Join us at 12 pm ET on Wednesday for the next installment of WIRED25, and check out the replay of our opening night events.
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Monday, September 21, 2020
How to Install Moodle Learning Platform with Nginx in CentOS 8
Moodle is the world’s most popular learning management system for building robust online learning sites. It features a range of activities and educational tools that you can choose from, it supports assessment management and
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Sunday, September 20, 2020
How to Install Windows Subsystem for Linux
The Windows Subsystem for Linux (WSL) runs a GNU/Linux Environment which includes most of the command-line utilities and applications on top of Windows OS. Traditionally there are many ways we can set up Linux
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Zendaya makes history with Emmy win for ‘Euphoria’
The 24-year-old actress became the youngest to ever win in the category
Zendaya just made Emmys history!
The 24-year-old star of HBO’s Euphoria just became the youngest person to win the Emmy for Outstanding Actress in a Drama. She earned the honor for her role as a drug-addicted teen, Rue, in the provocative series created by Sam Levin and executive produced by Drake.
The former Disney star beat out Hollywood heavy hitters including Jennifer Aniston (The Morning Show), Olivia Colman (The Crown), Jodie Comer (Killing Eve), Laura Linney (Ozark) and Sandra Oh (Killing Eve) and could hardly contain her excitement during her acceptance speech.
Read More: Tyler Perry honored with Emmys Governor’s Award: “We’re all sewing our own quilts”
“I’m really, really nervous. Ok. Oh gosh…I just want to say thank you to the TV Academy, to all the other incredible women in this category, I admire you all so much, this is pretty crazy — I don’t really cry,” she said through tears.
“To the incredible cast and crew of Euphoria, I’m so lucky to go to work with you every day and I’m inspired by everything you do…And to Sam Levinson, I appreciate you so much and you’re my family, I’m so grateful for Rue, I’m so grateful that you trusted me with your story, and I hope I can continue to do you proud.”
She also took time to encourage the youth and shout out the work they have been doing amid all the chaos in the country right now.
Read More: Yahya Abdul-Mateen II dedicates Emmy win to ‘all the Black women in my life’
“I know that this feels like a really weird time to be celebrating, but I just want to say that there is hope in the young people out there,” she said. “I know that our TV show doesn’t always feel like a great example of that, but there is hope in the young people, and I just want to say to all my peers out there doing the work in the streets: I see you, I admire you, I thank you.”
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Tyler Perry honored with Emmys Governor’s Award: “We’re all sewing our own quilts”
Oprah Winfrey and Chris Rock shared warm words about the “talented, crazy” mogul
The 72nd annual Primetime Emmy Awards were full of powerful moments, but when Tyler Perry accepted the coveted Governor’s Award, his speech served up the inspiration we need.
Oprah Winfrey and Chris Rock presented the award to the billionaire described as “talented and crazy.” In his touching acceptance remarks, the 51-year-old content creator, actor, philanthropist, and studio head told a story about a quilt his grandmother gave to him and likely prompted tons of tears from viewers at home.
“This is amazing. I didn’t expect to feel this way,” he said before sharing the anecdote that highlighted the strength and impact of storytelling about our community and collective experiences.
Here’s the full speech:
When I was about 19 years old … my grandmother, she gave me a quilt that she had made. And this quilt was something I didn’t really care for. It had all these different colors and these different patches in it. And I was quite embarrassed by it. I had no value in it at all.
When the dog got wet, I dried him off with it. When I needed to change the oil in the car, I laid it on the ground. I had no respect for this quilt. Many years later, as I was walking past those fancy antique stores that I could finally go in and shop, I saw in the window a quilt that looked just like the one that she had given me.
As I’m in that store wondering where that quilt was, there was an attendant who walked up to me and said, “Let me tell you about this quilt. It was made by an African American woman who was a former slave and each patch in the quilt she had put in represented a part of her life. One part was from a dress that she was wearing when she found out she was free. Another part was from her wedding dress when she jumped the broom.
Read More: Regina King accepts Emmy for ‘Watchmen’ wearing Breonna Taylor t-shirt
And as I was hearing this story, I became so embarrassed. Here I was, a person who prides myself on celebrating our heritage, our culture, and I didn’t even recognize the value in my grandmother’s quilt.
I dismissed her work and her story because it didn’t look like what I thought it should. Now, whether we know it or not, we are all sewing our own quilts with our thoughts, our behaviors, our experiences and our memories. Like in my own quilt, one of my memories was I was about 10 years old. I remember my father standing at the door and I was wondering why he stood there so long.
He was frustrated and he walked away and I asked my mother what was going on. She said he had worked all weekend and he was waiting for the man to come and pay him and he never did. They needed the money at the time. And, I tell you, she was so frustrated, she turned to me and she said, “Don’t you ever stand by a door waiting for White folks to do nothing for you.”
Now, my mother wasn’t a racist. But, in her quilt, she couldn’t imagine a world where her son was not waiting by the door for someone. In her quilt, she couldn’t imagine me building my own door and holding that door open for thousands of people. In my mother’s quilt, she couldn’t imagine me owning land that was once a Confederate Army base where Confederate soldiers plotted and planned on how to keep Blacks enslaved.
Read More: Tyler Perry officially becomes a billionaire, Forbes announces
And now, on that very land, Black people, White people, gay, straight, lesbian, transgender, ex-cons, Latin, Asian, all of us, come together, working. All coming together to add patches to a quilt that is as diverse as it can be. Diversity at its best.
