Just how valuable can big data be for a business? Some analysts believe that simply increasing data accessibility by 10 percent can help the average Fortune 1000 company generate an additional $65 million in income.
Quality data can be even more valuable for new startups. When you have a limited marketing budget, you can’t afford to let your customer acquisition efforts go to waste — especially when in many industries, companies spend hundreds of dollars to acquire a single customer.
Unlocking the potential of data science and analytics will enable you to gain greater insights into your customers, allowing you to spend your marketing budget more efficiently. Here are some of the top reasons why data science should play a central role in your acquisition strategy.
Identifying signals of intent and creating predictive models
When it comes to making the most of your marketing budget, few insights are more valuable than discovering why a customer wants to buy a particular product or service. This intent is usually signaled through a wide range of resources, including Google searches, visiting shopping comparison sites or reading product reviews on your own website.
Big data helps identify when a particular user is engaging in these activities, indicating that they are more likely to become a paying customer with the right marketing push. Targeting the right person at the right time is usually a recipe for sales success. Over time, as data science determines the strongest signals of intent, your team will also be able to create predictive models that will allow you to consistently target those who are most likely to convert.
By focusing your customer acquisition efforts on the customers that are demonstrating signals of intent, you’ll be able to stretch your advertising dollars further and get a much greater return on investment. Analytics can even be used to predict future needs, allowing you to nudge current customers at the right time to encourage additional purchases.
Testing marketing strategies
Data doesn’t just help you identify those who are most likely to make a purchase; it can also help you fine-tune the strategies you use to guide potential customers through the buyer’s journey. A/B testing can be used to determine the effectiveness of everything involved in customer acquisition. From comparing email campaign copy to changing the location of your call-to-action button, these tests will enable your team to find ways to keep customers engaged until they make a purchase.
As an example of this, Google Analytics allows businesses to break down their e-commerce products based on product list performance. This data goes well beyond revealing how well a certain item sold. It can also reveal which items receive a lot of views, but few clicks, or even which items are most likely to be abandoned in a consumer shopping cart.
Identifying underperforming product lists can greatly improve your customer acquisition efforts. Data will help your team find the reasons why a particular product isn’t performing well, or help you decide to discontinue an unappealing product. In some cases, even something as simple as changing the display order can provide a boost in sales — and A/B testing will reveal the answers.
Improved segmentation yields improved targeting
Not all customers are created equal — while some may become lifelong devotees to your brand, others may only make a single purchase. A customer may not generate a profit from an initial purchase, but if their lifetime value outweighs the cost of acquisition, your company will be better poised for long-term success. Once again, proper use of data analytics can help you identify the right customers to target as part of your acquisition strategy.
The Lyric Opera of Chicago “used machine learning algorithms to take into account hundreds of dimensions at once” to better fine-tune their audience segmentation strategy. Even without predictive modeling, these algorithms examined a wide swath of data that described top opera-goers. Combining this data with lookalike modeling improved their conversion rate by 3.7 times with a high-value group.
Segmentation data will allow you to identify those individuals who deliver the highest average customer lifetime value, ensuring that those you reach with your customer acquisition efforts will deliver significant profits in the long run.
The potential of big data
Put simply, better data enables better decision-making. Leveraging the power of big data will do much more than help your marketing team create more effective advertising campaigns. It can help you adapt your web content, fine-tune your SEO efforts and even help you discover new potential customer groups, all by providing invaluable insights that you wouldn’t be able to discover on your own.
For companies that truly wish to maximize their potential for customer acquisition, it is clear that big data is the key to a successful future.
How big data has changed politics
Data isn’t new, but we often forget that. In fact, even Sherlock Holmes recognized the power of data, as we can tell from one of his most famous quotes: “It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”
This is especially relevant to us in an age of fake news and in which politicians are arguably being held more accountable than ever thanks to our newfound ability to process and understand data. Politicians are finding that it’s more and more difficult to “twist facts to suit theories” when thousands of wannabe Holmeses are taking to Reddit to debunk them.
Data is so important these days that it’s overtaken oil as the world’s most valuable resource, which of course means it’s a hot topic amongst politicians. Governmental organisations are learning to understand and to deal with data at regional, national and international levels, not because they want to but because they have to. Data is just that important.
Big data and machine learning
To understand how data has changed politics, you need to first understand what big data is and how its complex interplay with machine learning is a game changer. Big data is essentially just data at a massive scale, while machine learning is a subset of artificial intelligence which relies on teaching computers to “think” like human beings so that they can solve abstract problems.
