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“A human is worth more if they’re addicted, polarized, outraged, misinformed and narcissistic because that’s better for producing an effect in human attention.”  This strong statement from Tristan Harris describes the era of attention capitalism and it catapulted the day for one of Bloomberg’s largest tech events. At “Sooner Than You Think” (STYT), technologists, policymakers, educators and journalists gathered to talk about the impact of technology on our society and the balance between innovation and obligation in the industry.

I found myself mixed with concern, anger and optimism as we explored the crossroads and paved a path forward. Read on to see the  three critical themes I took home with me.

I’ll believe it when I see it.

Maybe we shouldn’t? Creating fakes isn’t a new concept – many of us have used Photoshop to add a mustache to a friend’s face or make it look we’re in a picture with our favorite TV characters. But now we have the technology to create fake audio and video that’s incredibly believable and imperceptible to the human eyes and ears. We also have the ability  to distribute this fabricated content to the global masses or to a micro-target audience with the help of algorithms. The reality is that this deep fake-making technology is amoral, its use can be benign like making comedic satire or it can be weaponized by placing women’s faces in pornography or by creating messages of hate.

Check out this deep fake Jordan Peele made by using AI on a video of Barack Obama.

In this video, Jordan Peele calls on us to be more vigilant about what we trust on the internet. Shamir Alibhai mentioned at STYT that not only is it easier to create deepfakes, but eventually the deepfake creation technology will outcompete deepfake detection technology. It only takes about 15 minutes of human work to make a deepfake by leveraging cloud computing to do the rest. You can literally download this and make one today, and I just googled that.

Alibhai emphasized the urgent need in creating a system to authenticate critical content and videos that have an evidentiary character, such as film captured by a bystander, security footage or a camera on a police officer’s car. We shouldn’t let technology and our inability to detect fakes with our naked eye get in the way of due process.

Scary thought: Just imagine someone deepfaking a benign video of you speaking and making you appear to say something racist, and then it goes viral on twitter? Could you be fired or sued?  How do you prove that it’s fake?

Check out these other fakes that have taken over the internet.

Free speech vs. paid speech

Three men and two women sit on a gray stage in black chairs. They appear to be discussing something as a large screen behind them shows their headshots and titles, along with the words "Protecting Our Democracy: Counting down to 2020"

Fake news that proliferates, aggravates, incites action and polarizes us has been the topic of discussion for the last few years. Shamir defined fake news as “deceptive blogs with a veneer of newsworthiness being shared online.” According to an MIT study, fake news spreads six times faster than true information on Twitter.

At SYTY, Brittany Kaiser (pictured above in the second chair from the right) who used to run business development at Cambridge Analytica (CA) took the stage and spoke about how they leveraged the tremendous amount of Facebook user data to identify and target the “persuadables”, those who haven’t made up their mind yet and could be persuaded to decide in a specific direction. For the 2016 election, CA bombarded these “persuadable” users with over 5 million pieces of customized content to create a desired perception of the world that CA wanted them to have. You can watch The Great Hack or read Vox’s op-ed to learn more.

The panel pointed out that while Facebook shut down over 2 billion fake user accounts in three months this year, they still won’t fact check political ads or posts by candidates, even if it violates the site’s hate speech rules. This decision came from Facebook’s desire to be neutral during the election, but this may further proliferate misinformation and malevolence being spread by those that can afford to create and promote fake news.

This stance upset the staff at Facebook, leading them to write an open letter to Mark Zuckerberg demanding a more active stance on misinformation. They suggested solutions “where they submit campaign ads to fact-checking, limit microtargeting, cap spending, observe silence periods or at least warn users.”

What can we do about the state of misinformation?

Two men and one woman sit on a gray stage in black chairs. The screen behind them shows their headshots and the words "The Role of Government in our Data Privacy"

The speakers shared some advice for us to consider as we grapple with the current state and look towards the future with optimism, here’s what they said:

Don’t give up

Actress Kerry Washington shared that it’s important to be aware and active during the election off-seasons, so we can make sure that we’re selecting the right leaders to represent our community. Speakers also suggested readers should be mindful of what to follow and who to trust.

Make products with privacy in mind

DuckDuckGo, Density, and FourSquare shared how they’re leading profitable companies without commoditizing user data and only tracking what’s necessary. Jeff Gleuck of FourSquare emphasized how they even have a blacklist of locations they do not share to protect groups from harm, like locations of Planned Parenthoods and LGBT spaces. Also, give your users a “terms and conditions” they can read and understand. Yes, please!

The Chief Information Officer of Equifax relayed that you should store data assuming that you’ll have a data breach, so ask yourself “How can we store less valuable information?”

The government needs a new framework

Former FCC commissioner, Mignon L Clyburn (pictured above on the far right) points out that the reason the government hasn’t been able to regulate big tech is that “we’ve got a 19th-century framework for 21st-century problems”. She also points out that as long as we’re all working in our own silos, we won’t make progress. Instead, lawmakers, regulators, ethicists and technologists need to actually hear each other, get past their own industry cultures and work together.

Tom Bossert, who served as the homeland security advisor to two presidents, emphasized that this new framework of rules and standards needs to account for the current and evolving state of technology and a process of accountability and responsibility.

