Last week, I started a new position as Machine Learning Engineer at Merck through Insights Global.
Words are not enough to express my excitement, so I won’t even try.
I wanted to hit the ground running and run, I did.
Thank you, Kathleen, Regina, and CJ, for this opportunity. I will endeavor to make you all proud.
I have left TaskUs.
I would like to express gratitude for all the experience and support that TaskUs has given me. Being part of TaskUs’ success and seeing it grow from a startup to a publicly traded company is something I feel very proud of.
It’s been a pleasure working with a great team in the Business Insights and Data Science department. I am blessed to have worked with talented people, in particular, Scott Gamester, Rachel Perez, and Darcy Delamore. I have built a lasting friendship with colleagues that I will continue to cherish. I am grateful for my teammates, William Li, Dahlia Curtin, Sabrina Castillo, Antonio Morena, Tim Reyna, Sanjana Putchala, Priyanka Manchanda, and countless others.
I am sad to leave. At the same time, I am excited that everything I have learned during my time with TaskUs will help shape the rest of my career. Being with TaskUs afforded me the opportunity to apply data science to bring about actionable insights.
Lastly, I would like to offer my sincerest gratitude to Shauna Zamarippa and Tom Flynn for taking a chance on me.
A friendly tutorial on getting zip codes and other geographic data from street addresses.
Knowing how to deal with geographic data is a must-have for a data scientist. In this post, we will play around with the MapQuest Search API to get zip codes from street addresses along with their corresponding latitude and longitude to boot!
In 2019, my friends and I participated in CivTechSA Datathon. At one point in the competition, we wanted to visualize the data points and overlay them on San Antonio’s map. The problem is, we had incomplete data. Surprise! All we had were a street number and a street name — no zip code, no latitude, nor longitude. We then turned to the great internet for some help.
We found a great API by MapQuest that will give us exactly what we needed. With just a sprinkle of Python code, we were able to accomplish our goal.
Today, we’re going to walk through this process.
To follow along, you can download the data from here. Just scroll down to the bottom tab on over to the Data Catalog 2019. Look for SAWS (San Antonio Water System) as shown below.
Download the file by clicking on the link to the Excel file.
OR, you can click on this.
Head on over to https://developer.mapquest.com/ and create an account to get a free API key.
Copy the ‘Consumer Key’ and keep it in a safe place. We’ll need it later.
Now, let’s fire up a Jupyter notebook and get coding!
For starters, let’s set up the environment by doing a couple of imports.https://towardsdatascience.com/media/7d0f7ced4082761e995ecf8ce0213c3f
Don’t forget to replace the API_KEY (line#12) with your own key above.
Now. let’s read the Excel file with a simple df = pd.read_excel().
Next, we’ll combine the street number and street name columns.https://towardsdatascience.com/media/1696465db27770b7f2942ab707d2efa5
The ALL CAPS hurts my eyes. Let’s do something about it:
df['street_address'] = df.street_address.str.title() .
Below are two functions that call the API and returns geo data.https://towardsdatascience.com/media/3ec6009e8b6069387a9edde18bdad0d3
We can manually call it with the line below. Don’t forget to replace the ‘#####’ with your own API key. You can use any address you want (replace spaces with a + character).
get_zip('https://www.mapquestapi.com/geocoding/v1/address?key=####################&inFormat=kvp&outFormat=json&location=100+ Military+Plaza&thumbMaps=false&delimiter=%2C')
But we’ve got many addresses, so we’ll use a loop to call the API repeatedly.https://towardsdatascience.com/media/9a970862f0352997417c5211df359a9b
Let’s see what the result looks like:
Finally, let’s create a dataframe that will house the street addresses — complete with zip code, latitude, and longitude.https://towardsdatascience.com/media/adfcc23ff94f54877bc80b72e2537ed9
Voila! We’ve got ourselves geo data.