I stand here tonight to say thank you to all of the people who are celebrating and know the value of every patch, and every story, and every color that makes up this quilt that is our business, this quilt that is our lives, this quilt that is America. Because in my grandmother’s quilt, there are no patches that represented Black people on television. But in my quilt, her grandson is being celebrated by the Television Academy. I thank you for this. God bless you.
Read More: Yahya Abdul-Mateen II dedicates Emmy win to ‘all the Black women in my life’
Check out his full speech below:
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Uzo Aduba, Zendaya, and more win big at the 2020 Emmy Awards (UPDATING)
DJ D-Nice is helping Jimmy Kimmel pull off the virtual event
The 2020 Emmy Awards are going down now and the historic event has been full of surprises. For the first time in its 72-year history, the annual event is being produced without an audience and so far, the virtual presentation is going pretty well.
Hosted by Jimmy Kimmel, the mostly virtual event has featured a few of our faves (like Tracee Ellis Ross, Zendaya and Anthony Anderson) as presenters in the flesh, while other famous faces like Issa Rae and Kerry Washington have appeared from remote locations.
Read More: Yahya Abdul-Mateen II dedicates Emmy win to “all the Black women in my life”
Even though HBO’s Watchmen went in as the year’s frontrunner with 26 total nominations, Schitt’s Creek is the show that has been racking up the statues so far. Still, Regina King already nabbed the coveted award for Outstanding Lead Actress in a Limited Series for her role in the groundbreaking series and her co-star, Yahya Abdul-Mateen II won Outstanding Supporting Actor in a Limited Series for his role in the one-season hit.
Read More: Regina King accepts Emmy for ‘Watchmen’ wearing Breonna Taylor T-shirt
Another huge moment came from Zendaya when she won her first Emmy for Outstanding Lead Actress in a Drama for her role in HBO’s riveting teen drama, Euphoria.
Check out the full list of winners:
Best Comedy
Best Limited Series
“Watchmen” (HBO)
Best Actress, Comedy
Catherine O’Hara, “Schitt’s Creek”
Best Actor, Comedy
Eugene Levy, “Schitt’s Creek”
Best Actress, Drama
Zendaya, “Euphoria”
Best Actor, Drama
Jeremy Strong, “Succession”
Best Actress, Limited Series or TV Movie
Regina King, “Watchmen”
Best Actor, Limited Series or TV Movie
Mark Ruffalo, “I Know This Much Is True”
Supporting Actress, Comedy
Annie Murphy, “Schitt’s Creek”
Supporting Actor, Comedy
Daniel Levy, “Schitt’s Creek”
Supporting Actress, Limited Series or a Movie
Uzo Aduba, “Mrs. America”
Read More: HBO renews ‘Euphoria’ for season 2 after Zendaya makes huge impact
Supporting Actor, Limited Series or Movie
Yahya Abdul-Mateen II, “Watchmen”
*Television Movie
“Bad Education” (HBO)
*Variety Sketch Series
“Saturday Night Live” (NBC)
Variety Talk Series
“Last Week Tonight With John Oliver” (HBO)
Reality Competition Program
“RuPaul’s Drag Race” (VH1)
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Yahya Abdul-Mateen II dedicates Emmy win to ‘all the Black women in my life’
The actor beat out two of his ‘Watchmen’ costars for Outstanding Supporting Actor in a Limited Series
Watchmen is getting the glory it deserves at the 2020 Emmy Awards and Yahya Abdul-Mateen II just earned his first award.
The actor snagged the trophy for Outstanding Supporting Actor in a Limited Series or Movie for his role in the HBO hit that earned a total of 26 nominations for its one and only season.
Read More: Regina King accepts Emmy for ‘Watchmen’ wearing Breonna Taylor T-shirt
As if we needed another reason to swoon, the Yale grad seemed genuinely surprised when he got the good news and delivered a touching acceptance speech that highlighted the importance of the show and his love for the Black women in his life.
“Oh, man. Thank you so much to the Academy. I’m so excited right now. I want to say thank you first off to my mama and my daddy. Man, I’m surprised. Thank you so much to HBO, to Damon Lindelof, for your words, for your creativity,” he said.
“Watchmen was a story about trauma. It was a story about the lasting scars of white domestic terrorism. It was a story about police corruption and brutality, but in the midst of all that, it was also a story about a god who came down to Earth to reciprocate, to a Black woman, all the love she deserved…He did all that in the body of a Black man, and I’m so proud that I was able to walk into those shoes. So, I dedicate this award to all of the Black women in my life.”
Read More: Yahya Abdul-Mateen II to star in Netflix’s ‘The Trial of the Chicago 7’
Watchmen has already earned 10 Emmys and there is still plenty of time for the groundbreaking series to rack up a few more. In it, Mateen plays the stay-at-home husband of Regina King’s character, Angela. His alter ego is a glowing God, Doctor Manhattan. The actor beat out two of his own co-stars, Jovan Adepo and Louis Gossett Jr., as well as Tituss Burgess, Jim Parsons, and Dylan McDermott.
Yahya Abdul-Mateen II will next be seen co-starring in Aaron Sorkin‘s upcoming Netflix film, The Trial of the Chicago 7. In it, he plays Black Panther co-founder, Bobby Seale.
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Regina King accepts Emmy for ‘Watchmen’ wearing Breonna Taylor T-shirt
King also shouted out Ruth Bader Ginsburg and urged viewers to vote during her acceptance speech.
The 72nd Annual Primetime Emmy Awards are in full swing and Regina King just scored a major win for her leading role in Watchmen.