Take a stance. 

Many panelists suggested that leaders need to take a stance on where they stand and use it to inform their organizational decisions.

Well, Jack Dorsey, CEO of Twitter took a stance on misinformation this Wednesday. He tweeted that he’s banning ads from candidates on Twitter globally:

Twitter also won’t accept payments to promote tweets or other ads that take a position on policy issues, such as immigration, health care, national security, and climate change.

The worldwide web turned 30 years old this year and it’s still learning and evolving, and us with it. The creators built it to efficiently share information from computer to computer, from person to person. They probably never imagined a day where the internet would need passwords, rules and protections. So by design, it was open, vulnerable, and a big unknown.

Similarly, we may be making new technology today with unforeseeable repercussions. So it’s critical that we’re having these conversations about truth, trust, and responsibility while demanding ethical standards and challenging business models anchored around selling user data and creating digital addictions.

By

As the Director of Innovation at the Ad Council, Ariba is charged with creating digital products that make a measurable impact, scaling design thinking practices and exploring future-forward technology for the organization. With over 10 years of experience in user-focused product design and leading workshops for entrepreneurs in global cities, Ariba leverages her life experience as an immigrant, startup mindset, and scientific approach to create digital products for social good and empower an innovative culture. In her spare time, she teaches underprivileged high school students UX design, advocates for inclusive design, goes on mountain-climbing adventures (Mt. Kilimanjaro is her favorite), and knits more scarves than she’ll ever need.

Sourced from AdLibbing

By Natan Pollack

Today we navigate our way across cities, pull up electronic tickets, purchase items, monitor our health, and, of course, stay connected with friends and family on our smartphones. The smartphone is one of those innovations that make us think,  “how did I ever function without it?” Smartphones revolutionized our personal lives, but there’s a megatrend set to disrupt the business world; it’s called augmented analytics.

Augmented analytics is on the cusp of becoming the business world’s next significant evolution.

Gartner identified augmented analytics as to the number 1 top trend for data and analytics technology in 2019, and market leaders are already starting to invest in this burgeoning industry.

SAP recently acquired augmented people analytics company Qualtrics for $8 billion, shelling out a price equivalent to over 20x the company’s current revenue. A newcomer to the game, Denver based startup Nodin raised $5 million in funding this past March, a month before even launching its platform.

The global market for augmented analytics is forecasted to reach $29.86 billion by 2025. But just what is augmented analytics, and what makes it such a hot new trend?

Data or die

According to a recent study by Forbes Insights and Treasure Data, only 13% of companies can be considered “leaders” in leveraging the full potential of their customer data. The full potential of the customer data is significant, as 55% of executives think these insights to be valuable in achieving disruptive innovation.

Companies must now collect, clean, and translate their raw data into insights they can use to build better products and reach target audiences.

In today’s fast-paced business world, data-driven decisions are no longer a nice to have; they’re a necessity to stay competitive and on top of market volatility. To get ahead, significant players from Booking.com to PepsiCo are relying on teams of data analysts to collect, clean, and analyze the surge of data now being generated.

SME’s are also leveraging their data to gain a competitive advantage in a sea of new competitors popping up every day. The problem is that data analysts are not only scarce in number; they’re also costly, especially for SMEs.

Even for companies that do have data scientists on board, the sheer volume of the data we’re now collecting through various platforms and tools means that they spend more of their time on activities like data preparation and visualization, leaving less time for actual analysis.

Augmented analytics harnesses the power of AI and machine learning to automate these tasks and generate insights.

Let’s say you’re an ecommerce store that’s seen a sudden decrease in sales on your Shopify account. To find out why you’d have to comb through your company’s data and find insights by:

  • Logging in to Google Analytics to analyze patterns in your website traffic.
  • Checking out the performance of your social media accounts and ad campaigns.
  • Reassessing your keywords on Google Adwords.
  • Investigating new competitors or changes in the market.

Instead, augmented analytics tools collect and analyze all your data together to identify potential causes and automatically generate reports with actionable insights.

Here are three significant ways augmented analytics will disrupt the business world:

We’re in a data race – the winner takes the money.

With most businesses adopting artificial decision-making capabilities, we’re now in a race to see who can make the faster, better business decisions. Our businesses are like data-guzzling V12 engines that need data to fuel growth. Automating this process, and using augmented analytics to spot growth opportunities in your data, before your competitors, means you win the race.

Gartner believes that by 2020, over 40% of data science tasks will be automated. The automation will allow data scientists to spend less time on repetitive tasks and more time on strategic analysis and decision-making. Not only does it take the manual labor out of their job, but it also does it faster and eliminates the potential for human error.

Bring together the whole picture.

At the moment, most company’s data lives on several different platforms – isolated. Only 34% of executives agreed they have one aggregated view of all their customer data points. Not only is this inefficient, but it also blocks businesses from making informed decisions. We shouldn’t be looking at how each part of the engine works separately but how it all works together.

Having data points integrated into a rapid reporting system, such as Aerialscoop or DataBox, allows you to track the entire customer journey on one platform, all the way from lead generation until earning your first Dollar from the client. It also provides for better cohesion and collaboration across the organization. It’s not just ‘how is my marketing team doing on their KPIs?’ — but how are the marketing team’s results directly impacting my revenue growth and retention rates?

