Referred Link - https://www.inc.com/marcel-schwantes/6-ways-to-be-awesome-even-when-you-think-youre-just-average.html

If you've ever felt like you're just the average Joe or Jane, I have news for you. Anybody can be awesome.

Being awesome doesn't mean having off-the-chain leadership skills at work or killing it with your uncanny negotiation prowess.

Smart, talented, pretty, tall, charismatic, funny. If you know people like that, they're no more awesome than anyone else just because they have desired attributes.

Truth is, you can be awesome by choice the moment you step out of bed every morning. It's part of having a growth mindset, as espoused by psychologist Carol Dweck.

That's reaching a point where you don't mind or fear failure as much because you realize you can improve your work, relationships, leadership or performance simply by learning and picking up new habits. And learning comes from failure (more on that below).

6 unconventional ways to be an awesome person

If you want to learn a few new tricks that will elevate your average status to that of "awesome," try learning and adapting the behaviors of the most successful people on the planet.

1. Be awesome by showing resilience.

Things happen and we all experience setbacks and disappointments. That's why having a coping mechanism when facing hurdles or getting crushed is key to bouncing back.

Rather than avoiding our emotions, judging our thoughts, or rehashing the past, University of Nevada, Reno, psychologist Steven Hayes says the right way to cope is to accept your thoughts and feelings and view them with curiosity.

At the same time, says Hayes, think consciously about what you really care about in life, and how you want to be in the world. Then, organize your behavior around those values you've identified as near and dear to you.

2. Be awesome by failing forward.

Nobody likes to fail. Yet failure is the secret to success. If you haven't been rejected a number of times, the current mantra goes, you just haven't experienced success.

Sir Richard Branson, founder of Virgin Group, swears by this premise. At Virgin, they encourage and even celebrate failure. There's an underlying theme there that, without trying something new and failing, it's virtually impossible to innovate and grow.

The billionaire Branson says, "Do not be embarrassed by your failures. Learn from them and start again. Making mistakes and experiencing setbacks is part of the DNA of every successful entrepreneur, and I am no exception."

Wherever you are on your career path, it's time to acknowledge that failing is common, no matter how hard you try to avoid it. And that's awesome.

3. Be awesome by banning the small talk.

Ever walk into a networking event or cocktail party and all you hear is superficial chit-chat? The small talk is deafening and doesn't evolve into anything substantial. You can hardly stand not to elicit an eye-roll in between sips of your Mojito.

Questions like what do you do? and where do you live? are predictable and exhausting; commentary about the weather or last night's game fill up awkward moments as people size each other up to determine -- is this is someone I want to talk to?

As it turns out, the types of conversations you're engaging in truly matter for your personal wellbeing. In 2010, scientists from the University of Arizona and Washington University in St. Louis investigated whether happy and unhappy people differ in the types of conversations they have.

As published in Psychological Science, the happiest participants had twice as many genuine conversations and one third as much small talk as the unhappiest participants.

These findings suggest that the happy life is social and conversationally deep rather than isolated and superficial. The research has also confirmed what most people know but don't practice: surface level small talk does not build relationships

4. Be awesome by surrounding yourself with awesome people.

At a 2004 Berkshire Hathaway annual meeting, billionaire Warren Buffett told a 14-year-old from California one of the secrets to his success: "It's better to hang out with people better than you. Pick out associates whose behavior is better than yours and you'll drift in that direction."

Buffett taught a common-sense life lesson for all of us about absorbing the very qualities and traits of awesome and successful people further down the path than us -- people who have demonstrated skills and traits that will make us better leaders, workers, parents, and human beings.

5. Be awesome by learning to play an instrument.

Not only will picking up guitar, piano, or drum lessons make you more awesome, it'll make you smarter too in the long run. There is growing evidence that musicians have structurally and functionally different brains compared with nonmusicians.

The research suggests "the areas of the brain used to process music are larger or more active in musicians. Even just starting to learn a musical instrument can change the neurophysiology of the brain. The brain regions involved in music processing are also required for other tasks, such as memory or language skills."

