Big Data: Career Opportunities Abound in Tech’s Hottest Field

As big data continues to grow, companies around the world are on the hunt for technical recruits — a shift experts predict will continue through 2014 and beyond. WinterWyman’s NY Tech Search division, for example, has seen a 300% increase in demand for data scientists and engineers since 2013. The hottest sectors for big data growth are ad tech, financial services, ecommerce and social media

The hottest sectors for big data growth are ad tech, financial services, ecommerce and social media — those with the highest opportunity for revenue.

According to Dice, a few of the U.S. cities making the most big data hires include New York, Washington D.C./Baltimore area, Boston and Seattle.

Below is a quick guide for both companies and job seekers seeking big data opportunities.

Q&A

What is big data?
Big data is the advance in data management technology, which allows for an increase in the scale and manipulation of a company’s data. It allows companies to know more about their customers, products and their own infrastructure. More recently, people have become increasingly focused on the monetization of that data.

How can companies benefit by using big data; and more importantly, which industries use it?
Big data is everywhere; the volume of data produced, saved and mined is mind-boggling. Today, companies use data collection and analysis to formulate more cogent business strategies. This will continue to be an emerging area for all industries.

What is the current hiring landscape for big data? What are the salary ranges?
Currently, tech positions in big data are hard to fill because the demand is overwhelming and the talent pool is so small. It is difficult to find job candidates with the specific skill sets needed while balancing the cost of that talent. Companies need to ensure they can make money off of the “data” to justify offering candidates a large salary.

The highest demand is for data engineers who can code, utilize data analytics and manipulate for marketing purposes. The newest — and most sought-after role — is for data scientists who can integrate big data into both the company’s IT department and business functions.

These positions are all within a salary range of $90-$180,000

These positions are all within a salary range of $90-$180,000, depending on the individual role and experience. The typical time to hire is less than three weeks.

What is a data scientist?
Data scientists integrate big data technology into both IT departments and business functions. Many have a formal education in computer science and math, focusing on architecture/code, modeling, statistics and analytics. There is also a trend toward data-oriented master’s degree programs being offered at many colleges and universities. A data scientist must also understand the business applications of big data and how it will affect the business organization, and be able to communicate with IT and business management.

What can job seekers do to get the skills they need for a job in big data?
You need to be a marketable programmer already, or enroll in a program/school like General Assembly. To help make you more marketable to transition to a big data job, aim to work on projects using platforms like Hadoop or Mongo.

3 tips for hiring companies

  • 1. Don’t delay time to hire: These candidates have a lot of career options. They often have multiple job offers and are not on the market long. Wait too long, and they won’t be available.
  • 2. Promote your company: It’s good to have a distinct company culture, but candidates are more concerned with how their job will evolve, who they will work with, what technology they will use, etc. Be sure that you’re hitting all of these key points throughout the interview process and on your company website’s “careers” section.
  • 3. Practice flexibility when considering candidates’ qualifications: Because of the limited number of qualified candidates, companies must be open to considering candidates from different industries who have transferable job skills. Focus on a candidate’s potential to learn and grow with the company rather than strict prerequisites for hard skills.

3 tips for job seekers

  • 1. You have options: The market is strong, and this is a great time to be looking for employment. You have negotiating power when it comes to salary, benefits, etc.
  • 2. Contract or permanent jobs abound: Most job candidates can convert a contract/temporary position to permanent employment. There are more opportunities today than in the past few years to transition from a contract position to a full-time one. That being said, developing a strong portfolio of contract/freelance work can prove lucrative — you’ll need to decide what option works best for your needs, goals and schedule.
  • 3. Your technical skills are hot: People with strong tech backgrounds on their resumes are being bombarded with offers. You can afford to be selective about the company you decide to ultimately join.

Predictions for the future of big data

In my expert opinion, there will be a continued hiring demand for big data-related positions in industries such as mobile, healthcare and financial services — but industries that have the ability to monetize big data, such as ad tech, will likely have a longer, deeper and steeper hiring demand for big data-related positions.

What is so exciting is that big data applies to almost all industries. As a data scientist, you can work for any number of companies or industries. As an employer, it’s all about finding the right talent to fit your big data needs.