Democratize your data analytics.

Meanwhile, for smaller companies that don’t have the means to hire a team of data scientists (currently the global average salary is $90k), augmented analytics will make data-driven insights accessible to the masses. The accessibility is expected to be a major wave of development for the next five years.

According to Gartner, through 2020, the number of citizen data scientists will grow five times faster than professional data scientists. This means everyone from executives to marketeers will have the power to make data-driven decisions, without having to rely on data science professionals to provide the information they need.

Having the information easily accessible to all opens doors for SME’s to accelerate their growth at an exponential rate across departments. If there was ever a time that smaller, more nimble start-ups were able to pose a real threat to major companies, the democratization of data analytics ought to be the catalyst.

Much like smartphones have become the tool we can’t imagine our lives without, augmented analytics will set a new standard for business growth.

Those who start to leverage this technology early on will reap the benefits that faster, aggregated, and accessible data can bring. Where will your company stand in the data race of the future?

By Natan Pollack

Sourced from readwrite

Unique Communications, Dublin City

We are currently looking for Contract/Freelance iOS & Android Developer urgently required to work with us on a contract basis.

Unique Communications is a full services digital marketing agency based in the Guinness Enterprise Centre in the heart of Digital hub area.

Requirements

  • Relevant qualifications
  • 2-3 yrs. experience
  • Fluent English
  • Expertise in developing fully featured native mobile applications for iOS &/or Android
  • Expertise in Objective-C, Java
  • Swift or functional programming is a plus
  • Have some knowledge of PHP
  • Good understanding of web services, networking and standards (HTTP, RESTful, JSON)
  • A portfolio or examples of apps you have published to the app store
  • Solid software engineering skills; being comfortable with software design, coding, algorithms and data structures
  • Source code management (Git, Bitbucket)
  • Have experience in using Amazon Aws
  • Ability to work to tight deadlines
  • Strong attention to detail

How to Apply

Please email your CV, and answer the questions.

Click HERE to apply for this job.

Ripple Marketing, Sandyford, Dublin

Salary range depending on experience- €50k-€60k

We’re looking for a Head of Digital – Digital Lead to join us here in Ripple Marketing!

An exciting opportunity is available for a motivated digital marketing professional to drive current digital campaigns and drive digital growth within the business.

Our business is growing and varied, and we are looking for an energetic Head of Digital who is keen to join a hardworking, fun loving team and drive the digital growth of Ripple Marketing!

Key responsibilities include:

In this role, you will have the opportunity to drive the digital channels, google adwords and SEO/organic component for clients and meet a brief to deliver on their digital needs. In this role, you will be given the opportunity to take ownership over a large portfolio of Digital Marketing clients and will be tasked with and incentivised to grow this portfolio and take Ripple’s already impressive Digital Marketing offerings to the next level.

You will have full ownership and drive end to end digital projects for the organisation, engaging with current and new clients and working closely with an already established digital team.

This role offers a flexible work environment, where a candidate’s career progression and work-life balance are of equal importance!

If the below sounds like you, please get in touch!

  • Degree in Marketing/Digital Marketing (or related discipline)
  • 4+ years’ experience in the online marketing space
  • A proven track record of delivering and managing digital projects
  • Comprehensive awareness and understanding of current digital marketing trends
  • Experience in digital advertising and budget management
  • Relevant strong experience in strategic planning, content creation, management and reporting across multiple digital channels
  • Ability to manage and inspire a marketing team.
  • A proven ability to identify and drive new business and growth within the Digital Marketing sphere.
  • A proven track record of generating sales/leads via digital channels
  • Flawless attention to detail
  • A can-do attitude with superb work ethic

Salary range depending on experience- €50k-€60k

As a reward for your commitment to the role you will receive an excellent salary and the chance to work with some of the biggest brands and events in Ireland. You will join and drive a young dynamic team with the opportunity to influence and create major digital marketing campaigns and contribute to driving further business growth.

Click HERE to apply for this job.

By

According to a recent report by IDC, digital transformation spending is expected to surpass $6 trillion dollars within the next four years, and it’s believed that enterprises globally will spend more than $1 trillion on digital transformation before the end of 2019 alone.

The report also notes that industries like process and discrete manufacturing and transportation will be some of the biggest spenders. These investments are fueling the growth of machine learning (ML) and the internet of things (IoT) to improve customer experiences and operational efficiency and accuracy. As companies have begun adopting digital transformations, there are a few things I’m looking forward to seeing more of in 2020.

1. Big Data Grows To Ginormous Data

According to a Network World article, “IDC predicts that the collective sum of the world’s data will grow from 33 zettabytes this year [2018] to 175ZB by 2025, for a compounded annual growth rate of 61%.” (One zettabyte is equal to one trillion gigabytes.) This means that we’ll see not only a massive increase in the amount of IoT-generated and real-time data, but an abundance of new data created and managed by enterprises.

By 2025, nearly 60% of the 175 zettabytes of data will be created and managed by enterprises versus consumers.  Driving this growth is IoT edge devices sending waves of information to the cloud. 