She won Outstanding Actress in a Limited Series or Drama for fronting the HBO series that also stars Yahya Abdul-Mateen II, who just won the Emmy for Outstanding Supporting Actor in a Limited Series or Movie.
Read More: Regina King unveils directorial debut ‘One Night in Miami’ at Venice Film Festival
King accepted the award from her home while wearing a pink suit with a T-shirt that featured the face of Breonna Taylor under the words “Say Her Name.” In her speech, Regina King urged viewers to vote after acknowledging the other women nominated in her category including Kerry Washington and Octavia Spencer. She also made sure to pay tribute to Supreme Court Justice Ruth Bader Ginsburg, who died on Friday at the age of 87.
“I truly love being a thespian…Thank you Television Academy for choosing me to represent the thespian community…Damon Lindelof I love you, brother,” she said to the show’s creator and EP. “Thank you for your brilliant mind. Thank you for choosing all of us to join this journey as you stepped outside of your comfort zone and led us on a journey where we could bring art to truth to power.”
Read More: ‘Watchmen’ cast demands ‘wash your hands’ in COVID-19 PSA
As usual, King used her platform to share an important message.
“Gotta vote. I would be remiss not to mention that, being a part of a show as prescient as Watchmen. Have a voting plan, go to ballotpedia.com, vote up the ballot, please. Go to ballotpedia.com and find out who are voting in your municiapal elections. It is very important,” she urged. “Be a good human. Rest in power, RBG. Thank you.”
Watchmen has already won four Creative Arts Emmys this week and was a monumental success for HBO, even if for only one season, earning the network 26 total Emmy nominations.
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‘Best of My Love’ singer Pamela Hutchinson dies at 61
Hutchinson’s voice helped shape The Emotions’ unique sound
The iconic group The Emotions has lost one of their voices this weekend.
Pamela Hutchinson died on Friday according to a post made on the group’s official Facebook page.
The post told fans that their “beautiful sister” will now “sing amongst the angels in heaven in perfect peace.”
“In loving memory, we are saddened to announce the passing of our sister, Pamela Rose Hutchinson, on Friday, September 18, 2020. Pam succumbed to health challenges that she’d been battling for several years,” the post read.
“During this time, the family kindly asks for fans and friends to respect our privacy. We appreciate all kind words, photos, and videos you may want to post for our beloved Pamela and of course your loving prayers. A life so beautifully lived deserves to be beautifully remembered. We love you, Pamela!” the post continued.
Read More: Former Temptations lead singer Bruce Williamson dies from COVID-19
Hutchinson, though the youngest of the trio, helped shape the group’s unique sound.
The Emotions originally launched as a gospel group, known as the Hutchinson Sunbeams who traveled with their father Joe Hutchinson.
The Sunbeams sang on Jerry Van Dyke‘s “Children’s Gospel” television show and even toured with New Orleans native and famed gospel singer Mahalia Jackson.
When the group added an R&B/Soul feel to their music, they dropped “Sunbeams” and became The Emotions. The grew a large following in their hometown of Chicago and the rest was history.
VH1 listed The Emotions as one of the most influential girl groups of all time.
Music lovers spanning several generations probably know the group for one of their best known hit singles, “Best of My Love.”
Pamela Hutchinson was 61.
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‘Fifth girl’ in 1963 KKK church bombing seeks apology, restitution
A church deacon pulled Sarah Collins Rudolph out of the wreckage when she was 12
Sarah Collins Rudolph, a survivor of the church bombing that killed four Black girls 57 years ago in Birmingham, Alabama has never received an apology or any compensation for the lifelong trauma she suffered from the attack, her lawyers said on Wednesday.
She blames then Gov. George Wallace‘s violent rhetoric for the racial hatred that led to the bombing.
After years of contacting local and state officials, seeking some form of restitution for the injuries and decades of trauma she endured, Rudolph’s lawyers wrote a letter to Gov. Kay Ivy of Alabama, calling on the state to issue a formal apology to Rudolph and monetary compensation to “right the wrongs that its past leaders encouraged and incited.”
Read More: Thomas Blanton, 1963 KKK bomber of 16th St Baptist Church in Birmingham, dies
The letter continued, “The actions of the bombers, affiliated with the Ku Klux Klan and inspired and motivated by then-Governor Wallace’s racist rhetoric, left Ms. Rudolph hospitalized for months and scarred, both physically and mentally, to this day.”
The Washington Post reported that Gina Maiola, spokeswoman for the Alabama governor, wrote in an email that the office received the letter and was reviewing it.
Rudolph was 12 years old at the time of the terrorist attack on the 16th Street Baptist Church that took the lives of her sister, Addie Mae Collins, 14, Carol McNair, 11, Carole Robertson, 14, and Cynthia Wesley, 14.
The girls were attending the church’s annual Youth Day event and were changing into their choir robes in the church’s basement when at least 15 sticks of dynamite planted by four Klansmen exploded under the steps of the church, near the basement.
A church deacon pulled Sarah out of the wreckage, but she was left blind in her right eye, and she still has remnants from the blast lodged inside her body, along with a piece of glass in her left eye that her doctor fears will lead to total blindness if he attempts to remove it.
Read More: ‘4 Little Girls’ remembered: The price of freedom after 50 years
Being blind in her right eye destroyed her dream of becoming a nurse. Being introverted and disabled, Rudolph could only work odd jobs, including foundry work and housekeeping.