Originally posted via “Big Data: Career Opportunities Abound in Tech’s Hottest Field”

Source: Big Data: Career Opportunities Abound in Tech’s Hottest Field by analyticsweekpick

Impacting Insurance Company’s bottom line through Big-data

In one of the recent blog I published What Insurance companies could do to save others from eating their lunch, I have stated the importance of good data management as one of the essential component for business growth. Big data fits right into that alley.

What is big-data?
In ideal scenario, big-data definition change from case to case. But, to summarize Wikipedia does a good job: In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. The challenges include capture, storage, search, sharing, analysis, and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to “spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.”

Why is it important for insurance and why should they care?
Insurance companies have been gathering data (both structured and unstructured) for years now. So, the current big-data landscape fits their bill well. Insurance companies could use big-data to analyze their data and learn great deal of insights to service customers better and help differentiate them from competitors. Here are a few used cases that should motivate insurers from embracing big-data in their arsenal.

1.     Do linkage analysis of structured and unstructured data: Insurance companies have been collecting data for eons. This data is placed in either structured or unstructured form. Before the age of sophisticated analytical tools, it was nearly impossible to comb the data for any further insights considering the amount of effort and cost vs expected outcome. Thanks to big-data, a lot of tools have emerged that are well capable of doing that task with minimum resource requirement and promise great outcomes. So, it should be taken as an opportunity for insurance companies to look deep in their data silos and process them to find meaningful  correlations and insights to further help business.

2.     Use public data from social web and scoop for prospect signals: Another big area that has been unleashed by sophisticated big-data tool is capturing social-web and searching it for any meaningful keyword, and use it to understand the insurance landscape. For example, consider looking for keywords that are utilized to describe one’s business and see how much you lead that space. There are many other used cases that are super critical to insurance and could be solved by big-data tools.

3.     Use data from social web to spy on competition: This is another powerful used case being used by many companies to better understand their competition, their brand perception and their social media footprint. It is done by sniffing on public web activity for competition and further analyzes the findings to learn more about competition. Real-time nature of the data makes it all the more interesting keeping information current and real-time.

4.     Sniffing and processing all the product interfaces for insights: This is another big area harnessed by big-data tools. Due to superior analytical skills, big-data tools could also help in providing real-time insights from data collected from all the product interfaces. Whether it is verbal(call-center logs, queries etc.) or non-verbal data(logs, activity report, market conditions etc.). Once an appropriate model-framework to consume that data is build, big-data tools could get to job and start real-time analysis of customers, sales and provide invaluable actionable insights.

5.     Big-data for data driven innovation: I have been a strong advocate for data driven innovation. Data driven innovation is innovating using the power of data. Once appropriate modules are identified that could advocate innovations, their information could be then processed and monitored for any correlations with business critical KPIs. Once a direct link is established, tweaking the process and monitoring its impact on the system and quickly help in understanding the areas for improvement. So, this module could be used to create innovation and promote lean build-measure-learn loops for faster learning and deployment. This will drastically reduce the execution cycle for testing innovations.

I am certain that there are numerous other areas, in which insurance could pitch in. Feel free to share your thoughts in comments.

Source by d3eksha

Jan 04, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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Superintelligence: Paths, Dangers, Strategies

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Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

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Q:What is statistical power?
A: * sensitivity of a binary hypothesis test
* Probability that the test correctly rejects the null hypothesis H0H0 when the alternative is true H1H1
* Ability of a test to detect an effect, if the effect actually exists
* Power=P(reject H0|H1istrue)
* As power increases, chances of Type II error (false negative) decrease
* Used in the design of experiments, to calculate the minimum sample size required so that one can reasonably detects an effect. i.e: ‘how many times do I need to flip a coin to conclude it is biased?’
* Used to compare tests. Example: between a parametric and a non-parametric test of the same hypothesis

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Decoding the human genome originally took 10 years to process; now it can be achieved in one week.

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Which Machine Learning to use? A #cheatsheet

In current teams driving data science, there has been an on-slot of discussions around which machine learning method to use and which algorithms perform optimally for which solutions.