Netflix’s recommendations system is a great example of big data and machine learning in action. Their algorithms are able to process the huge amounts of viewing data that they store on each of their users and then to crunch the numbers and to make super relevant recommendations. The machine learning algorithm learns as it goes, which means that the more data it has access to, the better it gets.
At first glance, it might seem as though this doesn’t relate back to politics, but the same idea applies no matter what the data itself is actually about. So for example, imagine if the mayor’s office had access to real-time traffic data which could be analysed by machine learning algorithms to provide suggestions in real time about when to close roads or to re-route traffic. We’re talking about an algorithm that has the potential to save lives.
The power of data
Data is knowledge and knowledge is power, which is one of the reasons why data has changed the way we think about politics. You just have to look at the Cambridge Analytica scandal to see how much of a difference data can make, especially when it comes to elections. It’s not even anything new. After all, Obama’s 2012 reelection campaign was largely successful because of its smart use of big data.
Data – or more specifically, the interpretation of it – can make or break a political campaign. But while it’s true that it can help people to be elected into office, it can also help them to do their jobs much more effectively and efficiently. We’ve already talked about data being used to improve traffic flows and to make roads safer. Now imagine that the same concept could be rolled out in every single area that it’s a government’s job to oversee and facilitate.
For example, data and its analysis can be used by healthcare heads to determine where best to allocate funds. It can be used by foreign ministers to simulate complex trade agreements or to predict the long-term effects of uncertain political situations such as the UK’s decision to leave the European Union. It can be used to identify potential terrorist threats or to give advance warnings of disease outbreaks or other phenomena using population data.
Arguing the point
When it comes to debates, which politicians tend to be pretty good at, one of the most powerful assets to have is a set of data that supports the point that you’re trying to make. The only problem is that while data doesn’t lie, people do. People also disagree on what exactly the data means, and there are often multiple different potential conclusions that could be drawn. There’s often not any one right answer.
That’s assuming that politicians even have access to the data in the first place. After all, one of the biggest debates of our time is the debate over privacy and what data companies should be able to store about us. You only have to look at the incoming General Data Protection Regulation (GDPR) to see how times are changing.
Politicians find themselves in the interesting position of having to define these new rules and regulations whilst simultaneously working within their constraints. There’s also the risk that we’ll end up with people who don’t really know what they’re talking about drafting legislation that could cripple the future of the internet before it really has time to settle in as a medium. After all, the World Wide Web is less than thirty years old. When you compare it to some of our other inventions as a species, it’s just a baby. A baby made up of millions of terabytes of data.
Nasscom checks into Guiyang for analytics, big data projects
After striking a partnership with Chinese city of Dalian – a technology hub – last year, IT industry body Nasscomis going to strike a second partnership with the city of Guiyang on Monday to focus on collaboration in Big Data and Analytics.
As part of the partnership with the Guiyang Municipal government, agreements worth 25 million Yen between Chinese customers and Indian service providers are also going to be announced. The pilot projects, launched on the Sino Indian Digital Collaborative Opportunities Plaza (SIDCOP) platform, would be executed over the next year.
In the coming months, an IT Corridor within Guiyang HiTech city will be created to specifically promote Big Data and Analytics projects. The government of Guiyang is not only offering a host of policy benefits and incentives such as rentfree office space along with other tax concessions but will aid Indian service providers – who are members of Nasscom— in bagging government contracts.
Gagan Sabharwal, senior director, Global Trade Development, Nasscom, said the platform in Guiyang will promote co-create culture with the Chinese industry and advance mutual leadership in the global innovation ecosystem.
17 best data science bootcamps for boosting your career
Data scientist is the best job in America, according to a survey on Glassdoor, and it consistently makes the top of the list year after year. With a job score of 4.8 out of 5, a median base salary of $110,000 per year and over 4,500 current job openings, it’s a great time to be a data scientist.
But, as the role of data scientist grows in demand, traditional schools aren’t churning out qualified candidates fast enough to fill the open positions. There’s also no clear path for those who have been in the tech industry for years and want to take advantage of a lucrative job opportunity. Enter the bootcamp, a trend that has quickly grown in popularity to train workers for in-demand tech skills.
Here are 17 of the best data science bootcamps, designed to help you brush up on your data skills, with courses for anyone from beginners to experienced data scientists.