Those responsible for the bombing didn’t face justice until 1977 when a jury convicted KKK member, Robert Chambliss. It wasn’t until 2001 and 2002 that Thomas Blanton and Frank Bobby Cherry were indicted for their roles in the bombing.
All three men died in prison, and a fourth man, Herman Cash, died of cancer in 1994 without ever being charged.
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Weekly Digest, September 21
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.
Announcement
- How to Automate your Cloud Data Warehouse - Upcoming DSC Webinar
Featured Resources and Technical Contributions
- Stumped by Bayes' Theorem? Try This Simple Workaround
- Data-driven innovation in healthcare: synthetical clinical data
- All-Purpose Universal Platform for Real-Time AI/ML +
- 8 Smart Ways To Become A Data Scientist
- A Programming Guide with Probability and Statistics
- CALL SYMPUT in SAS Explained with Examples
- New Coursera Series: Machine Learning for Everyone
- Monitoring the Health of Any Data
Featured Articles
- Risk Avoidance Spectrum and Character Types
- Algorithms of Social Manipulation
- 3 Big Businesses Must Adopt DevOps and Cloud Computing
- The illusion of choice
- IoT’s Purview on Current Environmental Conditions
- Difference Between Deep Learning and Machine Learning in one Infographics
- Difference Between Correlation and Regression in Statistics
- Deep Learning Explained in 4 Simple Facts
Picture of the Week
Source: article flagged with a +
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The illusion of choice
Do you think your actions are the result of your own free choices? What if those actions are the inevitable and necessary consequence of antecedent states of affairs? What does this mean for your free will?
In a deterministic world where there’s an exclusive future for all our actions, digital users can become more predictable and monetizable than ever. In fact, by using creative designs and deceptive strategies, companies can create deterministic worlds and exploit the fact that human behaviour is hardwired to choose the path of least resistance.
Sites and apps designs become highly relevant in this scenario, because if you know how people think, you can design choice environments that make it easier for people to choose what you want. Several companies observe and learn an incredible amount of information about user behaviour in order to refine what is called “choice architectures,” discrete design elements intended to influence human behaviour through how decisions are presented.
There are choice architectures all around you, and they are never neutral: they always influence user behaviour, even when they fail to accomplish its objective or there’s no explicit strategy behind.
Nudging
“Nudging” refers to how users can be driven towards making certain choices by appealing to psychological biases. A nudge is any aspect of the choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives. It represents a small feature of the environment that alters people’s behaviour but does so in a non-enforced way.
It’s true that choice architectures influence by default (even when there’s no apparent intention behind), so a nudge is best understood as the intentional attempt at influencing choice.
Nudges work without limiting the user’s original set of choices, and can produce positive results like:
- Improving health caring. Sending people a simple reminder to schedule a dental check-up proved to double the rate of people who signed up for an appointment.
- Improving financial decisions. Sending students a few personalized text messages helped many of them remember to refile their application for student aid.
- Increasing choices that benefit others. Using an opt-out system for organ donations where people are automatically registered as organ donors can significantly increase the number of donors.
Whether through reminders, personalized notifications, awards or default settings, nudges can steer people to make better choices. But the question is: better to whom? What if there were opposite interests behind the act of driving user’s behaviours?
The dark side
There are tricks that go beyond nudges to influence decision making, and cause users to do things they may not otherwise do. Dark patterns are subtle ploys many companies use to manipulate you into doing something, such as buying or signing up for something, or disclosing personal or financial details.
A design that delivers the best conversion rate might not be the same that delivers the best user experience.
Dark patterns may exploit timing to make it harder for a user to be fully in control of its faculties during a key decision moment. In other words, the moment when you see a notification can determine how you respond to it. Or if you even notice it.
Dark patterns also leverage on a very human characteristic: we’re lazy by design, so producing friction is always an effective strategy. Designs that require lots more clicks/taps and interactions discourage users into engaging with that content.
The site Darkpatterns.org details some of these deceptive mechanisms like “roach motel” (when the design makes it simple to sign up but hard to cancel), “disguised ads” (ads masqueraded as content that isn’t trying to sell you something), or “privacy Zuckering” (named after Facebook CEO Mark Zuckerberg, is the trick of getting you to overshare data).
These strategies extend everywhere on the internet, and although some countries try to regulate this behaviour, the truth is that not all governments seem to be taking action:
- Google has been accused of using manipulative design practices to trick users into clicking on ads, making it nearly impossible to tell ads apart from organic search results.
- TurboTax is a US tax preparation software to file taxes online that has been accused of making it ridiculously complicated to find the actual free part of the service, and even making sure to hide it from Google results.
- Microsoft has also been accused of using dark patterns to push users into opening cloud accounts, making the offline-account option harder to find.
As digital users, we are in a very hard spot. The fact that many US banking sites are harder to read than Moby-Dick (making 58% of US bank content not readable for the average American), and about 11% of retail websites contain dark patterns, reveals that we are continuously under siege. Is there any way out?
Final thoughts
Manipulation through behavioural techniques can occur quietly and leave no trace. Since companies can drive customer’s decisions through heavy analytics and user interfaces, it’s easy to imagine a digital future in which social platforms employ algorithms to predict the virality and monetizability of each post, only accepting or highlighting the ones that could generate sufficient revenue.
Companies are super focused on testing and experimenting with different techniques to get the most desirable responses, and since they are incredible experts in the discipline, it seems hard to avoid being deceived. The good news is that education is a powerful tool, and by knowing some of their strategies and trying to be aware of our cognitive biases, we might be able to sidestep some of the traps.
