fabiochiu/medium-articles huggingface dataset:
HOW TO CREATE YOUR PERSONAL BRAND IN DIGITAL BUSINESS WORLD?
Tags: Personal Branding, Affiliate Marketing, Digital Business, Digital Marketing, Believe In Yourself

HOW TO CREATE YOUR PERSONAL BRAND IN DIGITAL BUSINESS WORLD?

Hello friends,

We all know the world is rapidly changing. Specially during the pandemic of covid-19 the world in 2030 becomes 2021. The technology which will developing in 2030 this develop in 2021. You know how rapidly are the things growing. You don’t think we have to prepare for future. In future the world will become digital. So have to ready for the future things. So what ? If you want to make career in digital business and create your own personal brand so you’re come on correct place. We all know in future about 90 to 95% people will work in digital world or digitally. Many people already know the secret of this. You don’t think you also have your own brand like Pepsi, coca cola and much more.

A personal brand is the building block that will lead to your future success. It can also lead you to many opportunities for advancement in your career.

You definitely feel empowered, enabled and confident to create and drive your personal brand in digital business world. So here is a program that will change your life definitely. I am not just saying there are so many positive reviews on this and so many people already make their brand and earn so much from this. It is my nu ways life and work program which teach you everything about digital business world. This make you perfect for future.

Ok, let’s another thing. If you have one of this thoughts in your mind, then life and work program is for you

· How to create my personal brand ?

· How to be my own brand ?

· How to present myself in meeting ?

· How to present the best version of myself ?

· How to be authentic ?

It’s not just the personal branding workshop that you know from everywhere else. It is full coachulting program.

THE PROGRAM INCLUDE :

• 20 On-Demand Videos [ca. 4 hours in total]

• 1 Program Handbook (Download File - Program Highlights).

• Various Coachulting Exercises and How-To Guides (Download Files) [max. 6 hours in total]

•Exclusive access to our Facebook MY NU WAYS! Community

• Recommended schedule & roadmap for your own context and planning.

•2 Months Online Access from day of purchase.

•Instant Access!

• 2 Bonus Support Modules and many more thought-provoking tricks & treats!

You can access the online program with a smart phone, computer, laptop and tablet. If you really want to become successful in digital business and you see the vision of our future in present then you have to try this program. Because it will gives you a lot of thing to become financially independent and create your own personal brand.

Click here to make it happen and live your life in digital business world.

https://www.digistore24.com/redir/359125/Uniquething/

Disclaimer -the article contains affiliate link.
How Personal Branding Helps You Become More Creative?
By: Entrancetv - Sasanka Dias
Tags: Personal Branding, Branding, Thinking, Creativity, Innovation

How Personal Branding Helps You Become More Creative?

I listened to a TED Talk recently, delivered by Tim Harford. He talked about a concept named “slow-motion multitasking”. How multitasking helps people become creative or simply put, how to bring out natural creativity in a person. Tim was mentioning few examples as Jurassic Park and ER, the most commercially successful movie and TV series respectively and how they were created.

As soon as I got hold of this concept, I thought to myself as to how we can use it for Personal Branding and consequently, how can we make people creative by creating their own personal brands.

The secret of multitasking in making people more creative is that you begin by focusing on one discipline and gradually you turn your focus towards another arena. Then, eventually the magic happens. Likewise, in personal branding, you showcase a different side of your personality in addition to what you do in your day to day life. Then ultimately you transform your ideas to deliver something quite creative.

Creativity occurs when an idea is taken in its original context and transporting it to somewhere else. The same phenomena can be done through personal branding. You can come up with your own concept for your personal brand. Ultimately, it will give birth to a novel concept.

If we want to be better at something we do, it’s always nice to do something else. Personal branding let’s you do that “something else” which will create your own personal brand and as a result it will make you Creative as well.

Let’s create your own personal brand and sharpen your creativity.
The Core Values Bullseye Framework
By: Mitchell Bradley
Tags: Personal Branding, Startup, Personal Development, Personal Growth, Value Proposition

The Core Values Bullseye Framework is designed to help you (or your business) to understand the core values that drive you and help you to prioritise.

The Core Values Bullseye Framework begins with a broad list of potential values.

The list serves as a baseline to get you started with potential values; it is not a complete list (and can, therefore, be individually adjusted and expanded).

Begin by initially brainstorming 12 values that are most important to you and that you think drive your actions.

Once you have selected the 12, go ahead and reduce those 12 to 6 according to importance.

Once you have chosen those 6 values, sit back and think about why you chose those particular 6 values and what they mean to you.

Now you’re going to reduce the 6 to the core 3 values.

Take time to consider which value you think drives you the most, in order to rank those 3 in the core of the framework.

Now, these aren’t just THE 3 values you hold, but they are the ones that drive you and are hierarchically more powerful in their relevance in comparison to the other values. They act as tiebreakers between the 6 and in turn the outer 12 to all other non-top 12 values.

1 → 2 → 3 → 6 → 12 → All Others

Determining a value prioritization helps to set up a decision-making process when values are in conflict. In a certain situation, values are examined according to the position in your own individual value hierarchy.

