the “Nbextensions” tab:

8. View the Documentation of a Method

To view the documentation of a method, highlight the method and press Shift + Tab. To further expand the modal, press the + button on the top right to expand the modal:

9. Extend the Number of Columns and Rows Shown in pandas

A pandas table can only show a limited number of rows and columns. However, you can change that.

For instance, let’s set the max output rows and columns to 1000:

import pandas as pd 10. Hide Unnecessary Output

Use a semicolon at the end of a statement to suppress an annoying output. For example, when plotting with Matplotlib, you see a somewhat redundant output before the graph:

To get rid of this, use a semicolon after the plotting statement:


Looks better, doesn’t it?


That’s it for the list. I hope I was able to increase your productivity and save your time with these Jupyter notebook tips.

Thanks for reading. Happy coding!

Further Reading

50 Python Interview Questions and Answers

50+ Buzzwords of Web Development

Make Windows Easier To Use: 5 Helpful Tips And Tricks

With its nested file system and hundreds of menus, functions, and folders, Windows is a highly complex organism. All your installed applications add yet another layer of headaches. If you want to work quickly and efficiently within Windows, you’ll need to optimize it to fit your way of working. Fortunately, Microsoft’s operating system is by no means rigid—on the contrary, it can be quite adaptable. This applies not only to the design, but also to basic operation.

Enter this guide. We’ll show you a handful of tricks, hidden functions, and additional software that will make your everyday Windows PC life much easier. For even more ease-of-use help, be sure to check out PCWorld’s guides on how to make Windows 11 look like Windows 10 and 8 ways to ease eye strain in Windows.

This article was translated from German to English, and originally appeared on

Organize your Windows desktop

Load up the Windows taskbar

Make Windows easier to actually use

You can simply enlarge the desktop icons by pressing the Ctrl key while the desktop is active and turning the mouse wheel. The mouse pointer can also be enlarged; you’ll find the settings by searching for “Change mouse pointer size.” That said, the universal tool for reading small, poorly visible text and viewing details is called Magnifier. Make sure that Smooth image and text edges is activated in the options that appear.

For people who have difficulty distinguishing individual colors, Windows offers a range of color filters.

Sam Singleton

People who have difficulty distinguishing between individual colors and have a red-green deficiency, for example, will find a way to adjust the Windows display accordingly under Color filters. Under Contrast Designs, on the other hand, you can set the desktop to maximum contrast.

For people with hearing problems, Windows offers to translate system sounds into graphic signals. You can turn on “Show audio alerts visually” in the Hearing section of the Ease of Access settings.

Adapt File Explorer for fast everyday work

In the browser settings of Windows you will find a hidden option that activates autocomplete for quick folder searches in Explorer.

You can also work faster with File Explorer if you switch on the auto-complete function. It’s located in a place where you would never expect it—in the settings of Internet Explorer. Yes, seriously. Although IE is no longer included in Windows 11, the settings still exist and now refer to the more modern Edge browser. One of the options also controls the auto-complete function of Explorer.

File Explorer’s search function can also be improved. It already offers dozens of options to search for files and can also search for file contents in Word documents, for example. However, it’s slow and awkward to use. The superb Everything works considerably faster. This long-beloved freeware does not have a desktop search function, so it cannot look inside files. When searching for file names, however, it delivers suitable results almost immediately, after performing an initial index when you set up the program.

Microsoft traditionally relies on the single-window technique for Windows Explorer, which makes copying and moving files unnecessarily cumbersome and confusing. Sure, it’s true that you can open another Explorer window without any problems, or use Windows 11’s new (sadly inadequate) Explorer tabs. But it’s better to use third-party tools that feature two windows right from the start, such as Double Commander. This file manager also offers other helpful functions such as tabs for quickly switching between drives and a viewer for different file types.

Activate more mouse functions with manufacturer software

Many modern computer mice have more than just two buttons and a scroll wheel. Special gaming models offer seven or eight buttons that can be assigned different functions or macros. But mice for office workstations also often have four or more buttons. You can assign predefined functions or a key combination to the keys using the driver software provided by the mouse’s manufacturer. Using these tools, many Windows can be carried out more quickly and easily.