2. IoT And ML Are No Longer Future Technologies

The workforce is just not equipped to analyze such large amounts of data, so enterprises will be (and already are) looking for new ways to do so using ML and augmentation. As a consequence of ginormous data, IoT should be viewed as the backbone of today’s data-driven economy. To make sense of this data, the evolution of IoT products and services will become less focused on core technology and more focused on technologies that make better use of the data gathered.

3. Data As A Service

With all of the data developed day to day — in 2020, every person will create 1.7MB of data per second — it only makes sense to use this data to make more knowledgeable business decisions.

For example, KAR Global has released a platform that gives automotive dealers a wide-angle view of cars currently in demand. The platform also shows the best ROIs and how dealers can move less-desirable vehicles, in addition to inventory segmentation analyses and recommendations for remarketing. All of this uses data available from KAR and its customers in a proprietary way that benefits the auto sales industry as a whole. We should expect other industries to begin using the DaaS model in the same way for decision-making.

4. The Decline Of Packaged Apps

Instead of downloading apps, soon progressive web apps (PWAs) will be much more commonplace. PWAs are accessed the same way as those downloaded from app stores, but they load faster, they are more secure and they are far smaller in size. Companies such as Lumavate help developers in industries such as motorsports, medical manufacturing, construction, and financial services move from native applications to cost-effective PWAs that ultimately deliver a better user experience and free up space on devices. 

5. Prescriptive Analytics

Prescriptive analytics goes beyond forecasting possible options and instead suggests a range of actions and the potential outcomes of those actions. As more tools become available, this type of data analyzation is becoming a holy grail.

Autonomous vehicles are fantastic examples. A self-driving car must make millions of calculations based on analyzed data to decide when to turn, change lanes and so on.

Oil and gas industries are also using prescriptive analytics to assess supply, demand, pricing and impacts on the industry when they change. Prescriptive and predictive analytics work together as business intelligence that gives executives insight, as well as foresight, into their company data.

6. More Jobs Will Actually Be Created From AI Rather Than Lost

AI is predicted to eliminate 1.8 million jobs but also create 2.3 million jobs in 2020. Industries such as healthcare, education and the public sector will see growing job demands. While middle- and low-level positions will take the biggest hits, new roles for these types of workers will open up in sectors such as solar-powered energy, which is now the fastest-growing industry for job creation. Industrial manufacturing is also an industry working to reskill its workforce, marrying the technical and nontechnical know-how of its employees for the digital transformation.

7. Work Augmentation Through Machine Learning

Machine learning used to mean automating tasks and replacing human work. The focus now is on ML’s ability to augment human work to make us more productive and efficient. In 2020, we’ll see machine learning models engineered to optimize logistics, retail and robotics. Things like recommendation engines, fraud detection, and robotic process automation will become standard and make industry competition fierce.

8. Robot Process Automation (RPA)

This year, Deloitte saw enterprises double the number of intelligent automation tools (e.g., robot process automation) for day-to-day business tasks such as inventory management. The manufacturing industry, in particular, has been watching RPA for several years and will increase its adoption in 2020. Already, successful RPA solutions in manufacturing include order fulfillment, purchase order processing, inventory reports and transportation management. Executives who have implemented RPA note that employees are more engaged by way of strategic and creative thinking.

No matter the industry, investments in IoT, ML and data analytics will increasingly be required to stay competitive. Most of what we’ll see in technology next year and in the future will center on IoT products and services that enable us to comprehend data acquired by the second. Building and analyzing data now gives enterprises more information than ever before. In 2020, they will use this data to elevate customer, employee and stakeholder experiences.

By

John is the CEO of ClearObject.com, an IoT systems integrator and Inc. Magazine’s fastest growing Indiana IT services company for 2014-2017.

Sourced from Forbes

By Benjamin Obi Tayo Ph.D.

ata Science is such a broad field that includes several subdivisions like data preparation and exploration; data representation and transformation; data visualization and presentation; predictive analytics; machine learning, etc. For beginners, it’s only natural to raise the following question: What skills do I need to become a data scientist?

This article will discuss 10 essential skills that are necessary for practicing data scientists. These skills could be grouped into 2 categories, namely, technological skills (Math & Statistics, Coding Skills, Data Wrangling & Preprocessing Skills, Data Visualization Skills, Machine Learning Skills,and Real World Project Skills) and soft skills (Communication Skills, Lifelong Learning Skills, Team Player Skills and Ethical Skills).

Data science is a field that is ever-evolving, however mastering the foundations of data science will provide you with the necessary background that you need to pursue advance concepts such as deep learning, artificial intelligence, etc. This article will discuss 10 essential skills for practicing data scientists.