Sites like Darkpatterns.org or Princeton’s Web Transparency have lots of examples regarding dark patterns and awesome material. I suggest you take some time and go through them. It will pay off. You can also explore @darkpatterns or use the hashtag #darkpatterns on Twitter to call out what you’ve found or discover what others have found. Social media engagement is a good way to put pressure on companies to stop using these practices.
If you can detect deceptions, you’re more likely to avoid them
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8 Smart Ways To Become A Data Scientist
Data Science has been coined everywhere, and everybody wants to express their views and thoughts on this subject even though they are a novice or lack adequate knowledge about data science! The idea that everyone can become a data scientist only by studying a few technological advances and solving any complicated problems cripples the world today.
Let us first understand the career perspectives of data science. Data science has three pillars:
- Business Expert - Data Analyst
- Technology Expert - Data Engineer
- Statistics & Algorithm - Data Scientist
First, make it clear whether you want to become a data analyst, a data engineer, or a data scientist.
Smart ways to become a data scientist
- Education requirement
Education is known to be one of the primary sections of resumes and it is not going to change at all. Educational background serves a signal to the employers to better know about their future employees. When it comes to Data Science, you’ll find most of the professionals holding Ph.D. education. As per the data gathered by 365datascience, the typical data scientist in 2020 holds a Master’s degree (56%), a Bachelor (13%), or a Ph.D. (27%) as their highest academic qualification. The highest level of education achieved by data science professionals is a doctorate. Though, the considerable drop in being a data scientist is a bachelor-level degree only. The advanced levels are just to ensure a specialization in data science. We have discussed degrees! But what is the best degree to become a data scientist? To answer this, degree related to Computer Science or Statistics and Mathematics are inclined to data science proficiency. Data Science and Analysis graduates have made their room on the top of the research on the career path of becoming a data scientist.
Let me tell you a hack here! In reality, no single degree can prepare a person for a real job in data science. Even if you are post-graduate in data science, but you don’t have strong analytical and programming skills, you can’t be a data scientist.
- Learning formats
Primarily, you can choose either of the three learning formats- Online training, Offline training, and Self-learning. You can avail of data science training from various platforms like Coursera, Deakin, Udemy, and many more. Offline training, on the other hand, can also be a great option if you have proper resources of data science learning available around you. If you are already in the profession and don’t have time to avail of both online and offline learning, then you have another option to explore, it is self-learning. What you need is to religiously follow various resources online and offline. You can subscribe to various YouTube channels providing data science training and watch the videos as per your time availability.
- Hands-on learning
Solving real-world data science problems will only improve your practice in data science. But from where will you start? Working with the dataset with the classic Titanic data set with survival classification or clustering is likely to damage your portfolio, rather than help. Instead, consider taking ideas from shared Github ventures. Look at what others are creating based on the network that you acquired from LinkedIn through tech sessions and certifications. Feel free to use samples from Github projects on Udacity or Coursera. Then mix real datasets from Google Research, Kaggle, or search for an interesting dataset and start building real problem solutions. If you are involved in a particular field or business, consider looking for public datasets and developing a sample project. For example, if you're interested in fintech, try building a loan approval algorithm using the public loan data from Lending Club. The key takeaway to work with real datasets is that they are very messy and noisy compared to academic ones. You need to prepare the skillset by practising online datasets available. What all you need to do is:
- Download and open the data in Excel or a related application
- See what trends would you find in the data by eyeballing them
- If you think the evidence-backed the article's conclusions? Whether or not?
- What more questions do you think you should answer with the data?
- Programming skills
Starting up in programming can be very challenging, and there are a lot of myths out there that make people think programming is a skill they can never learn, or that landing a job as a data scientist is almost mission impossible. They couldn't be any worse. To become a Data Scientist, prepare yourself to master the following skills:
- Statistics
- R and Python programming language
- Data wrangling
- Mathematical skills like calculus and linear algebra
- Data visualization
- Visualization tools like a tableau, ggplot, plotly.
- Predictive modelling
- Machine learning
Research shows that the sector is continually changing and adapting to market needs as well as its rising importance in academia and around. Universities are meeting demand while Master's is defining itself as the traditional golden degree. Python keeps consuming away at R, but even SQL is on the rise!
- Algorithmic approach
Your prime emphasis should be on a deep understanding of the algorithms. You should be able to answer certain questions as-
- What are the input and outputs of the algorithm?
- What are the assumptions that are part of the algorithm?
- Where does it fail?
- And the master question to test your expertise. If given time and resources, can you manually run an algorithm with just a pen and paper?
If you can answer these for any algorithm then you have acquired experience for that algorithm at the data science level. Practice makes a man perfect is a right saying to be quoted here!
- Stop believing myths
Whilst on the learning path of data science, you might get to know about several myths. My recommendation to you here is DON’T BELIEVE ANY MYTHS, I repeat, DON’T BELIEVE THEM AT ALL! Rather, focus more on:
- Data science is about being able to answer questions and generate value for business, not software
- Learning the definitions matter more than learning the syntax
- Creating and sharing projects is what you are going to do in an actual data science job, and practicing this way will give you a head start
- Practice on storytelling with data
A story in the sense of data science is a tale of what you've discovered, how you find it, and what it means. An explanation could be the revelation that in the past year the company's sales have fallen by 20 percent. It's not enough to merely state the fact — you're going to have to explain why sales fell, and how to address it. The key components of data-storytelling are:
- Understanding and contextualization
- Find ways to investigate
- Using imperative data visualization
- Applying various data sources
- Have a coherent narrative
- Supplementing data
In your data scientist job, you will be given raw datasets only. So, you should have the ability to combine raw datasets before performing data analysis. The first move in creating a supplementing a good quality dataset is to know what expertise to show. The key skills businesses seek in data scientists, and therefore the primary skills they want to show in a portfolio, are:
- Control of communication
- Power to work with others
- Technical know-how
- Power to reason with data
- Motivation and the potential to take action
Are you a future-ready Data Science professional?