The 12 best established data science bootcamps
- Byte Academy
- The Data Incubator
- The Data Science Dojo
- General Assembly
- NYC Data Science Academy
Byte Academy offers full-time, part-time and remote programs in data science; and attendees have the option to defer payment until they secure a job after graduating. If you aren’t hired within six months, Byte Academy will refund your full tuition. The full-time course is five days per week for 14 weeks, while the part-time course takes place two evenings per week over 24 weeks. Byte Academy also offers corporate and onsite training for companies and custom corporate training with topics like blockchain and quant-algos.
|Cost||$14,950 for full- and part-time bootcamps, $5,500 for a la carte options. If you aren’t hired within six months after graduation, your tuition will be refunded|
|Locations||New York City, Bangalore and online|
DataCamp is entirely online and it’s aimed at professionals who are already working in technology, finance and healthcare. However, anyone interested in data science will benefit from DataCamp’s program. The courses not only teach you the necessary skills, you can also practice and apply those skills to real-world problems through hands-on projects. It’s free to try, but for full access you’ll need to pay a subscription fee.
|Cost||Free with limited access or $29 per month/$25 per year for an unlimited subscription|
The Data Incubator
The Data Incubator is an eight-week program aimed at more experienced tech workers with a masters or Ph.D.; fellowships are available for qualifying students. Qualified fellows “already have the 90 percent difficult-to-learn skills” and Data Incubator promises to equip them “with the last 10 percent.” The program also offers students mentorship directly from hiring companies, including LinkedIn, Microsoft and The New York Times, all while they work on building a portfolio to showcase their skills.
|Cost||Free for those accepted|
|Locations||Boston, Washington (D.C.) and online|
The Data Science Dojo
With campuses in Seattle, Silicon Valley, Barcelona, Toronto, Washington and Paris, the Data Science Dojo brings quick and affordable data science education to professionals around the world. It’s one of the shortest programs on this list, but in just five days, Data Science Dojo promises to train attendees on machine learning and predictive models as a service, and each student will complete a full IoT project and have the chance to enter a Kaggle competition.
Due to the short nature of the course, it’s tailored to those already in the industry who want to learn more about data science or brush up on the latest skills. However, it’s open to anyone at any skill level — if you’re ready to throw yourself in the trenches of data science.
|Locations||Seattle, Silicon Valley, Washington (D.C.), Paris, Chicago, Toronto, New York City, Barcelona, Amsterdam, Austin and Singapore|
Dataquest is the most highly rated data science boot camp on review site Switchup, earning near five-star reviews for overall experience, curriculum and job support. Dataquest relies on teaching students through “interactive coding challenges” instead of video lectures, so you will have the chance to code and work with data, while receiving feedback as you go. You can opt for the Data Scientist, Data Engineer or Data Analyst path — each promise to prepare you to jump right into a career in data.
|Cost||Free with limited access, $29 per month for a basic account and $49 per year for a premium account|
Galvanize offers a 13-week, full-time data science course that focuses on everything you need to know to become an effective data scientist. You’ll learn the fundamentals of data science, including python, statistics and machine learning through real-world case studies. The course ends with a capstone project and you’ll be more than prepared to enter the job market. The application process includes a technical exercise to gauge your current level and to see whether you have potential to succeed in the field.
|Cost||$16,000 for the 13-week course|
|Locations||Denver, San Francisco, Boulder, Seattle, Austin, Phoenix and New York City|
General Assembly offers full-time and part-time courses, workshops and events in person and online. The full course catalog is extensive, and there is a program for every data science skill you can imagine. Courses range from one-week accelerated courses to full-time immersive 10- to 13-month programs, but it’s easy to find something to fit your schedule and budget. Whether you’re a recent graduate, looking to make a job switch or you’re an experienced data scientist trying to expand your skillset, General Assembly will have a program for you.
|Cost||Payment varies depending on the program you choose, but financing options are available|
|Locations||Boston, London, Los Angeles, New York City, San Francisco, Sydney, Washington (D.C.) and online|
Offered through Northeastern University, Level is a two-month program that wants to turn you into a hirable data analyst. Each day of the course focuses on a different real-world problem that a business might face, and students develop projects to solve these issues. Students can expect to learn more about SQL, R, Excel, Tableau and PowerPoint and walk away with experience in preparing data, regression analysis, business intelligence, visualization and storytelling. You can choose between a full-time eight-week course that meets five days a week, eight hours a day and a hybrid 20-week program that meets online and in-person one night a week.