For extra credit, let’s import the data in Tableau and get a pretty spiffy visual:
And that’s it, folks!
You can find the jupyter notebook here.
Thanks for stopping by and reading my post. Hope it was useful 🙂
If you want to learn more about my journey from slacker to data scientist, check out the article below:From Slacker to Data ScientistMy journey into data science without a degree.towardsdatascience.com
And if you’re thinking about switching gears and venture into data science, start thinking about rebranding now:The Slacker’s Guide to Rebranding Yourself as a Data ScientistOpinionated advice for the rest of us. Love of math, optional.towardsdatascience.com
Stay tuned!
You can reach me on Twitter or LinkedIn.
This article was first published in Towards Data Science’ Medium publication.
A hands-on introduction to Microsoft Analytics Tool
As a data scientist, you’ll need to learn to be comfortable with analytics tools sooner or later. In today’s post, we will dive headfirst and learn the very basics of Power BI.
Be sure to click on the images to better see some details.
The dataset that we will be using for today’s hands-on tutorial can be found at https://www.kaggle.com/c/instacart-market-basket-analysis/data. This dataset is “a relational set of files describing customers’ orders over time.” Download the zip files and extract them to a folder on your local hard drive.
If you haven’t already, go to https://powerbi.microsoft.com/desktop and click on the “Download free” button.
If you’re using Windows 10, it will ask you to open Microsoft Store.
Go ahead and click on the “Install” button.
And let’s get started by clicking on the “Launch” button.
Click on “Get data” when the splash screen appears.
You will be presented with a lot of file format and sources; let’s choose “Text/CSV” and click on the “Connect” button.
Select “order_products_prior.csv” and click on the “Open” button.
The image below shows what the data looks like. Click on the “Load” button to load the dataset into Power BI Desktop.
Load the rest of the dataset by selecting “Get Data” and choosing the “Text/CSV” option on the dropdown.
You should have these three files loaded into Power BI Desktop:
You should see the following tables appear on the “Fields” panel of Power BI Desktop, as shown below. (Note: the image shows Power BI in Report View.)
Let’s see what the Data View looks like by clicking on the second icon on the left side of Power BI Desktop.
And now, let’s check out the Model View where we will see how the different tables are related to each other.
If we hover a line, it will turn yellow and the corresponding related fields are both highlighted as well.
In this case, Power BI Desktop is smart to infer the two relationships. However, most of the time, we will have to create the relationships ourselves. We will cover this topic in the future.
Let’s go back to the Report View and examine the “Visualizations” panel closely. Look for the “slicer” icon which looks like a square with a funnel at the bottom right corner. Click on it to add a visual to the report.
In the “Fields” panel, find the “department_id” and click the checkbox on its left.
This will cause the “department_id” field to appear under the “Visualizations” panel in the “Field” box.
Next, take your mouse cursor and hover over the top right corner of the visual in the Report View. Click on the three dots that appeared in the corner as shown below.
Click on “List” in the dropdown that appeared.
While the “department_id” visual is selected, you should see corner marks indicating the visual as the active visual. While the “department_id” is active, press CTRL+C to copy it and then CTRL+V to paste it. Move the new visual to the right of the original visual.
Make the second visual active by clicking somewhere inside it. Then look for the “aisle_id” field in the “Fields” panel on the right of Power BI Desktop as shown below.
Try selecting a value on the “department_id” visual and observe how the selection on “aisle_id” changes accordingly.
Now, examine the “Visualizations” panel again and click on the table visual as shown below.
In the “Fields” panel, select “product_id” and “product_name” or drag them in the “Values” box.
Power BI Desktop should look similar to the image below.
This time, try selecting a value from both “department_id” and “aisle_id” — observe what happens to the table visual on the right.
Let’s create another visual by copying and pasting the table visual. This time, select (or drag) the following fields to the “Values” box of the visual.
Power BI Desktop should now look similar to the image below.