Values are dynamic rather than set and will therefore change/evolve in priority as you progress through life and experience lots of different influencing aspects along the way.

Your top 1 value may move out of being a top 3 altogether and in some cases out of the 12 as you pass through different phases of your life.

This is what the framework is about. It aims to provide understanding and visualisation on your value prioritisation.

As such it is advisable to reassess the value constellation every year or so to notice what has moved in and what has moved out.
Predicting Credit Risk
By: Nupur Shrinet
Tags: Data Science, Predictive Analytics, Credit Risk, Predictive Modeling

U.S banks will set aside up to $320 billion to cover potential credit losses in 2020 due to the financial strain of the pandemic, according to a new report from Accenture (NYSE: ACN). ; Accenture estimates that banks will need to hold an additional US$210 billion to US$265 billion to cover potential write-offs for bad loans in 2020 depending on the severity of the public health aspect of COVID-19

Dataset & Investigative Questions

The current report illustrates the data analysis of the behavior of the German borrowers. The data classify individual by a set of attributes as good or bad credit risk (target variable). The attributes have both integer and categorical variables defining characteristics of an individual as good or bad credit. Multiple question such as What is the duration of account with the bank? How many dependents does the individual have? What is the credit amount owned by the borrower? The project answers many such question to understand the pattern for credit risk being good or bad. We will explore various features with respect to our target variable i.e. Credit Risk (V21) in our exploratory data analysis.

a) Logistic regression (analysis performed using R): I selected the logistic model for our base model compared to other decision tree models (C5.0, CHAID, Quest, CNR) because we are dealing with high dimensional data. Also learning of simple hyperplane in logistic or logit model is better due to the curse of dimensionality

b) XGBoost Tree (analysis performed using SPSS): I selected XG Boost against the Random Forest model, as we had imbalanced target variable 70% positive and 30% negative. The XGBoost model comes from gradient boosting family of algorithm which is great for most of the classification models (specifically for imbalanced group).

Data Engineering & Limitation

The target value distribution is not equally distributed i.e. 700 (1- Good risk) and 300 (2-Bad risk).This is a common issue in scenarios where anomaly detection is important such as fraudulent of transaction in banks, identification of disease in patient, etc. If we ignore the current issue, the predictive model created will be biased and inaccurate.

Model Evaluation & Findings

Appendix

Please have a look at my GitHub repository for exploratory data analysis, complete set of codes and documentation.
Predicting Kickstarter Campaign Success with Gradient Boosted Decision Trees: A Machine Learning Classification Problem
By: Riley Predum
Tags: Tutorial, Data Science, Predictive Analytics, Machine Learning, Python

I was surfing data.world the other day and I came across a dataset that looked at kickstarter campaigns that succeeded and failed.

I thought, ‘what would it take, i.e. what features are important, in predicting whether or not a kickstarter campaign succeeds?’ Thus, this project was born. In this article, I’ll walk you through the more exciting highlights of the project. For the full code, see the link to the repo at the bottom!

Exploratory Data Analysis to Data Cleaning and Back Again

Jumping right in, I looked at the dataset’s structure to see what I could make of it. In data science, this stage of the workflow is called exploratory data analysis (EDA).

EDA usually comes after data cleaning, but it’s not necessarily a linear process. Sometimes, through EDA, you realize you need to clean something differently or more than you had before. You’ll see what I mean as we proceed.

The dataset contained 20,632 observations of 67 variables, also called features in machine learning problems. Calling .info() on the dataframe showed me what data type each feature was.

From that information I found a few useless variables and promptly removed them. Oftentimes in data cleaning, we deal with missing values. If your dataset isn’t too large and multidimensional you can do an ingenious little trick with the seaborn library (sns) to visualize exactly where there are using sns.heatmap(df.isnull()) where your dataframe is df. By doing that, only the values that are null will show up in a contrasting color to the rest like so.

As we can see, the profile variable is the only remaining variable that contains missing values. I next looked at that variable in more detail and saw that, since it was mostly metadata with each element already having their own variable elsewhere in the data, I decided to remove it.

It’s essential in a classification problem to visualize your target variable (the one you are trying to predict using machine learning algorithms). For this problem 0 = campaign failed, and 1 = campaign succeeded).

We can see that it is roughly a 60–40 split, with more failures than successes. This intuitively makes sense. Throughout your workflow, it’s important to always ask yourself if something intuitively makes sense.

Classification algorithm models are trained on a subset of data, and tested on data that doesn’t contain the target variable labels. By measuring how many of the predictions were correctly labeled, we can get a sense of the model’s performance.

It’s important to know the distribution of your target variable in a classification problem: there is bias in this sample space. Looking at the plot above, there are fewer successful campaigns to train on, so take predictions with a grain of salt (and then some more salt, as you’ll later see).
Complete Machine Learning Project for Beginner
By: Muhammad Iqbal Bazmi
Tags: Data Science Projects, Projects, Data Science, Predictive Analytics, Machine Learning

It’s recommended to use Jupyter notebook although you can use any IDE. But, I am gonna use Jupyter notebook codes in this blog.