Learning data science will be time-consuming – say 3 to 12 months or more of regular learning is required. By demonstrating a higher demand for junior data scientists than the US and the UK, India has won the position of the best country to start a career as a data scientist. If you just have a Bachelor's degree, it is still the place to be. You just began a life-long adventure, which promises exciting experiences to enjoy. So keep your interest refreshing, develop your collection of programming skills, and good luck in your career in data science! The ball is in your court!
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CALL SYMPUT in SAS Explained with Examples
CALL SYMPUT and CALL SYMPUTX
CALL SYMPUT and CALL SYMPUTX in SAS are the most commonly used DATA Step call routines to interact with the Macro Facility in SAS. Both are used to assign a value of DATA step to a macro variable.
SYNTAX:
CALL SYMPUT(macro-variable, value);
Arguments are macro-variable and can be one of the following items:
- a character string that is a SAS name, enclosed in quotation marks.
- It can contain the name of a character variable.
- A character expression to create a macro variable name. You can create a series of macro variables using this form.
value is the value to be assigned, which can be
- a string enclosed in quotation marks.
- It can be the name of a numeric or character variable. The macro variable is assigned with the value of the variable,
- a DATA step expression. The value returned by the expression in the current observation is assigned as the value of macro-variable.
Example 1: Using CALL SYMPUT to create macro variables of DATA step variables.
This DATA step creates the three macro variables countries, and respectively assign them the values in the code variable.
data currency_codes;
input country $ 6. code $4.;
call symput(country,code);
datalines;
USA USD
UK GBP
Japan JPY
Europe EUR
India INR
;
run;
%put &India;
OUTPUT
INR
Example 2: Creating a series of macro variable
In the below example, the CALL SYMPUT statement builds a series of macro variable names by combining the character string Country and the left-aligned value of _N_. Values are assigned to the macro variables Country1, Country2, and so on.
data currency_codes;
input country $ 6. code $4.;
call symput('Country'||left(_n_),country);
datalines;
USA USD
UK GBP
Japan JPY
Europe EUR
India INR
;
run;
%put &country2;
OUTPUT
UK
Below is an example of a string enclosed in quotation marks the statement assigns the string testing to the macro variable NEW:
data test;
call symput('new','testing');
run;
%put &new;
OUTPUT
testing
Example- When the value is the name of a numeric or character variable.
The macro variable is assigned with the value of the variable,
For numeric variable, SAS performs an automatic numeric-to-character conversion and writes a message in the log.
data test;
a=2;
b="a character variable";
call symput('a',a);
call symput('b',b);
run;
Use this form when macro-variable is also the name of a SAS variable or a character expression that contains a SAS variable.
A unique macro variable name and value can be created from each observation using the automatic data step variable _n_. See Example 2.
For character variables, SYMPUT creates only one macro variable, and its value changes in each iteration of the program. The macro variable contains only the value assigned in the last iteration of the data step.
data class;
set sashelp.class;
call symput('Name',name);
run;
%put &name;
OUTPUT
William
Example when the value contains a DATA step expression.
The value returned by the expression in the current observation is assigned as the value of macro-variable. In this example, the macro variable named HOLDATE receives the value July 4, 1997:
Also, Read the Date Interval Functions – INTNX and INTCK in SAS
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3 Big Reasons Every Business Must Adopt DevOps and Cloud Computing
Cloud computing has become ubiquitous in today’s fast-paced business environment. When you combine this dynamic technology with DevOps culture, your business transforms in real-time. With benefits like on-demand scalability, quicker run times, consistent codes, and high-performing apps, this game-changing combination can empower your business to become more agile, responsive and swift.
If you haven't implemented it yet, here are three big reasons to do it right away:
- Accelerated Time to Market: Automation is a key driving factor for infrastructure management. It determines the success of a product or service by accelerating its time to market. When business processes are powered by cloud computing, they can be made more efficient and error-free with automation. With a reliable infrastructure, developers can deliver high-performing solutions faster. Automation plays a vital role in optimizing IT processes and enabling efficient management. With the cloud, you can automate processes like running test cases, provisioning, or compiling reports from collected data.
- Faster Deployment: When the cloud powers IT processes, companies can deploy the code faster. However, customized and error-free deployment requires that you use DevOps in Cloud. Businesses that are backed by a DevOps culture can solve their IT issues by building custom logic. With easy access to the latest and advanced tools, it also becomes easier to define capabilities for better business outcomes.
- End-to-End Monitoring: Cloud makes it possible to keep all your essential tools for crucial projects in one centralized platform. From monitoring to backup to automation tools, everything is readily available in one place. This easy access to all infrastructural services is one of the most significant benefits of Cloud. It empowers developers to ensure the success of software solutions.
DevOps in Cloud can be leveraged for managing and monitoring the latest versions of all the tools. Companies can also set up custom alarms and notifications. These alerts are triggered to optimize tools for efficient use.