10 Essential Skills You Need to Know to Start Doing Data Science

1. Mathematics and Statistics Skills

(I) Statistics and Probability

Statistics and Probability is used for visualization of features, data preprocessing, feature transformation, data imputation, dimensionality reduction, feature engineering, model evaluation, etc. Here are the topics you need to be familiar with:

a) Mean

b) Median

c) Mode

d) Standard deviation/variance

e) Correlation coefficient and the covariance matrix

f) Probability distributions (Binomial, Poisson, Normal)

g) p-value

h) MSE (mean square error)

i) R2 Score

j) Baye’s Theorem (Precision, Recall, Positive Predictive Value, Negative Predictive Value, Confusion Matrix, ROC Curve)

k) A/B Testing

l) Monte Carlo Simulation

(II) Multivariable Calculus

Most machine learning models are built with a data set having several features or predictors. Hence familiarity with multivariable calculus is extremely important for building a machine learning model. Here are the topics you need to be familiar with:

a) Functions of several variables

b) Derivatives and gradients

c) Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function

d) Cost function

e) Plotting of functions

f) Minimum and Maximum values of a function

(III) Linear Algebra

Linear algebra is the most important math skill in machine learning. A data set is represented as a matrix. Linear algebra is used in data preprocessing, data transformation, and model evaluation. Here are the topics you need to be familiar with:

a) Vectors

b) Matrices

c) Transpose of a matrix

d) The inverse of a matrix

e) The determinant of a matrix

f) Dot product

g) Eigenvalues

h) Eigenvectors

(IV) Optimization Methods

Most machine learning algorithms perform predictive modeling by minimizing an objective function, thereby learning the weights that must be applied to the testing data in order to obtain the predicted labels. Here are the topics you need to be familiar with:

a) Cost function/Objective function

b) Likelihood function

c) Error function

d) Gradient Descent Algorithm and its variants (e.g. Stochastic Gradient Descent Algorithm)

Find out more about the gradient descent algorithm here: Machine Learning: How the Gradient Descent Algorithm Works.

2. Essential Programming Skills

Programming skills are essential in data science. Since Python and R are considered the 2 most popular programming languages in data science, essential knowledge in both languages are crucial. Some organizations may only require skills in either R or Python, not both.

(I) Skills in Python

Be familiar with basic programming skills in python. Here are the most important packages that you should master how to use:

a) Numpy

b) Pandas

c) Matplotlib

d) Seaborn

e) Scikit-learn

f) PyTorch

(ii) Skills in R

a) Tidyverse

b) Dplyr

c) Ggplot2

d) Caret

e) Stringr

(iii) Skills in Other Programming Languages

Skills in the following programming languages may be required by some organizations or industries:

a) Excel

b) Tableau

c) Hadoop

d) SQL

e) Spark

3. Data Wrangling and Proprocessing Skills

Data is key for any analysis in data science, be it inferential analysis, predictive analysis, or prescriptive analysis. The predictive power of a model depends on the quality of the data that was used in building the model. Data comes in different forms such as text, table, image, voice or video. Most often, data that is used for analysis has to be mined, processed and transformed to render it to a form suitable for further analysis.

i) Data Wrangling: The process of data wrangling is a critical step for any data scientist. Very rarely is data easily accessible in a data science project for analysis. It’s more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. Knowing how to wrangle and clean data will enable you to derive critical insights from your data that would otherwise be hidden.

ii) Data Preprocessing: Knowledge about data preprocessing is very important and include topics such as:

a) Dealing with missing data

b) Data imputation

c) Handling categorical data

d) Encoding class labels for classification problems

e) Techniques of feature transformation and dimensionality reduction such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

4. Data Visualization Skills

Understand the essential components of a good data visualization.

a) Data Component: An important first step in deciding how to visualize data is to know what type of data it is, e.g. categorical data, discrete data, continuous data, time series data, etc.

b) Geometric Component: Here is where you decide what kind of visualization is suitable for your data, e.g. scatter plot, line graphs, barplots, histograms, qqplots, smooth densities, boxplots, pairplots, heatmaps, etc.

c) Mapping Component: Here you need to decide what variable to use as your x-variable and what to use as your y-variable. This is important especially when your dataset is multi-dimensional with several features.

d) Scale Component: Here you decide what kind of scales to use, e.g. linear scale, log scale, etc.

e) Labels Component: This include things like axes labels, titles, legends, font size to use, etc.

f) Ethical Component: Here, you want to make sure your visualization tells the true story. You need to be aware of your actions when cleaning, summarizing, manipulating and producing a data visualization and ensure you aren’t using your visualization to mislead or manipulate your audience.

5. Basic Machine Learning Skills

Machine Learning is a very important branch of data science. It is important to understand the machine learning framework: Problem Framing; Data Analysis; Model Building, Testing &Evaluation; and Model Application. Find out more about the machine learning framework from here: The Machine Learning Process.

The following are important machine learning algorithms to be familiar with.

i) Supervised Learning (Continuous Variable Prediction)

a) Basic regression

b) Multiregression analysis

c) Regularized regression

ii) Supervised Learning (Discrete Variable Prediction)

a) Logistic Regression Classifier

b) Support Vector Machine Classifier

c) K-nearest neighbor (KNN) Classifier

d) Decision Tree Classifier

e) Random Forest Classifier

iii) Unsupervised Learning

a) Kmeans clustering algorithm

6. Skills from Real World Capstone Data Science Projects

Skills acquired from course work alone will not make your a data scientist. A qualified data scientist must be able to demonstrate evidence of successful completion of a real world data science project that includes every stages in data science and machine learning process such as problem framing, data acquisition and analysis, model building, model testing, model evaluation, and deploying model. Real world data science projects could be found in the following:

a) Kaggle Projects

b) Internships

c) From Interviews

7. Communication Skills

Data scientists need to be able communicate their ideas with other members of the team or with business administrators in their organizations. Good communication skills would play a key role here to be able to convey and present very technical information to people with little or no understanding of technical concepts in data science. Good communication skills will help foster an atmosphere of unity and togetherness with other team members such as data analysts, data engineers, field engineers, etc.