Prepare Problem

— Load Libraries

Before proceeding further for model development first load important libraries.

fig 1. Load important libraries ( Inspired by Jason Brownlee)

— Load dataset

the dataset should be in the same folder where your python file is.

To understand the dataset in detail just click here. then go further in this blog.

fig 2. load dataset

Summarize Data

— The dimension of the dataset

fig 3. dimension fo the dataset

In the above fig 3. It shows that there are 150 rows and 6 columns in the given dataset.

— Peek at the Data

fig 4. Peek at the Data

— Bottom of the Data

fig 5. Bottom of the Data

— Description

fig 6. Description of the data(Statistical Analysis)

— Class Distribution

Class Distribution shows that how many classes are there in the given dataset and how many instances for each class.

fig 7. group of Species

Data Visualization

— Pair Plot

fig 8. code for pair plot using Seaborn

fig 9. Pair Plot

By seeing the above Visualization (Pair Plot) It is very clear that two features petal_length and petal_width are import features.

Evaluate Some Algorithms

Now let’s create some models of the given data and estimate their accuracy on unseen data.

Steps to Evaluate Algorithms

Separate out a validation dataset. Setup the test harness to use 10-fold cross-validation. Build 5 different models to predict species from flower measurements. Select the best model.

Please drop the columns ‘Id’ before going further using dataset.drop(columns='Id')

fig 10. Code for Validation dataset.

Question: Why I used an array instead of a pandas Dataframe?

Answer: Because simple array is computationally faster than a pandas Dataframe.

Spot-Check Algorithms

fig 11. Spot-Check Algorithms

In the above picture, It is showing that SVM is the best choice among all to be selected as an Algorithm to make our model

Make predictions

SVM was the most accurate model that we tested. So, I am gonna make a prediction using the Support Vector Machine(SVM).

— let’s create a model

fig 12. Creating a model

— let’s fit the model

fig 13. fitting the model using fit() method

— Let’s make predictions

fig 14. predicting result on unseen data

fig 15. predicted results

— Let’s check what is the accuracy of this model

fig 16. accuracy of the model

— Let’s see the confusion matrix of the predicted result

fig 17. confusion matrix

— let’s see the classification report

fig 18. classification report.

Save the model for later use

— save the model to the disk

— sometime later

Please check the below link.

You can use the above template to solve any real-world Classification problem.

You are welcome for any queries and questions.
Squark — Affordable AI In A Spreadsheet
By: Jeff Piltch
Tags: Startup, AI, Artificial Intelligence, Predictive Analytics, Machine Learning

Squark — Affordable AI In A Spreadsheet

The Problem

Artificial intelligence is out of reach for organizations with less than $1 billion in revenue for a few main reasons. Not only is there an AI expertise shortage, but the data scientist median salary is $110,000, with 80 percent of this group with less than three years of experience. Another barrier is the high cost of technology: AI costs typically range from $100,000 to $1 million annually on top of hiring costs. There are also point solution limitations because companies are often left with isolated models that are only applicable to specific-use cases, with no reach across functions. Lastly, there are operational failures abound for companies trying to do this work — only 13 percent of AI Projects are in production, according to IBM.

What The Company Does

Squark provides a truly automated machine learning capability that is accessible and affordable to corporate and mid-market companies. It is codeless; offers the fastest time-to-value and the lowest total cost of ownership. Squark also scales across business functions, does automatic AI data preparation, feature engineering, production-ready classification, regression and forecasting.

Business Model

The company has a SaaS model selling licenses to enterprises.

The Market

A segment of the larger AI market spend, Squark’s core focus is on what is expected to be a $58 billion market by 2021 with 500,000 analysts. Just between Boston and NYC there are 22,000 analysts, all of whom could use a Squark license to put prediction at the center of their business. Competitors include companies like DataRobot, Big Squid and Aible, but these companies are largely service companies according to the Squark team.

Traction

Squark has on-boarded nine customers in the last six months that includes F100 companies. The team has participated in the NVIDIA Inception Program, a virtual accelerator that nurtures cutting-edge AI startups.

Founding Team Background

The team is comprised of entrepreneurs with multiple exits who are global thought leaders, academics, and authors.

What They Need Help With

Squark is in the midst of a $1.25M seed round. Feel free to reach out to them directly at judah@squarkai.com or to us at The Buzz at hello@ourbuzzmedia.com if you’d like more info. They are also looking for introductions to potential customers and have list of their open positions.

Subscribe To The Buzz To Get More Startups In Your Inbox
Predicting Taxi fares in NYC using Google Cloud AI Platform (Billion + rows) Part 1
By: Tejan Irla
Tags: Cloud Computing, Predictive Analytics, Deep Learning, Google Cloud Platform, Neural Networks

We definitely need to clean our data. For example, the latitude and longitude values are off (latitude should be between -90 and 90; longitude should be between -180 and 180). Some trips have a negative fare amount and some have a zero passenger count.