There are several dependencies to make that decision. Some are primarily linked to:
1. Type of data: such as quantity, quality and varsity in data.
2. Resources for the task
3. Expected time for the task
4. Expectation from the data
etc.

Our friends at SAS has put together a great cheet sheet that could work as a great starting point.

Chart picked from: SAS Blog

Originally Posted at: Which Machine Learning to use? A #cheatsheet

Data And Analytics Collaboration Is A Win-Win-Win For Manufacturers, Retailers And Consumers

Collaboration between consumer packaged goods (CPG) manufacturers and retailers is more important than ever. Why? It’s because manufacturers struggle with fading brand loyalty from price sensitive consumers who are increasingly switching to private label, niche or locally-produced products. And at the same time, retailers face two major challenges of their own: showrooming—when shoppers come into the store to browse before buying online—and pressures from aggressive online retailers.

To best counter these and other issues, manufacturers and retailers need to engage as partners. Through data and analytic collaboration, they can establish a critical connection that enables them to work together to co-create differentiated in-store experiences that deliver mutual benefits.

Everybody Wins

Consumers used to get product information from advertisements and by talking to salespeople in brick-and-mortar stores. Today, shoppers aggressively research and compare products before setting foot inside a store. Or even while in the store by looking across competitive retailer outlets on their smartphones and tablets. Due to the mobile revolution, prices, product variations and reviews are more available and easier to compare than ever.
This showrooming trend can result in lost customers and lost revenues. It also renders traditional approaches to collaboration between manufacturers and retailers ineffective. Making decisions based on historical sales data is no longer sufficient. Driving category growth is increasingly about serving the right information to the shopper at the right time to support a purchase decision.

What’s needed is cooperation along with analysis of integrated data to deliver actionable insights that enable better brand, product, packaging, supply chain and business planning decisions; and to power shopper marketing programs in-store and online. This approach benefits not only manufacturers and retailers, but also consumers who enjoy the advantages of shopper reward and loyalty programs.

The Winning Road

With many shopping decisions being made outside the store environment, there is an increased priority placed on understanding and influencing shopper behavior at many points along the path to purchase. Mobile, social networks, Web and email channels are the new media used every day by marketers to target content and offers that drive purchase activity. One-to-one relationships are becoming the new currency upon which the most valued brands are based, while creating unique shopper experiences has come to define retail excellence.

Leading CPG companies have differentiated themselves by executing laser-focused consumer connection strategies based on data analytics. A variety of data-driven decisions, from assortment and inventory planning through pricing and trade promotion, all affect shopper purchase outcomes.

Based on integrated and detailed data from sources such as the retailer’s point-of-sale system, loyalty programs, syndicated sources and data aggregators, analytics allows CPG companies to become more relevant to their consumers by meeting their needs, earning their loyalty and building relationships. In short, advanced analytics separate successful retail-manufacturer partnerships from those that aren’t.
Focus On Demand

Maintaining an efficient distribution and inventory process is critical to maximizing financial performance and meeting buyers’ expectations. Sharing shopper data and insights supports concepts such as collaborative demand forecasting, dynamic replenishment and vendor-managed inventory.

Price, promotion and shelf placement are critical areas that drive collaboration, but the efforts are often based on summary-level and infrequently updated data. To effectively move the needle in managing a category at the shelf, organizations must have a strong analytics foundation. Armed with better insights, category managers, store operations leaders, merchandise planners and allocation decision-makers can optimize the factors that influence sales performance of products in specific categories, geographies and stores.

Value In Data-Driven Collaboration

CPG manufacturer and retail business executives recognize the value of fact-based decision making enabled by integrated data and real-time analytics. Data-driven collaboration establishes a beneficial connection that allows both sides to achieve common objectives, including increased product sales, growth in revenue and brand loyalty.

Gib Bassett is the global program director for Consumer Goods at Teradata.

Justin Honaman is a Partner with Teradata and leads the National Consumer Goods Industry Consulting practice.

This story originally appeared in the Q2 2014 issue of Teradata Magazine.

Originally posted via “Data And Analytics Collaboration Is A Win-Win-Win For Manufacturers, Retailers And Consumers”.

Originally Posted at: Data And Analytics Collaboration Is A Win-Win-Win For Manufacturers, Retailers And Consumers