6. Be awesome by saying awesome things to people at work.

When communicating with another human being, it's the words we choose that matter. In the context of conversations, there are certain undeniable phrases that, if we use them more often, will result in others perceiving us in a way that will build bridges and increase trust. For example:

"I can't tell you how much [something performance-related] meant to all of us."Acknowledging others for doing a challenging task or for their specific work performance is critical for human motivation. We need to show fellow co-workers that we do pay attention and that their hard work is on our radar screen. So go ahead -- praise their work but make sure you're attaching the praise to a specific work performance to make it that much more impactful.
"Can I get your advice on this?" There's this false notion that people who ask for advice are perceived as less competent. To the contrary, research has linked people that ask for advice to being perceived as more competent. It demonstrates humility, a leadership strength that builds trust with others.
"I'm happy to see you!" Often used as a greeting phrase, when done with the proper and enthusiastic voice tone and body language it's packed with deeper meaning that positively elevates the other person (and makes you look and feel awesome). It communicates, "You matter, and I value your presence."

Referred Link - https://medium.com/activewizards-machine-learning-company/top-9-data-science-use-cases-in-banking-6bb071f9470c

Using data science in the banking industry is more than a trend, it has become a necessity to keep up with the competition. Banks have to realize that big data technologies can help them focus their resources efficiently, make smarter decisions, and improve performance.

Here is a list of data science use cases in banking area which we have combined to give you an idea how can you work with your significant amounts of data and how to use it effectively.

Fraud detection

Machine learning is crucial for effective detection and prevention of fraud involving credit cards, accounting, insurance, and more. Proactive fraud detection in banking is essential for providingsecurity to customers and employees. The sooner a bank detects fraud, the faster it can restrict account activity to minimize loses. By implementing a series of fraud detection schemes banks can achieve necessary protection and avoid significant loses.

The key steps to fraud detection include:

Obtaining data samplings for model estimation and preliminary testing
Model estimation
Testing stage and deployment.

Since every data set is different, each requires individual training and fine-tuning by data scientists. Transforming the deep theoretical knowledge into practical applications demands expertise in data-mining techniques, such as association, clustering, forecasting, and classification.

An example of efficient fraud detection is when some unusually high transactions occur and the bank’s fraud prevention system is set up to put them on hold until the account holder confirms the deal. For new accounts, fraud detection algorithms can investigate unusually high purchases of popular items, or multiple accounts opened in a short period with similar data.

Managing customer data

Banks are obliged to collect, analyze, and store massive amounts of data. But rather than viewing this as just a compliance exercise, machine learning and data science tools can transform this into a possibility to learn more about their clients to drive new revenue opportunities.

Nowadays, digital banking is becoming more popular and widely used. This creates terabytes of customer data, thus the first step of data scientists team is to isolate truly relevant data. After that, being armed with information about customer behaviors, interactions, and preferences, data specialists with the help of accurate machine learning models can unlock new revenue opportunities for banks by isolating and processing only this most relevant clients’ information to improve business decision-making.

Risk modeling for investment banks

Risk modeling is a high priority for investment banks, as it helps to regulate financial activities and plays the most important role when pricing financial instruments. Investment banking evaluates the worth of companies to create capital in corporate financing, facilitate mergers and acquisitions, conduct corporate restructuring or reorganizations, and for investment purposes.

That’s why risk modeling appears extremely substantial for banks and is best assessed with more information in hand and data science tools in reserve. Now, through the power of Big Data, innovators in the industry are leveraging new technology for effective risk modeling and therefore better data-driven decisions.

Personalized marketing

The key to success in marketing is to make a customized offer that suits the particular client’s needs and preferences. Data analytics enables us to create personalized marketing that offers the right product to the right person at the right time on the right device. Data mining is widely used for target selection to identify the potential customers for a new product.

Data scientists utilize the behavioral, demographic, and historical purchase data to build a model that predicts the probability of a customer’s response to a promotion or an offer. Therefore, banks can make an efficient, personalized outreach and improve their relationships with customers.

Lifetime value prediction

Customer lifetime value (CLV) is a prediction of all the value a business will derive from their entire relationship with a customer. The importance of this measure is growing fast, as it helps to create and sustain beneficial relationships with selected customers, therefore generating higher profitability and business growth.

Acquiring and retaining profitable customers is an ever-growing challenge for banks. As the competition is getting stronger, banks now need a 360-degree view of each customer to focus their resources efficiently. This is where the data science comes in. First, a large amount of data must be taken into account: such as notions of client’s acquisition and attrition, use of diverse banking products and services, their volume and profitability, as well as other client’s characteristics like geographical, demographic, and market data.