Try clicking one of the selections in the table visual (where it’s showing “product_id” and “product_name”) and observe how the table on the right changes accordingly.
For a closer look, activate Focus Mode by clicking on the icon as shown below.
The table displays the details of orders that have the product that you selected in the table with “product_id” and “product_name.”
Get out of Focus Mode by clicking on “Back to report” as shown below.
Let’s rename this page or tab by right-clicking on the page name (“Page 1”) and selecting “Rename Page.”
Type in “PRODUCTS” and press ENTER.
let’s add another page or tab to the report by right-clicking on the page name again (“PRODUCTS”) and selecting “Duplicate Page.”
Rename the new page “TRANSACTIONS” and delete (or remove) the right-most table with order details on it.
Change the top-left visual and make update the fields as shown below. The “Fields” box should say “order_dow” while the top-left visual is activated.
Move the visuals around so it looks similar below.
Do the same thing for the next visual. This time, select “order_hour_of_day” and your Power BI Desktop should like the image below.
Do the same thing one last time for the last table and it should now contain fields as shown below.
Let’s add another page or tab to the report by clicking on the “+” icon at the bottom of the report’s main work area.
In the “Visualizations” panel, select “Stacked column chart.”
Resize the chart by drabbing their move-handles.
Make sure the “Axis” box contains “order_dow” and the “Values” box with “order_id” respectively. Power BI Desktop should automatically calculate the count for “order_id” and display the field as “Count of order_id” as shown below.
The graph above is interesting because it shows a higher number of orders for Day 0 and Day 1.
Let’s make another chart.
We will follow the same procedure of adding a chart and for this time, we’ll use “order_hour_of_day” in the “Axis” box as shown below.
The graph shows the peak time for the number of orders.
One last graph!
We will add another chart with “days_since_prior_order” in the “Axis” box.
This last graph is the most interesting because the number of reorders peaks during these three time periods: 7 days, 14 days, and 30 days since prior order. This means that people are in a habit of resupplying every week, every two weeks, and every month.
That’s it, folks!
In the next article, we will “prettify” our charts and make them more readable to the others.
The procedures above have been drawn out. But if you’re a novice Power BI user, don’t despair! With regular practice, the concepts demonstrated in this article will soon become second nature and you’ll probably be able to do them in your sleep.
Thank you for reading. If you want to learn more about my journey from slacker to data scientist, check out the article below:From Slacker to Data ScientistMy journey into data science without a degree.towardsdatascience.com
And if you’re thinking about switching gears and venture into data science, start thinking about rebranding now:The Slacker’s Guide to Rebranding Yourself as a Data ScientistOpinionated advice for the rest of us. Love of math, optional.towardsdatascience.com
Stay tuned!
You can reach me on Twitter or LinkedIn.
This article was first published in Towards Data Science’ Medium publication.
A visual step-by-step guide to forecasting using Power BI.
In this post, we’ll go through the process of creating forecasting in Power BI.
You can download the dataset that I used here. It contains daily female births in California in 1959¹. For a list of other time-series datasets, check out Jason Brownlee’s article 7 Time Series Datasets for Machine Learning – Machine Learning Mastery.
Let’s load the data into Power BI. Open up Power BI and click on “Get data” on the welcome screen as shown below.
Next, you’ll be presented with another pane that asks what type of data we want to get. Select “Text/CSV” as shown below and click on “Connect.”
When the File Open window appears, navigate to where we saved the dataset and click on the “Open” button on the lower right-hand corner.
When a preview appears, just click on “Load.”
We’ll now see the main working area of Power BI. Head over to the “Visualizations” panel and look for “Line Chart.”
This is what the line chart icon looks like:
Next, a visual placeholder will appear. Drag the hot corner marking on the lower right-hand corner of the placeholder and drag it diagonally down and to the right corner of the main working area.
Next, head over the “Fields” panel.
With the line chart placeholder still selected, find the “Date” field and click on the square box to put a checkmark on it.