Let’s plot trip_distance vs fare_amount

Some bad data is coded as zero. Also, we are not going to include tips in our prediction as they tend to be unpredictable. Our target variable would be fare_amount + tolls_amount

Our predictors would be:

pickup_datetime pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude

We have selected these as they are best in line with our objective. One can argue that trip_distance can help us determine the fare, but we are excluding it as it cannot always be known in advance. Our project might have applications in the ride-hailing industry where we can predict the trip cost in advance, and the location coordinates of the source/destination should be chosen over trip_distance for accuracy.

Data Cleaning

We follow the following to filter unreasonable data:

Fare amounts should be greater than $2.5

Source/Destination location coordinates should be within NYC limits

Passenger Counts cannot be zero

Trip Distance cannot be zero

SELECT

(tolls_amount + fare_amount) AS fare_amount, -- label

pickup_datetime,

pickup_longitude,

pickup_latitude,

dropoff_longitude,

dropoff_latitude

FROM

`nyc-tlc.yellow.trips`

WHERE

-- Clean Data

trip_distance > 0

AND passenger_count > 0

AND fare_amount >= 2.5

AND pickup_longitude > -78

AND pickup_longitude < -70

AND dropoff_longitude > -78

AND dropoff_longitude < -70

AND pickup_latitude > 37

AND pickup_latitude < 45

AND dropoff_latitude > 37

AND dropoff_latitude < 45

-- repeatable 1/5000th sample

AND MOD(ABS(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING))),5000) = 1

Let us also create some categorical features by getting dayofweek and hourofday from pickup _datetime

SELECT

(tolls_amount + fare_amount) AS fare_amount, -- label

EXTRACT(DAYOFWEEK from pickup_datetime) AS dayofweek,

EXTRACT(HOUR from pickup_datetime) AS hourofday,

------------------

------------------

Split the data into Train, Validation, and Test sets

We split our 1/5000th sample 70–15–15

Write the data to CSV

In the next steps, we are going to create a machine learning model using TensorFlow which is slow to read files directly from BigQuery. Thus, we write our data out as .csv files

Given that we are only using a sample (0.02%) of the total data, we still have 151k rows to train on which is pretty good.

The next post where we discuss model creation can be found here.

Connect with me on LinkedIn. You can find the full code here.

Cheers !!
Your Next Top Model
By: Mia Iseman
Tags: Data Science, Machine Learning Models, Predictive Analytics, Machine Learning, Predictive Modeling

Your Next Top Model

Note: At the end of this brief article, click on the link to interact with the document created by Pat Fuller, Connor Anderson, and Mia Iseman.

What’s a random forest? If a decision tree falls there, will my parameters influence whether it makes a sound?

What’s a Bayes, and why is it still so naive after all these years? Shouldn’t it know better by now?

For real though, what does SVM even stand for?

There are a lot of machine learning models out there, and it makes sense that you don’t have the answers to everything off the top of your head. Maybe you are new to data science, perhaps you’ve been using the same models in your 9 to 5 for years now, or you need some quick interview prep. Either way, it’s nice to have a resource to remind you what options for machine learning models exist.

My colleagues and I have created a high-level overview of many different models as well as a taxonomy. We recently studied most of them listed, and we found that when we graduated from our course, it was harder to remember the ones we studied first or the ones that didn’t make it into one of our projects.

preview of the taxonomy we developed

While we know the resource itself is helpful, it’s probably no surprise to hear that the act of creating it was more beneficial to us personally. So, we’d love your help in keeping this a living document because it will benefit others and serve as a check-for-understanding for you too.

We know this is nothing groundbreaking, it’s simply a helpful resource. Let’s keep it updated — after all, at the rate that data science is growing and changing, we need something to help us keep all these models and their purposes straight. Please leave a comment on the deck here, and I hope it’s useful!

example slide from the deck

Link to deck: https://docs.google.com/presentation/d/1hiFRhHr7K2OCZSO0RwetD8xxbdUbPcPQ-dJqs93ERYY/edit?usp=sharing
EPL Fantasy GW20 Recap and GW21 Algo Picks
By: Dilyan Kovachev
Tags: English Premier League, Fantasy Sports, Predictive Analytics, Sports Betting, Soccer

Our Moneyball approach to the Fantasy EPL (team_id: 3277501)

Top team GW20

If this is the first time you land on one of our Fantasy EPL Blogs, you might want to check out some of our original EPL blogs in my Medium archives to get familiar with how this project started and the improvements we’ve made over time.

Top 100 FPL Team Stats for GW20

Very interesting distribution at the top100 players, where the points were scattered all over the place ranging from as low as 34pts to 87pts, with an average around 61pts. This just goes to show that GW20 was a difficult gameweek for everyone.

Most Selected Goalkeepers by Top100

Interesting to notice a lot of the top players having Jonhstone and Areola in their GK selections.

