Data science barely uses statistical science and techniques.
He goes on to clarify:
The truth is actually more nuanced …
And in the ensuing post he lists a series of new statistical concepts that are frequently useful in data science, followed by a series of old statistical concepts that are also often useful.
But then he follows up with this sentence:
From a typical 600-pages textbook on statistics, about 20 pages are relevant to data science, and these 20 pages can be compressed in 0.25 page.
Granville’s post is worth a read, as he goes into some reasons why old-school statistics proper is increasingly less useful in the world most of us live and work in, while machine learning techniques are becoming much more useful.
And I will add: Granville’s post, while framed somewhat controversially, fairly well summarizes the realities of data science work. There are several statistical concepts that are indeed useful when doing this work. But we have powerful software tools ready at hand to do much of that work — often using new techniques that yield better predictive results than older statistical approaches do.
Yes, we still often need to understand the meaning and implications of a range of statistical insights in relation to our data. But we can get lots of great work done with an intuitional understanding of those concepts. Thus, we can start with statistical fundamentals, use them as needed, and then expand our knowledge when the situation calls for it.
One last sentence from Granville summarizes this data-sciencey attitude toward stats:
I believe that you can explain the concept of random variable and distribution (at least what you need to understand to practice data science) in about 4 lines, rather than 150 pages. The idea is to explain it in plain English with a few examples.
In my view, a work like this does a great job of bridging the gap for those who are coming to data science from a variety of fields.
So there you are. Obviously there are many opportunities for disagreement on these points. But the fact remains, data science uses statistics differently than many other sciences. And there are a huge range of things that we need to know and be good at that lie outside of statistics proper.
That’s it for now.
If time and occasion allow, I’ll circle back and beef this up later.
Great visual analytics involves a sequence of steps which may be understood as both a science and an art. That sequence includes:
Understanding the business objectives. These drive and guide the analysis and provide it point and purpose.
Spending the time required to understand the data inside and out. (Exploratory analysis.)
Identifying and curating the most important insights, to prepare for explanatory analysis.
Refining the design for clear, effective, and efficient communication, reducing clutter and highlighting key data points.
Providing visual hierarchy, to draw attention to first things first, second things second, and so on.
Structuring the report to provide the right mix of breadth and depth — breadth so that the stakeholders can see the big picture, and depth so that they can’t miss what’s most important.
When the occasion calls, leveraging elements of a good narrative, to lead the audience along a progression of steps from attention to recognition to engagement and finally to action.
Each of these steps merits its own extended discussion. In this post, I’d like to draw attention to the seventh step: leveraging elements of good narrative to lead the audience to action.
To my knowledge, no one has discussed this more effectively than Cole Nussbaumer Knaflic. Her book, Storytelling with Data, is the leading book on the topic, hitting many of the preceding steps, and then driving on to leverage the power of narrative in the presentation of the story.
Among the narrative elements she discusses are:
Establishing the setting, as a reminder of what we’re doing here and the shared goals we have.
Highlighting the problem as a tension between current obstacles and desired outcomes.
Viewing your audience as protagonists, whose actions will drive the story forward.
Taking a role in the story yourself, recommending possible courses of action, provoking your participants to engagement and leading toward resolution — i.e., data-driven action.
Her irrefutable point: You may have the best, most insightful, most beautifully designed analysis. But if you fail to effectively communicate that analysis, its sum total value is exactly ZERO. For at the end of the day, the sole point and purpose of analysis is to inform and generate action.
This is where Knaflic’s work is so valuable. If you’re short on time to read a book, Knaflic has presented this seventh point in the form of an entertaining and informative short video. Indeed, in this video she takes the discussion a step further to discuss the transformative power of the narrative arc:
Climax / Tension
Resolution / Ending
Only 15 minutes in length, her presentation is work of art.
Properly received, her presentation should provoke you, as a data professional, to put the lesson into practice. May your future presentations be more focused, more meaningful, and much more effective at inspiring data-driven action.
If you’re fairly new to Tableau, chances are you’ll find Tableau’s repository of free training videos (free with registration) to be very helpful. Indeed, there’s enough there to help you go from zero to serious just about as fast as you dare to do it. The tutorials are really pretty great.
BUT their organization scheme needs a little help.
Most everyone realizes that statistics and data science share a lot in common. Sometimes it is helpful to understand the differences. While it’s true that data science can’t be done without statistics, it is also true that data science involves a great deal more — statistics plays a significant part in data science’s much larger undertaking.
I intend to update and expand on this post over time. But for now allow me to point to a helpful post that develops this point — and begins to clarify the nature of data science’s “larger undertaking.”
The Difference Between Statistics and Machine Learning
In his post, The Actual Difference Between Statistics and Machine Learning, Matthew Stewart helpfully explains how statistics differs from another key part of the data science toolkit: machine learning. Data science is still a larger than machine learning. But it’s very appropriate to say something very similar about the relationship between the two as we said above: Data science can’t be done without machine learning.
Both statistics and machine learning are part and parcel of the data science toolkit. And each plays a somewhat different role. Explaining the difference is helpful.