We’ll now see the “Date” field under Axis. Click on the down arrow on the right of the “Date” as shown below.
Select “Date” instead of the default, “Date Hierarchy.”
Then, let’s put a checkmark on the “Births” field.
We’ll now see a line graph like the one below. Head over the Visualization panel and under the list of icons, find the analytics icon as shown below.
Scroll down the panel and find the “Forecast” section. Click on the down arrow to expand it if necessary.
Next, click on “+Add” to add forecasting on the current visualization.
We’ll now see a solid gray fill area and a line plot to the right of the visualization like the one below.
Let’s change the Forecast length to 31 points. In this case, a data point equals a day so 31 would roughly equate to a month’s worth of predictions. Click on “Apply” on the lower right-corner of the Forecast group to apply the changes.
Instead of points, let’s change the unit of measure to “Months” instead as shown below.
Once we click “Apply,” we’ll see the changes in the visualization. The graph below contains forecast for 3 months.
What if we wanted to compare how the forecast compares to actual data? We can do this with the “Ignore last” setting.
For this example, let’s ignore the last 3 months of the data. Power Bi will then forecast 3 months worth of data using the dataset but ignoring the last 3 months. This way, we can compare the Power BI’s forecasting result with the actual data in the last 3 months of the dataset.
Let’s click on “Apply” when we’redone changing the settings as shown below.
Below, we can see how the Power BI forecasting compares with the actual data. The black solid line represents the forecasting while the blue line represents the actual data.
The solid gray fill on the forecasting represents the confidence interval. The higher its value, the large the area will be. Let’s lower our confidence interval to 75% as shown below and see how it affects the graph.
The solid gray fill became smaller as shown below.
Next, let’s take into account seasonality. Below, let’s set it to 90 points which is equivalent to about 3 months. Putting this value will tell Power BI to look for seasonality within a 3-month cycle. Play with this value with what makes sense according to the data.
The result is show below.
Let’s return our confidence interval to the default value of 95% and scroll down the group to see formatting options.
Let’s change the forecasting line to an orange color and let’s make the gray fill disappear by changing formatting to “None.”
And that’s it! With a few simple clicks of the mouse, we got ourselves a forecast from the dataset.
Thank you for reading. If you want to learn more about my journey from slacker to data scientist, check out the article below:From Slacker to Data ScientistMy journey into data science without a degree.towardsdatascience.com
And if you’re thinking about switching gears and venture into data science, start thinking about rebranding now:The Slacker’s Guide to Rebranding Yourself as a Data ScientistOpinionated advice for the rest of us. Love of math, optional.towardsdatascience.com
Stay tuned!
You can reach me on Twitter or LinkedIn.
[1] Machine Learning Mastery. (June 21, 2020). 7 Time Series Datasets for Machine Learning. https://machinelearningmastery.com/time-series-datasets-for-machine-learning/
This article was first published in Towards Data Science’ Medium publication.
Every once in a while, I would come across an article that decries online data science courses and boot camps as pathways towards getting a data science job. Most of the articles aim not to discourage but serve as a reminder to take a hard look in the mirror first and realize what we’re up against. However, a few detractors have proclaimed that the proliferation of these online courses and boot camps have caused the degradation of the profession.
To the latter, I vehemently disagree.
Data science have captured popular imagination ever since Harvard Business Review dubbed data scientist as the sexiest job of the 21st century. More than seven years later, data science remains one of the most highly sought-after job markets today. In fact, due to the dynamics of supply and demand, “the United States alone is projected to face a shortfall of some 250,000 data scientists by 2024¹.”
As a result, capitalism and entrepreneurship answered the call and companies like Codeup have vowed to “help bridge the gap between companies and people wanting to enter the field.”²
In addition, AutoML libraries like PyCaret are “democratizing machine learning and the use of advanced analytics by providing free, open-source, and low-code machine learning solution for business analysts, domain experts, citizen data scientists, and experienced data scientists”³.