8. Be a Lifelong Learner

Data science is a field that is ever-evolving, so be prepared to embrace and learn new technologies. One way to keep in touch with developments in the field is to network with other data scientists. Some platforms that promote networking are LinkedIn, github, and medium (Towards Data Science and Towards AI publications). The platforms are very useful for up-to-date information about recent developments in the field.

9. Team Player Skills

As a data scientist, you will be working in a team of data analysts, engineers, administrators, so you need good communication skills. You need to be a good listener too, especially during early project development phases where you need to rely on engineers or other personnel to be able to design and frame a good data science project. Being a good team player world help you to thrive in a business environment and maintain good relationships with other members of your team as well as administrators or directors of your organization.

10. Ethical Skills in Data Science

Understand the implication of your project. Be truthful to yourself. Avoid manipulating data or using a method that will intentionally produce bias in results. Be ethical in all phases from data collection, to analysis, to model building, analysis, testing and application. Avoid fabricating results for the purpose of misleading or manipulating your audience. Be ethical in the way you interpret the findings from your data science project.

In summary, we’ve discussed 10 essential skills needed for practicing data scientists. Data science is a field that is ever-evolving, however mastering the foundations of data science will provide you with the necessary background that you need to pursue advance concepts such as deep learning, artificial intelligence, etc.

By Benjamin Obi Tayo Ph.D.

Sourced from Towards Data Science

By William Arruda

Plenty of people who would never jump into the performing arts become actors as soon as they enter the workplace. They put on a work face that differs from the person they are at home, and while that might seem like an effective short-term strategy, it could actually contribute to burnout and hinder their job performance over time.

According to data collected by Gallup, 67% of workers report feeling burned out on the job at least some of the time. These overstressed employees tend to be 63% more likely to call in sick and 13% less confident in their work. In fact, burnout makes employees half as likely to bring up performance goals with their supervisors. When workers refuse to admit their burnout to managers and ask for help, they carry on with a business-as-usual act that only leads everyone deeper into a quagmire of reduced productivity, engagement and satisfaction.

Forbes contributor Mike Robbins sees this as a result of the compartmentalization we expect of ourselves and our co-workers. In his book, Bring Your Whole Self to Work, he advocates shedding the veil between our personal and professional faces to change the way we feel about work and our part in it.

In a Forbes interview, Robbins declares, “Bringing our whole selves to work means showing up authentically, leading with humility and remembering that we’re all vulnerable, imperfect human beings doing the best we can. It’s also about having the courage to take risks, speak up, ask for help, connect with others in a genuine way and allow ourselves to be truly seen.” New York City Executive Coach (and my business partner) Ora Shtull says, “When employees show up as wholly human at work, they create a ripple effect. Their willingness to live authentically gives those around them permission. This creates a work environment where talent is engaged and creative.”

Of course, this doesn’t mean showing up in bunny slippers with a remote control in hand. It also doesn’t give employees carte blanche to act disrespectfully in the name of self-expression. Instead, bringing a whole-person mentality to a job involves being genuine and intentional, all with the end goal of boosting your confidence and your performance. Authenticity is the foundation of effective personal branding. And personal branding helps you align who you are with what you do and how you do it.

If you struggle to recognize the image in the mirror at work, or you feel like you’re playing a fictional role when you enter the office, you are only stifling your productivity and increasing your stress. Adopt the following strategies to help you feel more engaged by embracing and showcasing your truth.

1. Overcome your derailers.

Think about the things that have threatened to hold you back. Kerry Goyette, president of Aperio Consulting Group, calls them her “derailers.” As Goyette explains, “our derailers cause us to act in ways that push away those who might be huge supporters or helpers.” They are those feelings of self-doubt that keep us from achieving or, in some cases, even trying.

The way to eliminate your derailers is to face them head on. Write down all the negative comments you say to yourself or the self-sabotaging actions you continually take. Then, create new, transparent, ambitious goals that fly in the face of those derailers. If you have always felt you weren’t good enough for the C-suite, for example, strive for promotions and seek out leadership opportunities. Pushing your derailers aside helps you feel less scared to reach for and talk about your dreams.

2. Embrace a hobby or two.

Feel as if you’ve become a one-trick pony? Pursuing a hobby can change your perspective and enhance your ability to innovate at work. Research published in the Journal of Occupational and Organizational Psychology unearthed a correlation between having hobbies and being more creative. Plus, nurturing a hobby has been known to relieve stress levels, according to a study in the Journal of Leisure Research.

When you have a hobby, you can turn off both the derailers and the stressors that come with working. Focusing on something enjoyable outside of your career gives your brain and emotions a much-needed break. You’ll bring fresh perspectives when you return to your desk, enabling you to tackle your workload with renewed energy.