Backup for Testing
Every cloud service provider has a backup mechanism to tackle unexpected performance issues. Despite this backup, the absence of DevOps can invite automation issues in applications. When this happens, servers need to be launched manually for restoring the backup. Such cases can hurt the end-user experience, causing customers to lose faith in the reliability of your business.
Conclusion
It is cloud computing that empowers businesses to unlock their full potential. It enables you to undertake load testing and assess the reliability of apps. Cloud computing also makes mobile automation testing possible. It integrates advanced tools to recreate the software development environment.
But, if these capabilities are used without DevOps in Cloud, replicating the production environment for testing can become a complicated process that is prone to errors.
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Algorithms of Social Manipulation
Do you know how your apps work? Are you aware of what tech companies are doing in the back with your data? And what’s more revealing: do you know which of your action are actually influenced by those apps? When you take a trip with Uber, buy stuff on Amazon, or watch a movie on Netflix: when are you consciously deciding and when are you being heavily influenced?
Tech companies are not passively observing your behavior and acting as a consequence: they are influencing your behavior so you become more predictable. By conditioning your actions, companies can predict outcomes in a better way, and know better what to sell you.
Every breath you take
Systems and apps make use of massive amount of data like user’s location, maps, browser’s interests, and data streams coming from mobile and wearable devices, in an era of unprecedented power for firms who are no longer merely appealing to our innate desires, but programming our behaviors.
The site True People Search probed the privacy policies of 11 of the biggest tech companies in the world to find out exactly what they know about us, and the result is scary. It’s not the information you are used to share (e.g. your name or email address), but all the information you wouldn’t consider sharing that makes this frightening. Big tech companies record data like income level, political and religious views, credit card information, your calendar events, all your search history and visited websites, and all the content you viewed or engaged with.
Uber stores massive amounts of data from its users, including their location, gender, spending history, contacts, phone battery level, whether they are on the way home from a one-night stand, and even if they are drunk or not. Uber has also experimented with its drivers to determine the most effective strategies for keeping them on the road as long as possible.
In order to place the right content in front of the right people, Netflix logs everything you have ever watched and how you watch: every time you click, watch, search, play, pause, what programs you consider watching but choose not to, and when you’re most likely to rerun a show. To better identify users’ preferences, content is categorized into tens of thousands of micro-genres, and then paired with a user’s viewing history. Everything you see on Netflix is a recommendation: the rows, the titles in those rows, and the order of those titles within the rows are all deeply considered.
But Amazon is a data powerhouse taken to a whole new level. They capture absolutely everything, from your product searches, what you look at but don’t buy, what you look at next, how you pay, how you prefer your shipping, your interactions with Alexa, or your requests to Echo. And the shocking thing is the level of detail they store: they capture what device you use, how many items you subsequently clicked on after selecting a product, your location, and the reading sessions and exact time of day for each tap on your Kindle device.
For Amazon, every mouse click and every twist and turn through its websites, apps and platforms are commodities that carry huge value.
When Amazon convinced third-parties to sell their items via their own marketplace, data collection skyrocketed and allowed the company to see way beyond their eye-sight: they can now access to any market they’ve ever wanted to, and see how customers behave in each one of them.
Tell me what I want
Each day you are influenced by algorithms that guide your decisions and choices. Algorithms are a step by step method for solving a problem or reaching a goal, based on taking an input and conducting a sequence of specified actions to reach a result. Since the explosion of modern technologies, they have expanded, sophisticated and replicated everywhere, having a central role in places like social media platforms.
The goal of several social media and content selection algorithms is to maximize clicks. They are designed to show or recommend stuff that will increase the probability of users clicking on it, since clicking is what generates revenue for the platforms.
For example, a click-through optimization algorithm is more profitable if it can better predict what people are going to click on, so it can feed them exactly that. So, a way to optimize the result is to feed users with content they like, and don’t show anything outside their comfort zone. Although it’s true that this causes their interests to become narrower, it’s not that the algorithms are trying to show you the stuff you like: they’re trying to turn you into a predictable clicker, taking you to a “predictable point” and making it easier for companies to perform any action (e.g. sell you something).
Companies have figured out that they can do this using your own data, by gradually modifying or emphasizing your preferences with targeted material. That’s basically, if you think of a spectrum of preferences, it’s to one side or the other because they want to drive you to an extreme, where you become a more predictable clicker and so they can monetize you more effectively. This is advanced applied behavioral science, or as Jeff Hammerbacher (the founder of Cloudera) said:
“The best minds of my generation are thinking about how to make people click ads. That sucks.”
The reasons behind this are mainly economic. A Wall Street Journal investigation found that Google manipulated search algorithms to prioritize large businesses over smaller ones, guiding search users to more prominent businesses over lesser-known ones. According to the investigation, eBay saw its traffic from Google nosedive during 2014, contributing to a $200 million decrease in its revenue guidance for the year. After Google told the company it had made a change that lowered the ranking of several high traffic pages, eBay considered pulling its roughly $30 million quarterly advertising spend from Google, but ultimately decided to start a pressure campaign on executives. Google eventually agreed to boost the ranking of several eBay pages that were demoted while eBay took on a costly revision of its web pages to make them more relevant.
Mechanized intervention is an ideal way to keep content flowing towards more lucrative topics, avoiding material that doesn’t generate engagement or profits. For tech companies to succeed, algorithms must focus on monetizable activities, and this is exactly what they are doing with our data.