This data often needs a lot of cleaning and manipulation to become usable and meaningful. The profiles, products, or services of the bank’s clients vary greatly, and so do their behaviors and expectations. There are many tools and approaches in the data scientists’ arsenal to develop a CLV model such as Generalized linear models (GLM), Stepwise regression, Classification, and regression trees (CART). Building a predictive model to determine the future marketing strategies based on CLV is an invaluable process for maintaining good customer relations during each customer’s lifetime with the company that results in higher proﬁtability and growth.

Real-time and predictive analytics

The growing importance of analytics in banking cannot be underestimated. Machine learning algorithms and data science techniques can significantly improve bank’s analytics strategy since every use case in banking is closely interrelated with analytics. As the availability and variety of information are rapidly increasing, analytics are becoming more sophisticated and accurate.

The potential value of available information is astonishing: the amount of meaningful data indicating actual signals, not just noise, has grown exponentially in the past few years, while the cost and size of data processors have been decreasing. Distinguishing truly relevant data from noise contributes to effective problem solving and smarter strategic decisions. Real-time analytics help to understand the problem that holds back the business, while predictive analytics aid in selecting the right technique to solve it. Significantly better results can be achieved by integrating analytics into the bank workflow to avoid potential problems in advance.

Customer segmentation

Customer segmentation means singling out the groups of customers based on either their behavior (for behavioral segmentation) or specific characteristics (e.g. region, age, income for demographic segmentation). There is a whole bunch of techniques in data scientists’ arsenal such as clustering, decision trees, logistic regression, etc. and, as a result, they help to learn the CLV of every customer segment and discover high-value and low-value segments.

There is no need to prove that such segmentation of clients allows for the effective allocation of marketing resources and the maximization of the point-based approach to each client group as well as selling opportunities. Do not forget that customer segmentation is designed to improve customer service and help in loyalty and retention of customers, which is so necessary for the banking sector.

Recommendation engines

Data science and machine learning tools can create simple algorithms, which analyze and filter user’s activity in order to suggest him the most relevant and accurate items. Such recommendation engines show the items that might interest the user, even before he searched for it himself. To build a recommendation engine, data specialists analyze and process a lot of information, identify customer profiles, and capture data showing their interactions to avoid repeating offers.

The type of recommendation engines depends on the filtering method of the algorithm. Collaborative filtering methods can be either user-based, or item-based, and work with user behavior to analyze other users’ preferences, then make recommendations to the new user.

The main challenge in collaborative filtering approach is using a huge amount of data that causes computation problems and increased price. Content-based filtering works with more simple algorithms, which recommend similar items to the ones the user engages with referring to a previous activity. These methods can fail in case of complex behaviors or unclear connections. There is also a hybrid type of engines, combining collaborative and content-based filtering.

No method is universal, each of them has some pros and cons, and the right choice depends on your goals and circumstances.

Customer support

Outstanding customer support service is the key to keep a productive long-term relationship with your customers. As a part of customer service, customer support is an important but broad concept in the banking industry. In essence, all banks are service-based businesses, so most of their activities involve elements of service. It includes responding to customers’ questions and complaints in a thorough and timely manner and interacting with customers.

Data science makes this process better automated, more accurate, personal, direct, and productive, and less costly concerning employee time.

Conclusion

To gain competitive advantage, banks must acknowledge the crucial importance of data science, integrate it in their decision-making process, and develop strategies based on the actionable insights from their client’s data. Start with small manageable steps to incorporate Big Data analytics into your operating models, and be ahead of the competition.

This list of use cases can be expanded every day thanks to such a rapidly developing data science field and the ability to apply machine learning models to real data, gaining more and more accurate results. We will be grateful for your comments and your vision of additional possible options for using data science in banking.

Referred Link - https://www.datasciencecentral.com/profiles/blogs/big-data-explained-in-less-than-2-minutes-to-absolutely-anyone

There are some things that are so big that they have implications for everyone, whether we want them to or not. Big Data is one of those concepts, and is completely transforming the way we do business and is impacting most other parts of our lives.

It’s such an important idea that everyone from your grandma to your CEO needs to have a basic understanding of what it is and why it’s important.

Source for cartoon: click here

What is Big Data?

“Big Data” means different things to different people and there isn’t, and probably never will be, a commonly agreed upon definition out there. But the phenomenon is real and it is producing benefits in so many different areas, so it makes sense for all of us to have a working understanding of the concept.

So here’s my quick and dirty definition:

The basic idea behind the phrase 'Big Data' is that everything we do is increasingly leaving a digital trace (or data), which we (and others) can use and analyse. Big Data therefore refers to that data being collected and our ability to make use of it.