Most Selected Defenders by Top100

No surprise to see lots of MCI and WHU defenders here

Most Selected Midfielders by Top100

Interesting to see Soucek as the 2nd most popular choice, also Saka, Smith Rowe, and Raphina showing in top 10.

Most Selected Strikers by Top100

With Bamford and Kane as some of the most selected choices, there is not surprise top100 players in the world didn’t have a great week.

We used this data to create the team below which is a blend of the most selected players by Top100:

Most selected Team Formation by Top 100

Looks like the Top100 players continue to try to capitalize on offensive midfielders and strikers with the 3–4–3 formation.

GW20 Team Performance Recap and Overall Stats

We have mixed feelings about last GW. The good news as one of our teams had 70pts, which was above the average of the Top100 players. The bad news was that on our top-performing team we sold Vardy to get Kane last GW, and Kane only got 1pt and also got injured. Seeing how many of the top players scored 50pts and below, makes us feel a little better about our poor scores.
Predict Customer Churn With Precision
By: Chuck Utterback
Tags: Data Science, Predictive Analytics, Marketing, Machine Learning, Python

Accuracy, please take a back seat. We’ll be promoting Precision and Recall today.

“Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers” — Wikipedia

For this post, let’s agree on the universal assumption that “customer churn is bad”, and “customer retention is good”.

Abstract: This article takes a different slant on a well-traveled churn dataset that does not have the right features to blindly deploy even the best predictive model we can generate. Below I’ll share the problem statement, data preparation steps, feature analysis, visualizations and select Python code from the best of the Scikit-learn classification models I tried for predicting customer churn. Most importantly, I’ll show that by moving the decision threshold probability along the precision-recall curve, you may find tranches of churn cases where you feel confident enough to deploy real retention actions.

In the interest of brevity, I’m not including the full code used on this project. For full details, please refer to my Jupyter notebook on GitHub.
Grand Jury misled in Uber driverless vehicle fatality case?
By: Michael Dekort
Tags: Uber, Driverless Cars, Autonomous Cars, Machine Learning, Autonomous Vehicles

Press article — Was the Backup Driver in an Uber Autonomous Car Crash Wrongfully Charged? https://www.phoenixnewtimes.com/news/uber-self-driving-crash-arizona-vasquez-wrongfully-charged-motion-11583771

I was all over this years ago. However, this still doesn’t mention the process is untenable and should not exist. Or that in many crash cases, time to effect a proper handover cannot be provided. And most importantly, that many “safety drivers” will have to be sacrificed so many edge/crash scenarios can be learned.

Rafaela Vasquez and Elaine Herzberg are both victims in Uber tragedy — There is a better way

More details here

The Autonomous Vehicle Industry can be Saved by doing the Opposite of what is being done now

· https://medium.com/@imispgh/the-autonomous-vehicle-industry-can-be-saved-by-doing-the-opposite-of-what-is-being-done-now-b4e5c6ae9237

SAE Autonomous Vehicle Engineering Magazine — Simulation’s Next Generation

· https://www.sae.org/news/2020/08/new-gen-av-simulation

My name is Michael DeKort — I am a former system engineer, engineering and program manager for Lockheed Martin. I worked in aircraft simulation, the software engineering manager for all of NORAD, the Aegis Weapon System, and on C4ISR for DHS.

Key Autonomous Vehicle Industry Participation

- Founder SAE On-Road Autonomous Driving Simulation Task Force

- Member SAE ORAD Verification and Validation Task Force

- Stakeholder USDOT VOICES (Virtual Open Innovation Collaborative Environment for Safety)

- Member SAE G-34 / EUROCAE WG-114 Artificial Intelligence in Aviation

- Stakeholder for UL4600 — Creating AV Safety Guidelines

- Member of the IEEE Artificial Intelligence & Autonomous Systems Policy Committee

- Presented the IEEE Barus Ethics Award for Post 9/11 DoD/DHS Whistleblowing Efforts
The uber driver that loves
Tags: Uber, Poetry, Southeast Asia, Love, Subcontinent

we can tell ourselves that we are loved / we can rest our aching for the moment / god knows mercy for those who are miserable / the uber driver anxiously awaits to meet his lover every night / the uber driver can’t afford a meal that cannot be shared / the uber driver can’t afford his car to breakdown / the uber driver cannot survive without love / he knows home in the arms of his lover / he knows calm in the warmth of a cuddle / he tells me, “what’s the point of anything if there’s no chasing?” / chasing / as a child / i chased ducks from one end of the street to another hoping to see them fly / as a teenager, i chased a girl from one end of our dreams to another hoping to see them come true / the uber driver says, “I do this part time" / & i think he means, he is trying to meet the wants of his family / the smog sits heavy on the streets / and i imagine the fog as the uncertainty of our dreams, how no matter how close the destination is, we must remember to reach safely / “i promise we will reach on time" / & another unkempt promise hooks and hangs from the busted lips of the uber driver / & when have we ever kept our promises / when have we ever remembered our dreams//
Twilio: upward and onward, rounding out its business
By: Tim Donovan
Tags: Uber, Salesforce, API, Lyft, Twilio

Jeff Lawson, Founder, CEO, & Chairman of Twilio Inc., rings the opening bell to celebrate his company’s IPO at the New York Stock Exchange (NYSE) in New York City, U.S., June 23, 2016.