The availability of online courses, boot camps, and AutoML libraries has led a lot of data scientists to raise their brows. They fear that boot camp alumni and self-taught candidates would somehow lower the overall caliber of data scientists and disgrace the field. Furthermore, they are afraid that the availability of tools like AutoML would allow anyone to be a data scientist.
I mean, God forbid if anyone thinks that they too can be data scientists! Right?
Wrong.
Alumni of boot camps and self-taught learners, like myself, have one thing going for them: our rookie smarts. To quote Liz Wiseman, author of the book Rookie Smarts:
In a rapidly changing world, experience can be a curse. Being new, naïve, and even clueless can be an asset. — Liz Wiseman
Rookies are unencumbered. We are alert and constantly seeking like hunter-gatherers, cautious but quick like firewalkers, and hungry and relentless like frontiersmen⁴. In other words, we’re street smart.
Many are so bogged down by “you’ve got to learn this” and “you’ve got learn that” that they forget to stress the fact that data science is so vast that you can’t possibly know everything about anything. And that’s okay.
We learn fast and adapt quickly.
At the end of the day, it’s all about the value that we bring to our organizations. They are, after all, the ones paying our bills. We don’t get paid to memorize formulas or by knowing how to code an algorithm from scratch.
We get paid to solve problems.
And this is where the street smart data scientist excels. We don’t suffer from analysis paralysis or be bothered with theories, at least not while on the clock. Our center of focus is based on pragmatic solutions to problems, not on academic debate.
This is not to say we’re not interested in the latest research. In fact, it’s quite the contrary. We are voracious consumers of the latest development in machine learning and AI. We drool over the latest development in natural language processing. And we’re always on the lookout for the latest tool that will make our jobs easier and less boring.
So what if we have to use AutoML? If it gets us to an automatic pipeline where analysts can get the results of machine learning without manual intervention by a data scientist, the better. We’re not threatened by automation, we’re exhilarated by it!
Do not let perfection be the enemy of progress. — Winston Churchill
By building an automatic pipeline, there’s bound to be some tradeoffs. But building it this way will free our brain cells and gives us more time to focus on solving other higher-level problems and produce more impactful solutions.
We’re not concerned about job security, because we know that it doesn’t exist. What we do know is that the more value we bring to a business, the better we will be in the long run.
After all this, I will concede a bit. For the sake of argument, maybe they’re right. Maybe online courses, boot camps, and low-code machine learning libraries really do produce low-caliber data scientists.
Big maybe.
But still, I argue, this doesn’t mean we don’t have value. Data science skills lie on a spectrum and so does companies’ maturity when it comes to data. Why hire a six-figure employee when your organization barely has a recognizable machine learning infrastructure?
Again, maybe.
Maybe, to be labeled as a data scientist, one must be a unicorn first. A unicorn data scientist is a data scientist who excels at all facets of data science.
Data science has long been described as the intersection between computer science, applied statistics, and business or domain knowledge. To this, they ask, how can one person possibly accumulate all those knowledge into just a few months? To this, we also ask the same question, how can a college grad?
Unicorns do exist I believe, but they also have had to start from somewhere.
So why can’t we?
A whole slew of online courses and tools promise to democratize data science, and this is a good thing.
Thank you for reading. If you want to learn more about my journey from slacker to data scientist, check out the article From Slacker to Data Scientist: My journey into data science without a degree.
And if you’re thinking about switching gears and venture into data science, start thinking about rebranding now The Slacker’s Guide to Rebranding Yourself as a Data ScientistOpinionated advice for the rest of us. Love of math, optional.
Stay tuned!
You can reach me on Twitter or LinkedIn.