I don’t love the term “big data” for a lot of reasons, but it seems we’re stuck with it. It’s basically a ‘stupid’ term for a very real phenomenon – the datafication of our world and our increasing ability to analyze data in a way that was never possible before.

Of course, data collection itself isn’t new. We as humans have been collecting and storing data since as far back as 18,000 BCE. What’s new are the recent technological advances in chip and sensor technology, the Internet, cloud computing, and our ability to store and analyze data that have changed the quantityof data we can collect.

Things that have been a part of everyday life for decades — shopping, listening to music, taking pictures, talking on the phone — now happen more and more wholly or in part in the digital realm, and therefore leave a trail of data.

The other big change is in the kind of data we can analyze. It used to be that data fit neatly into tables and spreadsheets, things like sales figures and wholesale prices and the number of customers that came through the door.

Now data analysts can also look at “unstructured” data like photos, tweets, emails, voice recordings and sensor data to find patterns.

How is it being used?

As with any leap forward in innovation, the tool can be used for good or nefarious purposes. Some people are concerned about privacy, as more and more details of our lives are being recorded and analyzed by businesses, agencies, and governments every day. Those concerns are real and not to be taken lightly, and I believe that best practices, rules, and regulations will evolve alongside the technology to protect individuals.

But the benefits of big data are very real, and truly remarkable.

Most people have some idea that companies are using big data to better understand and target customers. Using big data, retailers can predict what products will sell, telecom companies can predict if and when a customer might switch carriers, and car insurance companies understand how well their customers actually drive.

It’s also used to optimize business processes. Retailers are able to optimize their stock levels based on what’s trending on social media, what people are searching for on the web, or even weather forecasts. Supply chains can be optimized so that delivery drivers use less gas and reach customers faster.

But big data goes way beyond shopping and consumerism. Big data analytics enable us to find new cures and better understand and predict the spread of diseases. Police forces use big data tools to catch criminals and even predict criminal activity and credit card companies use big data analytics it to detect fraudulent transactions. A number of cities are even using big data analytics with the aim of turning themselves into Smart Cities, where a bus would know to wait for a delayed train and where traffic signals predict traffic volumes and operate to minimize jams.

Why is it so important?

The biggest reason big data is important to everyone is that it’s a trend that’s only going to grow.

As the tools to collect and analyze the data become less and less expensive and more and more accessible, we will develop more and more uses for it — everything from smart yoga mats to better healthcare tools and a more effective police force.

And, if you live in the modern world, it’s not something you can escape. Whether you’re all for the benefits big data can bring, or worried about Big Brother, it’s important to be aware of the phenomena and tuned in to how it’s affecting your daily life.

What are your biggest questions about big data? I’d love to hear them in the comments below — and they may inspire future posts to address them.

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Referred Link - https://medium.com/amp/p/791c3f18f5e6

“In my whole life, I have known no wise people (over a broad subject matter area) who didn’t read all the time — none. Zero.” — Charlie Munger, Self-made billionaire & Warren Buffett’s longtime business partner

Why did the busiest person in the world, former president Barack Obama, read an hour a day while in office?

Why has the best investor in history, Warren Buffett, invested 80% of his time in reading and thinking throughout his career?

Why has the world’s richest person, Bill Gates, read a book a week during his career? And why has he taken a yearly two-week reading vacation throughout his entire career?

Why do the world’s smartest and busiest people find one hour a day for deliberate learning (the 5-hour rule), while others make excuses about how busy they are?

What do they see that others don’t?

The answer is simple: Learning is the single best investment of our time that we can make. Or as Benjamin Franklin said, “An investment in knowledge pays the best interest.”

This insight is fundamental to succeeding in our knowledge economy, yet few people realize it. Luckily, once you do understand the value of knowledge, it’s simple to get more of it. Just dedicate yourself to constant learning.

Knowledge is the new money

“Intellectual capital will always trump financial capital.” — Paul Tudor Jones, self-made billionaire entrepreneur, investor, and philanthropist

We spend our lives collecting, spending, lusting after, and worrying about money — in fact, when we say we “don’t have time” to learn something new, it’s usually because we are feverishly devoting our time to earning money, but something is happening right now that’s changing the relationship between money and knowledge.

We are at the beginning of a period of what renowned futurist Peter Diamandis calls rapid demonetization, in which technology is rendering previously expensive products or services much cheaper — or even free.