According to Seeking Alpha, Twilio continues to remain in the top 1% of strongest stocks in the market. Despite also being in the 99th percentile based on growth metrics, many analysts are bearish the stock, some more recently calling it an egregious bubble. When a growth company like Twilio is undergoing rapid acceleration, it is only natural for people to become skeptical, seek shortcomings and claim the company overpriced — remember Amazon and Netflix?

Argument against Twilio — not about the company’s product and operations

As analysts have suggested, Wall Street’s pessimism on Twilio does not extend to its products and operations. Twilio provides intuitive and innovative communication products that help developers reach users in one line of code. More specifically, Twilio helps customers — platforms like Uber, Lyft and WhatsApp — communicate directly with users through its cloud-based Application Programming Interface or API.

For example, Uber works directly with Twilio to notify riders that their drivers have arrived via text message. This is majorly helpful to Uber’s development team. Instead of building an in-house system that integrates the many intricacies of carriers, regulation, hardware, and software, Uber developers simply need to plug into Twilio’s API to connect with customers directly.

So what’s the problem?

Bears on Wall Street are concerned that Twilio relies too much on a few, large customers like Uber, Lyft and WhatsApp to generate revenue. In 2015, for example, WhatsApp accounted for a solid 17% of the $166 million in revenue generated by Twilio. Moreover, in 2017, Twilio CEO Jeff Lawson announced that Uber, which accounted for 12% of Twilio’s revenue at the time, would be moving away from Twilio as its principal communications infrastructure provider. As a result, Twilio stock plummeted 30% during after-hours trading.

Why the pessimists are wrong?

Twilio has recognized this vulnerability. Diversifying its customer count last week was a move made to launch Twilio for Salesforce — a Twilio app that is now available on Salesforce Appexchange. This newly introduced application allows small businesses, non-profits and large commercial organizations using Salesforce to add out-of-the-box SMS to their existing workflow in Salesforce CRM.

As their partnership with Salesforce extends customer scope, Twilio’s $3 billion acquisition of SendGrid this past February will increase demand for the company’s services. With the SendGrid acquisition, developers and businesses can use Twilio to reach their customers over any voice, SMS, Facebook Messenger, WhatsApp, video and now email.
Financial Literacy in Our Everyday Lives
By: Sean Walsh
Tags: Uber

As a college kid headed out on the town for a Thursday night of fun, managing your financial stability for the next 30 years of your life is probably the last thing that crosses your mind. You’re probably more concerned with finding your next drink or whether your friend actually queued your favorite Beyoncé song on your drive out. And yet, these financial conversations seem to appear one way or another.

At least that’s the story of my latest Thursday night. On my way to meet friends in West Hollywood, I ended up having a fascinating conversation with my Uber driver. I hopped into his Toyota Camry with about a million things on my mind. Where were we going for dinner? Which bars would we hit first? Did Caitlin bring the Aux??? Immediately, the driver (whose name I’ll omit) began a rapid fire set questions. How was I doing? Where did I go to school? What did I do? Did I like the picture of his kid on the dash? As I answered each question, 3 more seemed to appear.

At first I was less-than-ethused to continue this 30-minute Uber ride, yet the conversation began to take a very surprising turn. When I told him that I was originally from the San Francisco area, he responded that he loved San Francisco and the culture of the city. He would have loved to live there, he told me, but that the city was just unbelievably expensive. We both agreed on that. He then explained that he had put himself through college here in Los Angeles.

In a more unexpected turn, he then opened up about an alcohol addiction, and that, despite his difficult financial circumstances, he would often take trips to San Francisco or around Los Angeles and party all the time. At this point, I was simultaneously sympathetic and confused, thinking the same thing you all probably are: Why was he telling me all of this?

As he continued, he described the positive changes he made to his life: quitting alcohol, spending more time with his family, and very interestingly, making personal financial investments. I congratulated him on these changes. He told me that he began investing his money in real estate, and that if he had known his opportunity to do so when he was younger, his life would have been completely different.

Uber was his opportunity to expose himself to different ideas, people, and places that he never would have experienced otherwise. He explained that it simultaneously served as a way for him to refocus his alcohol dependency, and give advice to others.

As we finally pulled to my friend’s house in West Hollywood, he stopped the car, turned around, and gave me a 5 minute speech about how to save, how to get into grad school, and what personal investments to make. Afterwards, he shook my hand and I was on my way.

What I had planned to be a quick and simple Uber ride turned into a lesson about the real-life implications of personal financial literacy. I learned financial literacy extends beyond simply saving enough to buy a nice house or retire comfortably. It had the power to change my Uber driver’s life completely once he put the power of his financial knowledge into his own hands.
Why I will never order from Uber Eats again!
By: Pia Shome
Tags: Uber, Hungry, Bad Service, UberEATS, Customer Success

Why I will never order from Uber Eats again!