Stewart summarizes the difference like this:
Statistical modeling aims first and foremost for understanding and explaining relationships between variables. Predictive power is a secondary consideration.
Machine learning aims first and foremost for effective prediction. Some machine learning algorithms are easy to interpret, and some are not.
Thus, if you are writing a scientific paper that needs to explain the relationships between variables, statistical modeling is probably the best route.
However, if the point of your work is to produce actionable results that translate into greater efficiency and effectiveness achieving the mission of your organization — machine learning is often the better route.
In Stewart’s own words:
Machine learning is all about results, it is likely working in a company where your worth is characterized solely by your performance. Whereas, statistical modeling is more about finding relationships between variables and the significance of those relationships, whilst also catering for prediction.
He goes further to develop a helpful analogy:
By day, I am an environmental scientist and I work primarily with sensor data. If I am trying to prove that a sensor is able to respond to a certain kind of stimuli (such as a concentration of a gas), then I would use a statistical model to determine whether the signal response is statistically significant. I would try to understand this relationship and test for its repeatability so that I can accurately characterize the sensor response and make inferences based on this data. Some things I might test are whether the response is, in fact, linear, whether the response can be attributed to the gas concentration and not random noise in the sensor, etc.
Statistical analysis is great in such a case. It’s the right tool for the job.
But what if the nature of the problem is slightly different, and the goals are different?
In contrast, I can also get an array of 20 different sensors, and I can use this to try and predict the response of my newly characterized sensor. This may seem a bit strange if you do not know much about sensors, but this is currently an important area of environmental science. A model with 20 different variables predicting the outcome of my sensor is clearly all about prediction, and I do not expect it to be particularly interpretable. This model would likely be something a bit more esoteric like a neural network due to non-linearities arising from chemical kinetics and the relationship between physical variables and gas concentrations. I would like the model to make sense, but as long as I can make accurate predictions I would be pretty happy.
This nails it home nicely. In the case of machine learning, our interest is in the results: How can we make the most accurate predictions? And moreover, do these predictions yield benefits for the mission of our organization?
Perhaps said otherwise, statistics is more about understanding — helping to answer the question, What’s really happening here? Machine learning is more about driving action — helping to answer the question, What can we anticipate next? — and by extension enabling efficient and effective responses.
So that’s a good start on understanding the differences between statistics and data science. There’s more to be said about that …
And I hope to return to develop the rest of this reflection one day soon.
Those who work in data mining or predictive analytics are familiar with the CRISP-DM process. Metaphorically, if not literally, that process description is taped to our wall. Tom Khabaza’s Nine Laws of Data Mining should be taped up right next to it.
Khabaza has published those laws as a series of blog posts, here. For each law, he has provided a short name, followed by a one-sentence summary, supported by a few paragraphs of explanation.
The value of these laws is that they help prepare us for what to expect as we do the work — and then they remind us of what we should have expected if we occasionally forget!
As I am a fan of brevity, I’m creating this post as a list of the single-sentence summaries. Occasionally I’ll add a short clarifying note. Here they are:
Tom Khabaza’s Nine Laws of Data Mining
Business objectives are the origin of every data mining solution.
Business knowledge is central to every step of the data mining process.
Data preparation is more than half of every data mining process.
The right model for a given application can only be discovered by experiment (aka “There is No Free Lunch for the Data Miner” NFL-DM).
There are always patterns (aka “Watkin’s Law).
Data mining amplifies perception in the business domain.
Prediction increases information locally by generalization.
The value of data mining results is not determined by the accuracy or stability of predictive models. (Rather, their value is found in more effective action and improved business strategy.)
All patterns are subject to change. (Thus, data mining is not a once-and-done kind of undertaking.)
These laws, as Khabaza points out, are not telling us what we should do. Rather they are “simple truths,” describing brute facts that give shape to the landscape in which data mining is done. Their truth is empirical, discovered and verified by those who’ve been doing the work. So it’s best to keep these truths in mind and adapt our efforts accordingly, lest we pay the price for failing to acknowledge reality as it is.
If you’re an educator (or student) interested in leveraging Amazon Web Services through AWS Educate to host a cloud database that allows student connections — this post is for you. In what follows, I’ll document the process to:
Configure and create a relational database instance from the AWS Management Console.
Set a security profile that will allow students to read and write to the database remotely — such as from a database client, from a program they’ve written, from a Jupyter Notebook, etc.
I’ll illustrate the process by creating a PostgreSQL database instance. Then I’ll provide illustrative code snippets for interacting with the database using Python.
The resulting database will be friendly for student projects that include database interactions such as querying, reading, and writing.
I’m going to assume that you’ve already created your Amazon Educate Account and are logged into your AWS Console. Thus, we’ll begin by creating a relational database.
Creating a Relational Database in the AWS Console
Once you have logged into your AWS Console, these are the steps to set up a relational database.
Use the search field under “Find Services” to search for “RDS.” You should see RDS: Managed Relational Database Service appear in the results. Select andnavigate to the RDS page.
Once you’ve arrived at the Amazon RDS page, select Databases in the left-hand sidebar, and then Create Database.