[1] Harvard Business Review. (June 3, 2020). Democratizing Data Science in Your Organization. https://hbr.org/sponsored/2019/04/democratizing-data-science-in-your-organization
[2] San Antonio Express-News. (June 3, 2020). Software development bootcamp Codeup launching new data science program. https://www.mysanantonio.com/business/technology/article/Software-development-bootcamp-Codeup-launching-13271597.php
[3] Towards Data Science. (June 4, 2020). Machine Learning in Power BI Using PyCaret. https://towardsdatascience.com/machine-learning-in-power-bi-using-pycaret-34307f09394a
[4] The Wiseman Group. (June 4, 2020). Rookie Smarts
Why Learning Beats Knowing in the New Game of Work. https://thewisemangroup.com/books/rookie-smarts/
This article was first published in Towards Data Science’ Medium publication.
For adventurous beginners in NLP.
For this project, we’ll be using PyCaret:
PyCaret is an open source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within seconds in your choice of notebook environment.¹
PyCaret
PyCaret does a lot more than NLP. It also does a whole slew of both supervised and unsupervised ML including classification, regression, clustering, anomaly detection, and associate rule mining.
To learn more, check out Moez Ali’s announcement.
Let’s begin by installing PyCaret. Just do pip install pycaret and we are good to go! Note: PyCaret is a big library so you may want to go grab a cup of coffee while waiting for it to install.
Also, we need to download the English language model because it is not automatically downloaded with PyCaret:
python -m spacy download en_core_web_sm
python -m textblob.download_corporaLet’s read the data into a dataframe. If you want to follow along, you can download the dataset here. This dataset contains Trump’s tweets from the moment he took office on January 20, 2017 to May 30, 2020.
import pandas as pd
from pycaret.nlp import *
df = pd.read_csv('trump_20200530.csv')Let’s check the shape of our data first:
df.shape
And let’s take a quick look:
df.head()
For expediency, let’s sample only 1,000 tweets.
# sampling the data to select only 1000 tweets
df = df.sample(1000, random_state=493).reset_index(drop=True)
df.shape
The fun part!
nlp = setup(data = df, target = 'text', session_id = 493,
customI _stopwords = [ 'rt', 'https', 'http', 'co', 'amp'])
PyCaret’s setup() function performs the following text-processing steps:
And all in one line of code!
It takes in two parameters: the dataframe in data and the name of the text column that we want to pass in target. In our case, we also used the optional parameters session_id for reproducibility and custom_stopwords to reduce the noise coming from the tweets.
After all is said and done, we’ll get something similar to this:
In the next step, we’ll create the model and we’ll use ‘lda’:
lda = create_model('lda', num_topics = 6, multi_core = True)
Above, we created an ‘lda’ model and passed in the number of topics as 6 and set it so that the LDA will use all CPU cores available to parallelize and speed up training.
Finally, we’ll assign topic proportions to the rest of the dataset using assign_model().
lda_results = assign_model(lda)
lda_results.head()
Let’s the plot the overall frequency distribution of the entire corpus:
plot_model()
Now let’s extract the bigrams and trigrams for the entire corpus:
plot_model(plot = 'bigram')
plot_model(plot = 'trigram')
But what if we only want to extract the n-grams from a specific topic? Easy, we’ll just pass in the topic_num parameter.
plot_model(lda, plot = 'trigram', topic_num = 'Topic 1')
If we want the distribution of topics we’ll simply change it and specify it in the plot parameter.
plot_model(lda, plot = 'topic_distribution')
And that’s it!
We’ve successfully conducted topic modeling on President Trump’s tweets since taking office.
Moez Ali wrote a great tutorial on using PyCaret in Power BI. Check it out.
Thank you for reading! Exploratory data analysis uses a lot of techniques and we’ve only explored a few on this post. I encourage you to keep practicing and employ other techniques to derive insights from data.
Stay tuned!
You can reach me on Twitter or LinkedIn.
[1] PyCaret. (June 4, 2020). Why PyCaret. https://pycaret.org/
This article was first published in Towards Data Science’ Medium publication.