This chart from Diamandis’ book Abundance shows how we’ve demonetized $900,000 worth of products and services you might have purchased between 1969 and 1989.

This demonetization will accelerate in the future. Automated vehicle fleets will eliminate one of our biggest purchases: a car. Virtual reality will make expensive experiences, such as going to a concert or playing golf, instantly available at much lower cost. While the difference between reality and virtual reality is almost incomparable at the moment, the rate of improvement of VR is exponential.

While education and health care costs have risen, innovation in these fields will likely lead to eventual demonetization as well. Many higher educational institutions, for example, have legacy costs to support multiple layers of hierarchy and to upkeep their campuses. Newer institutions are finding ways to dramatically lower costs by offering their services exclusively online, focusing only on training for in-demand, high-paying skills, or having employers who recruit studentssubsidize the cost of tuition.

Finally, new devices and technologies, such as CRISPR, the XPrize Tricorder, better diagnostics via artificial intelligence, and reduced cost of genomic sequencing will revolutionize the healthcare system. These technologies and other ones like themwill dramatically lower the average cost of healthcare by focusing on prevention rather than cure and management.

While goods and services are becoming demonetized, knowledge is becoming increasingly valuable.

Perhaps the best example of the rising value of certain forms of knowledge is the self-driving car industry. Sebastian Thrun, founder of Google X and Google’s self-driving car team, gives the example of Uber paying $700 million for Otto, a six-month-old company with 70 employees, and of GM spending $1 billion on their acquisition of Cruise. He concludes that in this industry, “The going rate for talent these days is $10 million.”

That’s $10 million per skilled worker, and while that’s the most stunning example, it’s not just true for incredibly rare and lucrative technical skills. People who identify skills needed for future jobs — e.g., data analyst, product designer, physical therapist — and quickly learn them are poised to win.

Those who work really hard throughout their career but don’t take time out of their schedule to constantly learn will be the new “at-risk” group. They risk remaining stuck on the bottom rung of global competition, and they risk losing their jobs to automation, just as blue-collar workers did between 2000 and 2010 when robots replaced 85 percent of manufacturing jobs.

Why?

People at the bottom of the economic ladder are being squeezed more and compensated less, while those at the top have more opportunities and are paid more than ever before. The irony is that the problem isn’t a lack of jobs. Rather, it’s a lack of people with the right skills and knowledge to fill the jobs.

An Atlantic article captures the paradox: “Employers across industries and regions have complained for years about a lack of skilled workers, and their complaints are borne out in U.S. employment data. In July [2015], the number of job postings reached its highest level ever, at 5.8 million, and the unemployment rate was comfortably below the post-World War II average. But, at the same time, over 17 million Americans are either unemployed, not working but interested in finding work, or doing part-time work but aspiring to full-time work.”

In short, we can see how at a fundamental level knowledge is gradually becoming its own important and unique form of currency. In other words, knowledge is the new money. Similar to money, knowledge often serves as a medium of exchange and store of value.

But, unlike money, when you use knowledge or give it away, you don’t lose it. Transferring knowledge anywhere in the world is free and instant. Its value compounds over time faster than money. It can be converted into many things, including things that money can’t buy, such as authentic relationships and high levels of subjective well-being. It helps you accomplish your goals faster and better. It’s fun to acquire. It makes your brain work better. It expands your vocabulary, making you a better communicator. It helps you think bigger and beyond your circumstances. It puts your life in perspective by essentially helping you live many lives in one life through other people’s experiences and wisdom.

Former President Obama perfectly explains why he was so committed to reading during his Presidency in a recent New York Times interview: “At a time when events move so quickly and so much information is transmitted,” he said, reading gave him the ability to occasionally “slow down and get perspective” and “the ability to get in somebody else’s shoes.” These two things, he added, “have been invaluable to me. Whether they’ve made me a better president I can’t say. But what I can say is that they have allowed me to sort of maintain my balance during the course of eight years, because this is a place that comes at you hard and fast and doesn’t let up.”

6 essentials skills to master the new knowledge economy

“The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” — Alvin Toffler

So, how do we learn the right knowledge and have it pay off for us? The six points below serve as a framework to help you begin to answer this question. I also created an in-depth webinar on Learning How To Learn that you can watch for free.