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Risk Avoidance Spectrum and Character Types
Ever since I started following stocks again, I have been preoccupied with developing a "game plan" to help prevent me from making emotional investment decisions. Plans can be improved. What I have come up with so far is a stress avoidance model to study how different character types might perform in relation to the market. Since quite a number of investors are actually algorithms - and many trades are algorithmically triggered - the concept of stress needs to be more liberally interpreted. What "stress avoidance" means to a human can probably regarded as "risk avoidance" to a machine.
As I have written in several blogs, I developed a series of market metrics. While I have yet to share the exact details, I think it would be fair to say the following: 1) selling occurs when risk is high; and 2) buying occurs when risk is low. Risk is expressed as a percentage in the case of my system; this means that that 0 percent will generally cause a buy while 100 percent will trigger a sell. It is therefore possible to create a chart mapping out the different combinations and therefore character types: e.g. 1/50 conforms to the notion of buy-low and sell-high; 50/50 reflects the idea of buy-and-hold - this to say, regardless of the exact purchase price and holding indefinitely.
Below, I labelled a chart from 1946 containing data from the Dow Jones Industrial Average. The risk levels in this case are averaged over 5 days. I note four primary character types although of course there is a spectrum: 1) traditionalists; 2) idealists; 3) opportunists; and 4) players. It is difficult to appreciate the behaviour of the lower sell-risk level characters and particularly the "players" using 5-day risk averaging - since they might respond quite rapidly if averaging is disabled. I call the chart below an "Advantage Thunderbird Chart" - because it maps out the extent to which the subjects exceed or fall below market performance. In this chart using data from 1946, the subjects with the lower risk tolerances generally enjoyed the highest returns.
Now I jump forward in time to last Friday to this ETF traded on the Toronto Stock Exchange - less resolution and using no risk averaging. I chose it specifically to show that sometimes the subjects are relatively unlucky. Almost all the subjects below score either far below market or just slightly above - the 0-50 on the legend providing a relatively poor portrayal of the situation. In short, using this risk model, it has generally been better simply holding on to the stock no matter what, which is even more idealistic than the "Idealists" who might sell at a risk level of 46 to 56 percent.
Risk levels at 46 to 56 percent can certainly precede a market meltdown. I even suggest that a persistent level of blind faith idealism might be an indication of an approaching correction - being euphemistic with my use of the term. It is therefore no minor commitment to hold a stock under such circumstances. Perhaps the biggest recent correction most people remember occurred in association with the COVID-19 lockdowns. There was quite a sell-off. Those that held on or even bought equities - despite the apparently high risk levels - have in some cases in certain sectors been rewarded well. Alas, a risk avoidance trading model is premised on lower levels of risk tolerance.
Some ideas intended to substantiate this model are as follows: 1) whether machine or human, risk is tolerated only up to a point; and 2) a successful approach tends to attract compliant subscribers. For example, if subjects from 1/20 to 50/20 are all unsuccessful for a prolonged period of time, their material agents or proxies (humans and computer algorithms) will tend to disappear. Similarly, if only the fiercest Idealists succeed, this is the monoculture that will prevail. It is the monocultural success bias that might explain the occurrence of a sudden stock market crash - as many like-minded people make the same decisions around the same time.
I understand the underlying numbers better than graphics on the surface charts. These charts are fairly backwards-looking. Suffice it to say that every correction is premised on success stories becoming nightmares. A success story looking backwards is always fabulous. In order to be more forward-looking, I check the recent developments to the sell lines: e.g. 4 weeks ago, 3 weeks ago, 2 weeks ago, and last week. The general idea is to try to find a pattern of change.
It is worth noting that as the sell-risk level increases, at some point the returns must reflect market: if the idea is to buy-and-hold, then in the long-term the rate of return must approximate the market. There is just no way around this in the long-term. In the short-term, it might be possible to buy on a dip. But then going forward, it will be a market-return if no trading occurs. So from a mathematical standpoint, a high sell-risk subject is unlikely to beat market for long. I suppose a trading firm might leverage their position in order to financially engineer more returns (borrowing to increase their exposure); but this would involve different types of risks given that the return would merely amplify the change in market value. I recall one company lending out their equities and then using this to generate a revenue stream. Anyways, no doubt there are various creative methods to generate revenue such as writing options. This does not mean that the underlying investment performs better than market, though.
I cannot say that I have developed a game plan yet. But what is more normal, really? Wouldn't a game plan be delusional given such uncertain circumstances? And if things are normal, why are so many people making such an effort to reassure me that they are normal - as if I am an unfit to decide for myself? It is a risk avoidance model. It is necessary to be aware of risk in order to avoid it. Presumably, people are highly invested in normal. The whole idea of the model is to take emotion out of the equation. While this takes care of my response to reality, there is a problem in terms of mass psychosis - or this illusion of normalcy that has the effect of preventing adaptive response behaviours. Clearly I am not entirely motivated by the technicals.
I took my mother out to a restaurant a few weeks ago. There were hardly any people indoors. Most of the customers were on the patio. Of course, in the winter, those customers will have to decide whether to go inside or not to eat at the restaurant. Basically the problem is that if they get ill, the local health care system might be unable to respond to the demand for services. The new Coronavirus is just one of various illness to affect people recently: H5N1, MERS, SARS, and now COVID-19. I was thinking maybe more people might consider a different line of work, possibly not owning and operating restaurants and other social establishments. Meanwhile, certain public services might wish to have highly trained sanitation and sterilization professionals. In order to trigger changes, it goes without saying that there must be unemployment and business failures. (Of course, I recognize a need to support transition programs.) So I think the next step in terms of the analytics is to ensure that risk avoidance metrics extend from metrics of risk awareness.
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