|Cost||Varies by programming, location and the schedule you choose|
|Locations||Boston, Charlotte, Seattle, San Jose, Toronto and online|
Metis has campuses in New York and San Francisco, where students can attend intensive in-person data science workshops. Programs take 12 weeks to complete and include on-site instruction, career coaching and job placement support to help students make the best of their newly acquired skills. Like other boot camps, Metis’ programs are project-based and focus on real-world skills that graduates can take with them to a career in data science. Those who complete the program can expect to walk away with in-depth knowledge of modern big data tools, access to an extensive network of professionals in the industry and ongoing career support.
|Cost||Cost varies depending on the program, starting at $2,350 for in-person professional development and $1,900 for Live Online professional development courses. Scholarships available|
|Locations||Chicago, New York City, Seattle, San Francisco and online|
NYC Data Science Academy
The NYC Data Science Academy is aimed at more experienced data scientists who have earned a masters or Ph.D. degree. Courses include training in R, Python, Hadoop, GitHub and SQL with a focus on real-world application. Participants will walk away with a portfolio of five projects to show to potential employers as well as a capstone project that spans the last two weeks of the course. The NYC Data Science Academy also helps students garner interest from recruiters and hiring managers through partnerships with businesses. In the last week of the course, students will participate in mock interviews and job search prep; many will also interview with hiring tech companies in the New York and Tri-State area.
|Locations||NYC and online|
The Data Science Career Track at Springboard consists of a six-month program that typically requires 10 to 15 hours per week of work. You’ll get access to the Springboard community, a personal mentor, career coach and student advisor. By the end of the program, participants will have an “interview-ready portfolio” and access to a data science network. Most students complete the course in two to four months and Springboard promises to refund your tuition if you don’t land a job within six months after graduating.
|Cost||$7,500 total, with month-by-month payment options|
|Locations||San Francisco and online|
Thinkful offers a self-paced online bootcamp with a project-based curriculum, career prep, and one-on-one mentorship with access to a full community of students, mentors and alumni. The course requires about 20 to 30 hours per week of work and most students graduate in about six months. Thinkful also offers a job guarantee — if you can’t find a job after graduating, the company will refund the cost of your tuition.
|Cost||$7,999 upfront, or $1,495 per month with the option for loans, financing and other payment plans|
|Locations||Washington (D.C.), Portland, Dallas, Los Angeles, Phoenix, San Diego, Atlanta and online|
5 emerging and niche data science bootcamps
- Data Science for Social Good
- Data Application Lab
- Insight Data Science
- K2 Data Science
- Microsoft Research Data Science Summer School
These data science bootcamps are recommended by Switchup, but they haven’t received many — or, in some cases, any — reviews. But that doesn’t mean they aren’t worth considering. Some are newer programs that recently launched, while others involve exclusive fellowships or target niche markets, like Ph.D. students or experienced professionals. Look to see if any of these data science bootcamps better suit your professional needs.
Data Science for Social Good
This Chicago-based bootcamp has specific goals; it focuses on churning out data scientists who want to work in fields such as education, health and energy to help make a difference in the world. Data Science for Social Good offers a three-month long fellowship program offered through the University of Chicago, and it allows students to work closely with both professors and professionals in the industry. Attendees are put into small teams alongside full-time mentors who help them through the course of the fellowship to develop projects and solve problems facing specific industries. The program lasts 14 weeks and students complete 12 projects in partnership with nonprofits and government agencies to help tackle problems currently facing those industries.
|Switchup rating||No reviews|
|Cost||Free for those accepted|
|Locations||Chicago, with plans to expand to Charlotte (N.C.) and Chile by 2019|
Data Application Lab
Data Application Lab is another in-person, full-time data science bootcamp stationed in Los Angeles and Silicon Valley, but they also offer online options if you can’t get to those locations. The programs focus on equipping students with “industry practical needs” in conjunction with traditional academics. Courses use lectures, hands-on experience and lab projects to help expedite the learning process. If you have less experience in computer science, they also offer a programming class and a 10-week internship to get you up to speed.
Data science programs include general data science, big data engineer, the basics, data analyst, data science full stack and big data solutions. Each bootcamp has different requirements, so you’ll want to make sure you meet them before you apply. Tuition varies depending on the program, location or course you choose.
|Switchup rating||No reviews|
|Locations||Boston, Washington (D.C.) and online|