Identify valuable knowledge at the right time. The value of knowledge isn’t static. It changes as a function of how valuable other people consider it and how rare it is. As new technologies mature and reshape industries, there is often a deficit of people with the needed skills, which creates the potential for high compensation. Because of the high compensation, more people are quickly trained, and the average compensation decreases.
Learn and master that knowledge quickly. Opportunity windows are temporary in nature. Individuals must take advantage of them when they see them. This means being able to learn new skills quickly. After reading thousands of books, I’ve found that understanding and using mental models is one of the most universal skills that EVERYONE should learn. It provides a strong foundation of knowledge that applies across every field. So when you jump into a new field, you have preexisting knowledge you can use to learn faster.
Communicate the value of your skills to others. People with the same skills can command wildly different salaries and fees based on how well they’re able to communicate and persuade others. This ability convinces others that the skills you have are valuable is a “multiplier skill.” Many people spend years mastering an underlying technical skill and virtually no time mastering this multiplier skill.
Convert knowledge into money and results. There are many ways to transform knowledge into value in your life. A few examples include finding and getting a job that pays well, getting a raise, building a successful business, selling your knowledge as a consultant, and building your reputation by becoming a thought leader.
Learn how to financially invest in learning to get the highest return. Each of us needs to find the right “portfolio” of books, online courses, and certificate/degree programs to help us achieve our goals within our budget. To get the right portfolio, we need to apply financial terms — such as return on investment, risk management, hurdle rate, hedging, and diversification — to our thinking on knowledge investment.
Master the skill of learning how to learn. Doing so exponentially increases the value of every hour we devote to learning (our learning rate). Our learning rate determines how quickly our knowledge compounds over time. Consider someone who reads and retains one book a week versus someone who takes 10 days to read a book. Over the course of a year, a 30% difference compounds to one person reading 85 more books.

To shift our focus from being overly obsessed with money to a more savvy and realistic quest for knowledge, we need to stop thinking that we only acquire knowledge from 5 to 22 years old, and that then we can get a job and mentally coast through the rest of our lives if we work hard. To survive and thrive in this new era, we must constantly learn.

Working hard is the industrial era approach to getting ahead. Learning hard is the knowledge economy equivalent.

Just as we have minimum recommended dosages of vitamins, steps per day, and minutes of aerobic exercise for maintaining physical health, we need to be rigorous about the minimum dose of deliberate learning that will maintain our economic health. The long-term effects of intellectual complacency are just as insidious as the long-term effects of not exercising, eating well, or sleeping enough. Not learning at least 5 hours per week (the 5-hour rule) is the smoking of the 21st century and this article is the warning label.

Don’t be lazy. Don’t make excuses. Just get it done.

“Live as if you were to die tomorrow. Learn as if you were to live forever.” — Mahatma Gandhi

Before his daughter was born, successful entrepreneur Ben Clarke focused on deliberate learning every day from 6:45 a.m. to 8:30 a.m. for five years (2,000+ hours), but when his daughter was born, he decided to replace his learning time with daddy-daughter time. This is the point at which most people would give up on their learning ritual.

Instead of doing that, Ben decided to change his daily work schedule. He shortened the number of hours he worked on his to do list in order to make room for his learning ritual. Keep in mind that Ben oversees 200+ employees at his company, The Shipyard, and is always busy. In his words, “By working less and learning more, I might seem to get less done in a day, but I get dramatically more done in my year and in my career.” This wasn’t an easy decision by any means, but it reflects the type of difficult decisions that we all need to start making. Even if you’re just an entry-level employee, there’s no excuse. You can find mini learning periods during your downtimes (commutes, lunch breaks, slow times). Even 15 minutes per day will add up to nearly 100 hours over a year. Time and energy should not be excuses. Rather, they are difficult, but overcomable challenges. By being one of the few people who rises to this challenge, you reap that much more in reward.

We often believe we can’t afford the time it takes, but the opposite is true: None of us can afford not to learn.

Learning is no longer a luxury; it’s a necessity.

Start your learning ritual today with these three steps

The busiest, most successful people in the world find at least an hour to learn EVERY DAY. So can you!

Just three steps are needed to create your own learning ritual:

Find the time for reading and learning even if you are really busy and overwhelmed.
Stay consistent on using that “found” time without procrastinating or falling prey to distraction.
Increase the results you receive from each hour of learning by using proven hacks that help you remember and apply what you learn.

Over the last three years, I’ve researched how top performers find the time, stay consistent, and get more results. There was too much information for one article, so I spent dozens of hours and created a free masterclass to help you master your learning ritual too!