3. Lean into your vulnerabilities.

It’s easy to heed our flight tendencies. Why would we want to dive headfirst into a situation that makes our palms sweaty or our heart race? Yet Brené Brown, University of Houston research professor, asserts that moments of intense vulnerability should be embraced, not dodged. “Vulnerability is the birthplace of innovation, creativity and change,” Brown says. “If you’ve created a work culture where vulnerability isn’t okay, you’ve also created a culture where innovation and creativity aren’t okay.”

It can be tough to constantly put yourself into experiences that feel uncomfortable, but they’re the moments that allow for complete authenticity. It should be fine to tell members of your team that you’re not certain of answers or that you need their help to bring a project to completion. Showing vulnerability and being true to the moment offers you a chance to grow and shine. Nothing is as freeing as being 100% authentic, and that’s what having the courage to be vulnerable is all about.

If you’ve been burdened by burnout and laden with imposter syndrome, take heart. It’s possible to bring the whole you to the job—what’s more, your work will be better for it. Over time, you can change not just the way you feel about yourself, but the way you react to stressors. And you can do it all without the need to play an imaginary role.

Feature Image Credit: Getty

By William Arruda

William Arruda is the cofounder of CareerBlast and creator of the complete LinkedIn quiz that helps you evaluate your LinkedIn profile and networking strategy.

Sourced from Forbes

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Are you using Instagram Stories to its fullest potential? Want to make your stories more consistent and engaging?

To explore how to use Instagram Stories for your brand, I interview Sue B. Zimmerman on the Social Media Marketing Podcast.

Sue is an Instagram marketing expert and author of The Instagram Strategy Guide. Her online course is called Ready, Set, Gram.

You’ll find tips and techniques for using Instagram Stories to help your business stand out, and discover how to use the new Create features that just dropped.

Click HERE for the remainder of the article.

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Sourced from Social Media Examiner

By Deanna Ting

TikTok is attracting a small but growing group of publishers eager to master the short-form video platform and its young and growing audience.

While most are still in an experimentation phase when it comes to using TikTok, they’re learning what works and what doesn’t on an app that doesn’t yet have publisher-friendly resources like discovery tabs or the sharing of ad revenues. For now, few have dedicated resources to the platform, choosing instead to treat it as an experimental channel.

Vice, which just started using TikTok earlier this year, is finding its TikTok followers crave exclusive content. Next month, Vice is planning to launch a Munchies by Vice account on TikTok and Vice Chief Digital Officer Cory Haik said the account will feature exclusive content made specifically for TikTok by Vice’s own social innovation team, which produces content for a variety of platforms.

“We need to go in with a specific offering that feels native to what users of TikTok are producing themselves,”  said Haik. “We can’t do a derivative.”

Those daily short-form videos, she said, “will still feel like Munchies,” but will also “feel very TikTok. It will have a different voice.”

Vice is still figuring out exactly what kinds of videos to post on TikTok for Munchies, but some early ideas include celebrating food bloopers, using recipes many people know but often mess up on, but embracing those failures. Or developing viral skits that look more closely at trending food items or themes. One example is, if throwing cheese on walls is trending on TikTok, Munchies might create a video that looks at which cheeses stick and why.

“It sounds like a big investment but we’re just optimizing teams that already exist,” Haik said.

BuzzFeed, which also started using TikTok earlier this year with four different accounts, isn’t creating exclusive content for TikTok just yet, but said reception to its repurposed video content has been positive. One of BuzzFeed’s most popular posts is from Nifty, demonstrating a baking hack.

“One of our motivating factors to be on TikTok and create a Tasty account was that we were finding a lot of copycat Tasty accounts on TikTok,” Tabir Akhter, head of platform strategy at BuzzFeed said. “People want to see food content on TikTok. It’s not only videos from high school bathrooms. It’s more than that. It’s a huge and robust platform of lots of people with lots of different interests, and people are really responding to our huge viral food videos on Tasty.”

She said her team has been very strategic and thoughtful about how they adapt existing video content for TikTok so that “it doesn’t feel like an imposter. If feels very native.” BuzzFeed is also thinking about launching more original TikTok content next year.

Hearst Magazines, which has been using TikTok and its former iteration, Musical.ly, since May 2017, also repackages content from other platforms onto TikTok. It’s found certain types of content to be the most engaging: namely, anything featuring celebrities and “mesmerizing” content, said Sheel Shah, Hearst Magazines’ vp of strategic partnerships and consumer products. One of Seventeen’s most popular TikTok posts, he said, was one that showcased the art of bullet journaling.

Historically, publishers have been reticent to spend time and money on platforms where it’s not clear how they can generate revenues. Currently, there is no mechanism for creators or publishers to directly monetize on TikTok, such as with sharing ad revenue, but all three publishers are hopeful there will be one day.

The short-form nature of TikTok videos, however, makes it relatively cost-effective to produce new content as Vice intends to do, but it also forces creators to get creative, even when they’re adapting existing content.

“What makes TikTok unique is the hyper-speed at which content is created and consumed,” said Akhter. “We do enjoy the really short-form nature of the videos … it forces your creativity in the adaptation process.”

Shah, however, said he wonders if TikTok will eventually allow longer-form video content like Musical.ly did before, since he finds the 15-second video format somewhat limiting.

All three publishers said they view TikTok primarily as a long-term way to engage and grow their respective audiences, similar to how they did with Snapchat a few years before.