It was a Friday evening! Like most couples, my husband and I love to eat from out! However, this Friday, we decided to call in for food and binge on some web series instead! We do allow ourselves such indulgences once in a while!

We thought of trying out the new delivery partner on the block! In India, Uber is new in the food delivery space. We like to promote organizations that are venturing into a new space. And the same rule applied to Uber Eats!

We went though the restaurant list. Choose the menu, and placed the order.

We encountered our first discomfort when the app did not allow for more that INR 700 ( USD 10) for cash on delivery option. It seemed odd. However, we said, let’s pay upfront by the credit card. What can possibly go wrong!

And that is where we made the mistake!

We got a call from the Uber delivery agent asking for directions. We gave him the complete direction. We are happy and waiting in anticipation for the food.

To build some context, we stay in a high rise building. The building has a security gate. The security gate is at a 100 meters distance from the lobby. It is by any watch a two minute walk.

The Uber delivery boy called us, he said “your security is not letting me ride my bike to the lobby”! We said, it is a standard practice, please walk in.

To our surprise, he said, I won’t walk. I am taking your order away!

We said we paid for the order, Please bring it in! You are just a 2 minutes walk away! To this he replied, complain to the customer care! I won’t give you the food!

And there with him, our Friday food walked out leaving us hungry and angry!

What a great start to the weekend!

Dear Uber,

1. You do not have a customer care number! You are running a food business. It is a business of dealing with hunger, emotions and people. You can’t be only basing it on an incomplete app support!

2. There is only a ‘help’ section! The drop down menu is not exhaustive. I am also not sure if you even read what we wrote! I say so because you just refunded my money saying ‘order could not be completed’

3. Are you training your delivery boys? Do they understand that delivering food is all about meeting the demands of someone who is hungry ?

4. The process is the most impersonal process I have experienced. Are you not keen to know what your customers have to tell you ?

I thought of letting you know !
Carpooling VS Ride-sharing. TripBudy is a ridesharing platform that…
By: Daniel Sordello
Tags: Uber, Carpooling, Lyft Vs Taxi, Transportation, Lyft

TripBudy is a ridesharing platform that allows you to fill empty seats in your car with riders, or you can fill an empty seat in someone else’s car as well. Operating on a system of riders and drivers two things are accomplished, saving money and reducing the number of cars on the road.

A driver saves by being able to negate, expenses such as parking fees, fuel costs, or even vehicle maintenance. Passengers save money by avoiding incurring expenses such as parking fees, fuel costs, and vehicle maintenance. Simple math dictates four people in one car means three fewer cars on the road. While these numbers may seem like drops in a bucket, in the grand scheme of things these sort of numbers add up quickly. When achieved on a large scale, this kind of reduction can lead to things such as safer driving conditions for everyone, and less traffic overall, to name a few.

In terms of actually finding a ride or riders, tripBuddy operates based off locations. If you plan to go into the city for a day from the suburbs and you live 20 minutes away, tripBuddy finds riders with a similar route and general destination as yours. So if you’re going to a baseball game and your rider is going to a concert a block or two away, the venues are close enough that the rider and driver can each get to their respective destinations with ease.

What tripBuddy is not, is a ridesharing platform akin to Uber or Lyft. Rather than bringing a specific person(s) to a specific destination, tripBuddy brings many people to the same general area.

The main distinction between a tripBuddy driver and one for other ridesharing platforms is the destination. A traditional rideshare platform has drivers operate similarly to taxies. They look for fares to bring from point A to point B while tripBuddy drivers are already going to point B, thus generating income on a trip the driver was going to do regardless of getting riders.
Autonomous Driving in 2025
Tags: Google, Uber, Future, Autonomous Cars, Self Driving Cars

I’ve written this to document my ideas for where autonomous driving is heading and what companies and regulators need to focus on. For those unfamiliar with autonomous driving I make reference to the 5 levels of autonomy, these are graphed below, the rest should be pretty self explanatory. I will caveat this by expressing that I am an optimist to this technology, what I plan to identify are the ways companies should be focusing their attentions to get the technology to a level of regulatory approval, breaking down the psychological barriers of future consumers; and for safety to be a key focus area in what are the most risk prone development years. We have seen this many times in history, from steam engines exploding to plane crashes, where technology development has been stalled, therefor stalling progress.

The SAE (Society of Automotive Engineers) Automation Levels

Let’s start out by looking at the key players in the market in all autonomous systems operating in the outside world today.

Uber X Volvo

Uber — Uber Advanced technologies Group found themselves in a bit of hot soup last year after killing a cyclist. This sent shock waves through the autonomous driving community that big tech companies were letting untested and risky technologies out on the open road.

The approach Uber has taken, like many of the other large tech firms, is to build fully autonomous systems which are able to navigate multiple scenarios and environments. This allows for the most direct approach to full automation. The company is operating level 5 automation and needs a driver present in the vehicle as a security measure.