With the Microsoft Mouse and Keyboard Center, you can assign functions, macros, and key combinations to the keys of your mouse.


Microsoft provides the Mouse and Keyboard Center for its mice. With this software, you can, for example, show and hide the Windows desktop, call up the Start menu and the Settings app, or execute a macro at the touch of a button. Logitech’s counterpart is called Logitech Options. Be aware, though, that both programs usually only work with mice from the respective manufacturer.

Top Tips For Citizen Data Scientists To Become Experts In Big Data

Here’s how citizen data scientists can become well versed in big data

With data scientists regularly topping the charts as one of the most in-demand roles globally, many organizations are increasingly turning to non-traditional employees to help make sense of their most valuable asset: data. These so-called citizen data scientists, typically self-taught specialists in any given field with a penchant for analysis, are likewise becoming champions for important projects with business-defining impact. They’re often leading the charge when it comes to the global adoption of machine learning (ML) and artificial intelligence (AI), for example, and can arm senior leaders with the intelligence needed to navigate business disruption. Chances are you’ve seen several articles from industry luminaries and analysts talking about how important these roles are for the future. But seemingly every opinion piece overlooks the most crucial challenge facing citizen data scientists today: collecting better data. The most pressing concern is not about tooling or using R or Python2 but, instead, something more foundational. By neglecting to address data collection and preparation, many citizen data scientists do not have the most basic building blocks needed to accomplish their goals. And without better data, it becomes much more challenging to turn potentially great ideas into tangible business outcomes in a simple, repeatable, and cost-efficient way. When it comes to how machine learning models are operationalized (or not), otherwise known as the path to deployment, we see the same three patterns crop up repeatedly. Often, success is determined by the quality of the data collected and how difficult it is to set up and maintain these models. The first category occurs in data-savvy companies where the business identifies a machine learning requirement. A team of engineers and data scientists is assembled to get started, and these teams spend extraordinary amounts of time building data pipelines, creating training data sets, moving and transforming data, building models, and eventually deploying the model into production. This process typically takes six to 12 months. It is expensive to operationalize, fragile to maintain, and difficult to evolve. The second category is where a citizen data scientist creates a prototype ML model. This model is often the result of a moment of inspiration, insight, or even an intuitive hunch. The model shows some encouraging results, and it is proposed to the business. The problem is that to get this prototype model into production requires all the painful steps highlighted in the first category. Unless the model shows something extraordinary, it is put on a backlog and is rarely seen again. The last, and perhaps the most demoralizing category of all, are those ideas that never even get explored because of roadblocks that make it difficult, if not impossible, to operationalize. This category has all sorts of nuances, some of which are not at all obvious. For example, consider the data scientist who wants features in their model that reflect certain behaviors of visitors on their website or mobile application. But of course, IT has other priorities, so unless the citizen data scientist can persuade the IT department that their project should rise to the top of their list, it’s not uncommon for such projects to face months of delays — assuming IT is willing to make the change in the first place. With that in mind, technology that lowers the bar for experimentation increases accessibility (with appropriate guardrails) and ultimately, democratizes data science is worth consideration. And companies should do everything they can to remove roadblocks that prevent data scientists from creating data models in a time-efficient and scalable way, including adopting CDPs to streamline data collection and storage. But it’s up to the chief information officers and those tasked with implementing CDPs to ensure that the technology meets expectations. Otherwise, data scientists (citizen or otherwise) may continue to lack the building blocks they need to be effective. First and foremost, in these considerations, data collection needs to be automated and tagless. Because understanding visitor behaviors via tagging is effectively coding in disguise. Citizen data scientist experimentation is severely hampered when IT has to get involved in code changes to data layers. And while IT can and should be involved from a governance perspective, the key is that citizens data scientists must have automated collection systems in place that are both flexible and scalable. Second, identity is the glue in which data scientists can piece together disparate information streams for organizations to find true value. Thankfully, organizations have a myriad of identifiers about their customers to reference, including email addresses, usernames, and account numbers. And identity graphs can help organizations create order from the chaos so that it becomes possible to identify visitors in real-time, making these features essential for analyzing user behavior across devices.

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