“For TikTok, right now it’s more about long-term audience development and one day, maybe, monetization,” Shah said. “We want to figure out what this audience is interested in and how we can extend this understanding onto other platforms where we do have ROI.”

The strategies for the two platforms, however, are different, although both TikTok and Snapchat appeal to younger user bases and feature short-form video content.

“Snapchat, for us, is very curated and magazine-like,” said Akhter. “But on TikTok, there’s a huge potential for binge behavior and to go into a rabbit hole of our content, and we’re eager to serve them with that.”

At Vice, Haik said they’ve gotten their Snapchat strategy “down to a science” and they are not aiming to do original content on that platform, or on Instagram in the same way they’re doing it on TikTok.

One thing all can agree on about TikTok, however, is that its user base skews young. A leaked ad pitch deck fromJune 2019 said the majority of TikTok users (69%) are from Generation Z (ages 16 to 24), while 25% are age 25 and older. Most users are also female (60%). In the U.S., TikTok has more than 30 million monthly active users who spend, on average, 46 minutes on the app, per user, per day. Globally, the number of monthly active users is 800 million, with 500 million based in China.

Snapchat, by comparison, has 210 million daily active users worldwide, and eMarketer estimates it reached 297.7 monthly active users this year.

“We still believe, more so than ever, there’s a huge audience on the platform,” Shah said. “It’s a great opportunity for us to engage and interact with that new generation of consumers. We want to make sure that we’re there. We can get feedback on what this audience likes and doesn’t like, which informs our broader content strategy.”

While all three publishers have participated in trending hashtags on TikTok, none we spoke to have purchased a hashtag challenge or branded lens.

Looking ahead, Vice’s Haik said she wonders if TikTok will enable creators to have direct-sell ads and she thinks it could also be a platform where Vice could place branded content. She, along with Shah and Akhter, also said they wonder if and how TikTok will eventually make it easier for users to discover creators, their media titles included.

By Deanna Ting

Sourced from DIGIDAY

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Smartphone traffic now accounts for the majority of visits to retailers, but mobile conversion rates lag behind desktop. We take a look at the reasons for this.

A decade on from the release of the first iPhone, mobile shopping is massive. Much of this is thanks to Apple, and the many smartphones which followed, but there are still obstacles for retailers to overcome.

According to stats from Monetate, smartphone traffic worldwide to retailers is at 56.2%, and 34.5% for desktop.

However, this mobile traffic is converting at less than half the rate of that on desktop, at 2.25% compared to 4.81% for desktop. Even tablet fares better, converting at 4.06% on average.

We’ve seen the same pattern in our own stats. Around half of all visits to retailer’s sites come from mobile, but just 36% of purchases take place on mobile.

It seems that people are happy to browse on mobile, but many still prefer to buy on desktop, so let’s look at the reasons why.

There are several reasons why people prefer to buy on a laptop or PC. For one, it can be easier to navigate around the site and view images on a bigger screen, so some shoppers may browse on mobile and select products later on.

People are also more likely to buy on desktop when purchases are more complex. Travel purchases are generally more expensive and complicated – only 18% complete bookings on mobile.

Much of the issue comes down to checkout. Indeed, the add to cart rates shown above suggest this. While mobile conversion rates are less than half that of desktop, add to cart rates aren’t so far behind.

Even in sectors where shoppers are more likely to use mobile, such as fashion, mobile conversion rates still lag behind desktop.

Fashion sites attract a greater proportion of sales on mobile. In fact, this is the only sector to attract the majority of its sales from mobile shoppers (51.39%).

However, data from our recent Fashion Ecommerce Trends Report finds that fashion conversion rates are almost twice as high on desktop when compared to mobile.

Mobile usability on fashion sites has improved greatly, but some customers are still reluctant to convert via mobile devices.

The average mobile add to cart rate is 10.4%, compared to 12.9% for desktop. This implies that people are adding items to their cart at similar rates, but many more are bailing out during checkout.

The biggest issue behind lower mobile conversions is the checkout. So how can checkout be made easier? Here are three ways to do this…

People hate registering before they begin a purchase, and it seems like hard work for mobile shoppers, so providing a guest checkout option is one way to improve conversion rates.

It’s a barrier for customers, and one that isn’t necessary, as they can complete registration after purchase anyway. Streamlining forms makes checkout easier and faster, reducing hassle for shoppers, and removing sources of friction where people might abandon checkout.

Sites can allow users to autofill address and payment details saved on their phone’s browser, or postcode lookup tools to reduce the number of steps customers need to take.

Small details matter, such as defaulting to the most appropriate smartphone keyboard, like the numeric version for entering payment card details. It’s about making it easier for customers through marginal improvements.

Payment methods matter too, and providing alternatives can make it easier for mobile shoppers. Card details take time to enter, but PayPal and digital wallet options like Apple Pay can make payment fast and smooth.

Mobile is a challenge for retailers, but now that customers have shown they’re willing to browse and buy on mobile, it’s all about making the payment process smooth and easy for shoppers.

Feature Image Credit: Photo by William Iven on Unsplash.

By

Graham Charlton is editor in chief at SaleCycle

Sourced from The Drum