Waymo X Jaguar Land Rover, one of its many collaborations

Waymo — After spinning off from its parent Google, the company has taken a very similar approach to that of Uber in developing fully autonomous systems. Since beginning its pilot program in Phoenix, Arizona in April 2017 the company has recently begun charging for rides. The company chose Arizona due to its approach to regulation and their all round good weather giving the vehicles LiDAR technology the best environment to operate in (we do not want to see this running in any polar vortices just yet). The company has partnered with a number of OEMs to manufacture its vehicles. The company is operating level 5 automation and needs a driver present in the vehicle.

Cruise X Chevrolet

Cruise (GM) — Unlike Waymo, Cruise is all about doing the full stack. The company is pursuing a different market, currently that of SF, however, the companies are bound to clash heads in the coming years with the addition of Uber (pending further accidents) which will most likely lead to the biggest winner of this three horse race being the consumer. The company is operating level 5 automation and needs a driver present in the vehicle.
Tips for Finding an Ultimate Roofing Company
By: Jacobsen Elisa
Tags: Uber

Any building ought to have appealing properties since this is what will make it attractive and worthwhile. The numerous structures which your building have should exhibit a high level of robustness since this will be suitable. Appreciate the fact that the roofing structures which your building have ought to be as appealing as possible. It is a suitable thing to go for the ideal roofing service providers. After coming across different roofing agencies, it will catch your attention that there are several of them from which you ought to engage the best. Learn more about blenheim roofer, go here.



The roofing contractors will not only be perfect when it comes to the installation work as they will as well render the repairing solutions. You might sometimes wonder the right strategy to use when it comes to narrowing down to the perfect roofing service providers. There is a need to ensure that you learn a few things which carry much weight when choosing the best. By going through this article, you will come to discover the various elements which you can think of when finding a suitable roofing firm. Start by settling for the company which is known widely for the rendering of solutions which are appealing. Find out for further details on blenheim roofing company right here.



The advantage of these companies is that they will provide you with the services which are matching the high level of quality. Such contractors will at the same time take time to plan for their services well such that they will offer a long-lasting solution. On the other hand, settle for the roofing agency which is modern when it comes to rendering solutions to clients. It is a suitable thing to ensure that you target the roofing firm which is perfect with reference to the offering of solutions which are perfect and on point.



Choose the roofing service providers who render you services which are affordable. See the essence doing your market evaluation with reference to the difference in the billing for the roofing services by the numerous roofing contractors who are present. However, never dwell so much on the service fees and forget about the element of quality as it carry much weight. Sometimes going beyond the budget will lead you to the contractor who is excellent. Take a look at this link https://en.wikipedia.org/wiki/Roofing for more information.



Finally, it will be a suitable thing to target the roofing firm which has served many people before you. Such roofing contractors will most of the time know about the aspects which will make their services perfect.
Uber and Lyft Are Dead (They Just Don’t Know It Yet)
By: Jared A. Brock
Tags: Uber, Democracy, Lyft, Marketing, Business

Welcome to the World of Dark Marketing

“Dark Marketing is the latest trend in digital marketing and it’s changing online advertising like no other trend ever did. Dark Marketing is the combination of highly fragmented, segmented, and extremely personalized retargeting marketing campaigns. Industry leaders are shifting towards covert strategies to specifically target the young…” — Ankit Sharma

It was an unfair fight from the start

In addition to sending their top executives on a media marketing mega-tour — in which they threatened to leave the state if they didn’t get their law — the two companies invested more than $200 million on radio, print, digital, billboard, and mailer ads, in addition to hiring dozens of lobbyists and academic researchers to back the supposed benefits of denying workers full employment status.

It’s notoriously hard to win a yes in California ballot measures — PG&E dropped more than $40 million in 2010 and still lost — but Uber and Lyft had another advantage besides money in their fight against widest-spread wellbeing: the apps themselves.

In the weeks leading up to the vote, the companies turned their own apps into round-the-clock marketing machines; spamming their customers, bombarding them with push notifications, threatening price increases and longer wait times, while simultaneously claiming drivers would lose their jobs and that it would do irreparable harm to the corporate business model.

This really begs the question: If denying workers the rights of employees is the only way for workers to keep their jobs and keep the company profitable, is the rideshare business model ethical?

Clearly, smart companies only spend nine-figure sums if they expect to reap an even larger return.

Lyft and Uber not only spammed their customers, but they also inundated their employees with dire warnings about what would happen to their jobs if Prop 22 were to fail. Drivers had to click OK before they could continue with the app and do their job. That’s right: forced marketing. There’s a definition for this sort of behavior: political coercion.

In the end, grassroots workers’ rights groups didn’t stand a chance against two companies with a combined market cap of $112 billion, and the rideshare giants Dark-Marketed their way to a yes.

After the law passed, they went ahead and raised their prices anyway.

Uber’s CEO was gleeful on an earnings call, saying:

“Going forward, you’ll see us more loudly advocating for new laws like Prop 22.”

They’ve set a terrible legal precedent that other states are sure to follow, but it also sets a perverse marketing strategy precedent for tech companies to manipulate democracy in the future.