From the Create Database page, select your desired options for creation method and configuration. You are free to choose differently, of course, but I have chosen these options:
Standard Create — This will allow me to optimize the resources my database will use.
PostgreSQL — A favored option among data science types. But choose what’s best for you!
Select your desired Template (the labels here may depend on the engine you choose) according to your needed system resources and the size of your budget. I currently have access to the free tier, which I’ll use now. If that were not available, Dev/Test is the next least resource-intensive option I currently have.
If you desire, edit the database name (identifier), master username, and password.
Choose the DB instance size and storage (if relevant) that suits your needs and budget. I’ve chosen the least resource-intensive options, as these will be plenty for my intended use: basic CRUD operations performed by my students.
Under Connectivity, select “Additional connectivity configuration” and then “Yes” under Publicly accessible.
You’ll be given the option to create a new security group. You can keep the default, or create a new group. I’ve created one named “students.” You’ll also see the database port settings.
Depending on your selected database engine, you may (or may not) be given the option to choose Database authentication. With the PostgreSQL database I’ve chosen, I have these options, and I’ll choose Password authentication.
Depending on your selected database engine, you may (or may not) be given Additional configuration options. If you’re unsure about these options, click the handy Info link to read more about them. I’ve deselected automatic backups in order to conserve resources.
If all looks good, click Create database!
After clicking create, you may be given a message to go back and adjust a configuration option. If so, go back and do that. If all went well, you’ll be taken to a confirmation page. Here’s what mine looked like:
Notice that you can click to View credential details — a handy way to get the login information and save it for future reference!
Congratulations! You’ve created your database!
Now we need to set a security rule to allow interactions with the database.
Allowing Inbound Traffic
In these next few steps, we’ll set a connection security rule to allow inbound traffic to interact with the database.
Beginning at the RDS > Database page, click the database identifier.
Select the Connectivity & security tab.
Scroll down the page to Security group rules, and click to edit the Inbound rules.
If necessary, select Actions, and Edit inbound rules.
There should be an initial rule begun for you. Notice that the Type and Port range are already set to match your database settings. Now we need to allow a range of IP addresses. Configure this according to your needs. In my case, I’ll be working with online students. And since the database will not contain sensitive data, I’ll simply pull down the box under Source and select “Anywhere,” to allow traffic from any IP address.Once that’s been selected, I then see the result as two rules, allowing a full range of IP address options:
Click Save rules!
Connecting to the Database
After creating the database, you can connect to the database using an application or database management package. You’ll simply need a few key items of information. These were supplied when you first created the database. The items include:
Endpoint (aka host or hostname)
If you need to recover these later, you can do so by selecting the database from the RDS database list, and then looking under the two tabs: Connectivity & security, and Configuration.
The Endpoint (aka host or hostname) and Port can be found under the database Connectivity & Security details:
The DB name and Master username can be found under the Configuration tab:
As for the user password, you will hopefully have recorded or remembered it!
Those provide the essential credentials you’ll need to connect to the database.
In the next section, I’ll illustrate using these credentials to connect to the database, create a table, insert records, and query the table using Python.
Interacting with the Database Using Python
Python provides modules for connecting to any number of database engines. Since I selected a PostgreSQL engine, I’ll be using the psycopg2 module to interact with it. (For a MySQL database, you can use pymysql. And so on …)
If the module is not currently installed on your system, you’ll need to install it. In Python, this may be done easily using pip or conda:
# Start the cursor to enable SQL operations cur = conn.cursor()
# Create a table cur.execute("CREATE TABLE test1 (id serial PRIMARY KEY, num integer, data varchar);")
# Insert a record cur.execute("INSERT INTO test1 (num, data) VALUES (%s, %s)", (101, "abcdefg"))
# Query the table cur.execute("SELECT * FROM test1;")
# Output the query results cur.fetchall()
# Commit the changes conn.commit()
# Close the connection conn.close()
That’s it! Your database is ready to roll.
AWS Educate provides a fantastic opportunity to equip students with cloud resources. In fact, it’s worth pointing out that both educators and students can follow these steps. If a professor should want students to create their own cloud databases for their projects, the above steps will serve them just as well.
I hope this resource proves helpful. Please comment with feedback, suggestions, and recommendations!
In a recent Tableau project, I wanted to divide a long span of years into decades, as this would provide a more visually effective way to grasp the growth of revenue from top movies (data from The Movie Database) over time. With a little searching, I found the pieces I needed. Below I’ll include a description of my process, followed by links to the helpful sources of insight I found on this topic.
First, here is the visualization with total revenues year by year. Notice that despite its current width you still have to scroll left to reach the early 1900s. Meanwhile, the difference year to year is not in itself that interesting.
Now here is the visualization when years are chunked into decades. Much more effective!
DISCLAIMER: These charts use revenue numbers as entered in The Movie Database by contributors based on publicly reported figures. Thus, the data includes only a portion of all movies. I’ve as yet made no adjustments for inflation.
Getting to Decades from Dates
Now for the process of getting decades from dates. I broke my approach into two steps: