Currently, on the Web there exists the ability to complete a Masters curriculum in Data Science using Free/Open Source classes, ebooks, workspaces, software.  DataScienceMasters.org provides a full curriculum and links to each resource.  



Will it be an official Masters? No, but an official Masters is not always what is needed, it’s knowledge and experience working with the tools and techniques necessary to actually do Data Science.&nbsp; For some, this free curriculum will allow business-line leaders, Analysts and Programmers from other fields to fill in the education gaps and get better at their job, as well as, one step closer to being an actual Data Scientist.&nbsp;



<h2 class="wp-block-heading">The Open-Source Data Science Masters</h2>



The open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.



<h3 class="wp-block-heading">The Internet is Your Oyster</h3>



With Coursera, ebooks, Stack Overflow, and GitHub — all free and open — how can you afford not to take advantage of an open source education?



<h3 class="wp-block-heading">The Motivation</h3>



We need more Data Scientists.



…by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.



— <a href="http://bit.ly/datascienceshortage">McKinsey Report Highlights the Impending Data Scientist Shortage</a> 23 July 2013



There are little to no Data Scientists with 5 years experience, because the job simply did not exist.



— David Hardtke <a href="http://bit.ly/howtohireadatascientist">How To Hire A Data Scientist</a> 13 Nov 2012



<h3 class="wp-block-heading">An Academic Shortfall</h3>



Classic academic conduits aren’t providing Data Scientists — this talent gap will be closed differently.



Academic credentials are important but not necessary for high-quality data science. The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.



We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science,bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available. Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.



And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.

Guide | How to Obtain a Free “open Source” Masters in Data Science

If there were a competition for breathless hype in technology, big data would be the current champion — there’s even a Brooklyn-based band by the name.&nbsp; And though the phrase is ubiquitous in boardrooms and IT departments across categories of companies, the insurance industry is in many ways taking the lead in getting real business value from the volume, velocity, and variety of massive datasets.



Why are insurers taking this challenge on at the same time they are grappling with core-systems transformation, evolving customer expectation and regulatory upheaval? Well, says Pawan Divakarla, big data business leader at Progressive Insurance, “Big data actually does work.” And the results can be dramatic.



According to recent research from Accenture, a third of insurers now are using data from wearable technologies, such as FitBits and Jawbones, to collect lifestyle data from insureds. Insurance telematics is now mainstream — and subject matter experts say the latest big data technologies and techniques offer insurance companies an opportunity to circumvent exhaustive data cleanup efforts that previously have stymied reporting and analytics efforts.



But big data is still difficult, Divakarla admits, and while the technology itself offers great promise, people are a critical element for success. Insurers must develop data workers, not just data scientists, who need to understand programming, where to find data, and how it’s structured, as well as the business issues they are tasked with solving, he explains.



<h2 class="wp-block-heading">10 Big Data Case Studies</h2>



Insurance Networking News, our sister brand, identified 10 insurance companies, across lines of business, that demonstrate true leadership in big data and analytics excellence by developing cross-enterprise strategy, delivering results from the corporate investment, and perhaps most importantly, identifying and recruiting key staff members with the right expertise. The strategies may vary, but the commitment is real.



Here are the case studies:



<ol>
<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-progressive-insurance-35951-1.html">Progressive Insurance</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-massmutual-35952-1.html">MassMutual</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-american-family-insurance-35953-1.html">American Family Insurance</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-john-hancock-35954-1.html">John Hancock</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-cna-35959-1.html">CNA</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-nationwide-35958-1.html">Nationwide</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-swiss-re-35957-1.html">Swiss Re</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-allstate-35960-1.html">Allstate</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-zurich-35961-1.html">Zurich</a></li>



<li><a href="http://www.insurancenetworking.com/news/data-analytics/big-datas-big-guns-qbe-north-america-35962-1.html">QBE North America</a></li>



<li></li>
</ol>

Case Studies | 10 Big Data Projects in the Insurance Industry

You are excited. You have got that much awaited interview call for that dream analytics job. You are confident you will be perfect for the job. Now all that remains is convincing the interviewer. Don’t you wish you knew what kind of questions they are going to be ask?



As co-founder and one of the chief trainers at Jigsaw Academy, an online analytics training institute, I regularly get calls from our students days before their scheduled interview asking me just this. I am going to share with you just what I share with them. Here you go. Below are a few of the more popular questions you could get asked and the corresponding answers in a nutshell.&nbsp;



Question 1. Can you outline the various steps in an analytics project?



Broadly speaking these are the steps. Of course these may vary slightly depending on the type of problem, data, tools available etc.



1. Problem definition – The first step is to of course understand the business problem. What is the problem you are trying to solve – what is the business context? Very often however your client may also just give you a whole lot of data and ask you to do something with it. In such a case you would need to take a more exploratory look at the data. Nevertheless if the client has a specific problem that needs to be tackled, then then first step is to clearly define and understand the problem. You will then need to convert the business problem into an analytics problem. I other words you need to understand exactly what you are going to predict with the model you build. There is no point in building a fabulous model, only to realise later that what it is predicting is not exactly what the business needs.



2. Data Exploration – Once you have the problem defined, the next step is to explore the data and become more familiar with it. This is especially important when dealing with a completely new data set.



3. Data Preparation – Now that you have a good understanding of the data, you will need to prepare it for modelling. You will identify and treat missing values, detect outliers, transform variables, create binary variables if required and so on. This stage is very influenced by the modelling technique you will use at the next stage.&nbsp; For example, regression involves a fair amount of data preparation, but decision trees may need less prep whereas clustering requires a whole different kind of prep as compared to other techniques.



4. Modelling – Once the data is prepared, you can begin modelling. This is usually an iterative process where you run a model, evaluate the results, tweak your approach, run another model, evaluate the results, re-tweak and so on….. You go on doing this until you come up with a model you are satisfied with or what you feel is the best possible result with the given data.



5. Validation – The final model (or maybe the best 2-3 models) should then be put through the validation process. In this process, you test the model using completely new data set i.e. data that was not used to build the model. This process ensures that your model is a good model in general and not just a very good model for the specific data earlier used (Technically, this is called avoiding over fitting)



6. Implementation and tracking – The final model is chosen after the validation. Then you start implementing the model and tracking the results. You need to track results to see the performance of the model over time. In general, the accuracy of a model goes down over time. How much time will really depend on the variables – how dynamic or static they are, and the general environment – how static or dynamic that is.



Question 2. &nbsp; What do you do in data exploration?



Data exploration is done to become familiar with the data. This step is especially important when dealing with new data. There are a number of things you will want to do in this step –



a.&nbsp; &nbsp; &nbsp; &nbsp; What is there in the data – look at the list of all the variables in the data set. Understand the meaning of each variable using the data dictionary. Go back to the business for more information in case of any confusion.



b.&nbsp; &nbsp; &nbsp; &nbsp; How much data is there – look at the volume of the data (how many records), look at the time frame of the data (last 3 months, last 6 months etc.)



c. &nbsp; &nbsp; &nbsp; &nbsp; Quality of the data – how much missing information, quality of data in each variable. Are all fields usable? If a field has data for only 10% of the observations, then maybe that field is not usable etc.



d.&nbsp; &nbsp; &nbsp; &nbsp; You will also identify some important variables and may do a deeper investigation of these. Like looking at averages, min and max values, maybe 10th and 90th percentile as well…



e.&nbsp; &nbsp; &nbsp; &nbsp; You may also identify fields that you need to transform in the data prep stage.



&nbsp;Question 3: What do you do in data preparation?



In data preparation, you will prepare the data for the next stage i.e. the modelling stage. What you do here is influenced by the choice of technique you use in the next stage.



But some things are done in most cases – example identifying missing values and treating them, identifying outlier values (unusual values) and treating them, transforming variables, creating binary variables if required etc,



This is the stage where you will partition the data as well. i.e create training data (to do modelling) and validation (to do validation).



&nbsp;Question 4: How will you treat missing values?



The first step is to identify variables with missing values. Assess the extent of missing values. Is there a pattern in missing values? If yes, try and identify the pattern. It may lead to interesting insights.



If no pattern, then we can either ignore missing values (SAS will not use any observation with missing data) or impute the missing values.



Simple imputation – substitute with mean or median values



OR



Case wise imputation –for example, if we have missing values in the income field.



Question 5: How will you treat outlier values?



You can identify outliers using graphical analysis and univariate analysis. If there are only a few outliers, you can assess them individually. If there are many, you may want to substitute the outlier values with the 1stpercentile or the 99th percentile values.



If there is a lot of data, you may decide to ignore records with outliers.



Not all extreme values are outliers. Not all outliers are extreme values.



Question 6: How do you assess the results of a logistic regression analysis?



You can use different methods to assess how good a logistic model is.



a. Concordance – This tells you about the ability of the model to discriminate between the event happening and not happening.



b. Lift – It helps you assess how much better the model is compared to random selection.



c. Classification matrix – helps you look at the false positives and true negatives.



Some other general questions you will most likely be asked:



<ul>
<li>What have you done to improve your data analytics knowledge in the past year?</li>



<li>What are your career goals?</li>



<li>Why do you want a career in data analytics?</li>
</ul>



The answers to these questions will have to be unique to the person answering it. The key is to show confidence and give well thought out answers that demonstrate you are knowledgeable about the industry and have the conviction to work hard and excel as a data analyst.



About Sarita Digumarti: Sarita has over 10 years of extensive analytics and consulting experience across diverse domains including retail, health-care and financial services. She has worked in both India and the US, helping clients tackle complex business problems by applying analytical techniques. She has a Master’s degree in Quantitative Economics, from Tufts University, Boston, and a PG Diploma in Management from T.A. Pai Management Institute, Manipal. Sarita’s <a href="http://in.linkedin.com/in/saritadigumarti">Linkedin profile</a>

Common Analytics Interview Questions

<a href="http://datasciencereport.com/2014/02/09/top-5-enterprise-hadoop-stories-of-2013/">February 9, 2014</a> · by <a href="http://datasciencereport.com/author/friars93/">Ted O&#8217;Brien</a> · in <a href="http://datasciencereport.com/category/case-studies/">Case Studies</a>, <a href="http://datasciencereport.com/category/data-resources-tools/">Data Resources &amp; Tools</a>, <a href="http://datasciencereport.com/category/hadoop/">Hadoop</a>, <a href="http://datasciencereport.com/category/news-articles/">News Articles</a>, <a href="http://datasciencereport.com/category/top-ranked/">Top Ranked</a>. ·



By: <a href="http://searchdatamanagement.techtarget.com/contributor/Jack-Vaughan">Jack Vaughan</a> : Jack Vaughan is SearchDataManagement’s news and site editor. Email him at <a href="mailto:jvaughan@techtarget.com">jvaughan@techtarget.com</a>, and follow them on Twitter: <a href="https://twitter.com/sDataManagement">@sDataManagement</a>.&nbsp; <a href="http://searchdatamanagement.techtarget.com/feature/Top-five-enterprise-Hadoop-stories-of-2013?asrc=EM_NLN_25941240&amp;utm_medium=EM&amp;utm_source=NLN&amp;utm_campaign=20140102_Top%20five%20enterprise%20Hadoop%20stories%20of%202013_mewebb&amp;track=NL-1816&amp;ad=891000">Original Article Source</a>



<figure class="wp-block-image"><img decoding="async" src="https://lh7-us.googleusercontent.com/-dmYGsE2iNwHqN5j0-PkWLUv6tqtbyChCmcZZZDrhYyNICZwey-vMeTJxJAXCbSM3vn-isZXriwWSaYLlVgnlCfadBAFva3vjCS8zZazJkUthb_AUsii6KopNMrz1v3hfW25M490bxhoDND5tbV110I" alt="hadoop 2"/></figure>



Jack writes: It was clear when 2013 began that open source Hadoop was entering a new phase. It had moved from its original roots in large-scale, Yahoo-style Web applications and was appearing in analytical pilot projects across a variety of enterprises. During the year, software companies worked to add features to the Hadoop data platform in order to enable its wider use in production. As the spotlight shone on the software often represented by a small elephant, SearchDataManagement endeavored to cut through the hype that can obscure the real trends.



Our editors have reviewed our most popular Hadoop-related <a href="http://searchcio.techtarget.com/news/2240211515/SearchCIOs-top-stories-of-2013-Big-data-cloud-and-more">stories this year</a>, and taken together, they form a narrative of Hadoop in 2013. The content followed the path of Hadoop and related software tools, such as HBase, as they gained footholds in the enterprise. We also saw flurries of product activity, including new Hadoop distributions from major IT vendors. From mid-year to year’s end, a new version of the platform known as Hadoop 2.0 — complete with enterprise enhancements — gained attention.



<a href="http://searchdatamanagement.techtarget.com/tip/Hadoop-helps-bring-big-data-into-a-data-warehouse-environment">Hadoop helps bring big data into a data warehouse</a>. During the year, Hadoop implementers in greater numbers began to place their systems into workflows attached to existing enterprise data warehouses. The effect has been particularly noted on established extract, transform and load, or ETL, architectures. Hadoop’s ability to stage data with less reliance on full-scale up-front schemas has in some cases been a plus.



See these related stories: <a href="http://searchdatamanagement.techtarget.com/news/2240178820/Confronting-MapReduce-Hadoop-problems-and-complexities">Confronting MapReduce, Hadoop complexities </a><a href="http://searchdatamanagement.techtarget.com/news/2240182009/Up-from-the-sandbox-Hadoop-data-management-rising-in-importance">Hadoop’s move up from the developer’s sandbox </a><a href="http://searchdatamanagement.techtarget.com/news/2240184329/Expanded-Hadoop-use-cases-will-drive-need-for-new-enterprise-features">Expanded Hadoop use cases will drive need for new enterprise features</a>



<a href="http://searchdatamanagement.techtarget.com/feature/Big-data-fast-Avoiding-Hadoop-performance-bottlenecks">Big data, fast: Avoiding Hadoop performance bottlenecks</a>. Experience with Hadoop in the field shows that more than just “some assembly is required.” In very many shops, Hadoop needs tweaking and enhancements to meet enterprise needs. Our reporting also indicated the Hadoop-style of data processing that worked well at Google and Yahoo may not be the cure for every company’s problem.



See these related stories: <a href="http://searchdatamanagement.techtarget.com/opinion/Googles-big-data-infrastructure-Dont-try-this-at-home">Google’s big data infrastructure: Don’t try this at home </a><a href="http://searchdatamanagement.techtarget.com/video/White-Mind-the-hype-in-evaluating-and-choosing-Hadoop-technology">Mind the hype in choosing Hadoop technology</a>



<a href="http://searchdatamanagement.techtarget.com/news/2240179304/EMC-Intel-unveil-new-Hadoop-distributions-but-how-many-is-too-many">EMC, Intel unveil new Hadoop distributions, but how many is too many?</a> If Hadoop was wanting in some areas, there was no shortage of vendors ready to fill in with product improvements. Notably, the year witnessed IT heavyweights EMC and Intel entering the Hadoop Derby. Easier configuration was often a hallmark of the product enhancements.



See this related story: <a href="http://searchdatamanagement.techtarget.com/news/2240187351/Evolving-Hadoop-ecosystem-presents-new-ways-to-program-big-data-apps">Evolving Hadoop ecosystem presents new ways to program big data apps</a>



<a href="http://searchdatamanagement.techtarget.com/news/2240187351/Evolving-Hadoop-ecosystem-presents-new-ways-to-program-big-data-apps">Enterprise Hadoop will need to work with existing processes</a>. In June, the Hadoop Summit in San Jose, Calif., was a coming-out party of sorts for Hadoop 2.0. Spotlighted in this new version is YARN (for Yet Another Resource Negotiator), whose offbeat name belies a significant upgrade that expands Hadoop’s application into undertakings formerly limited to batch processing schemes. This and other bells, whistles and add-ons further targeted Hadoop for use in the enterprise. New features bring new capabilities but also new challenges.



See these related stories: <a href="http://searchdatamanagement.techtarget.com/feature/Hadoop-2-release-adds-potential-uses-and-new-issues-to-consider">Hadoop 2 release adds new issues to consider </a><a href="http://searchdatamanagement.techtarget.com/feature/Big-data-applications-require-new-thinking-on-data-integration">Big data applications require new thinking on data integration </a><a href="http://searchdatamanagement.techtarget.com/podcast/Hadoop-Summit-2013-Where-is-Apache-Hadoop-heading">Where is Apache Hadoop heading?</a>



<a href="http://searchdatamanagement.techtarget.com/opinion/Security-services-company-uses-MapR-HBase-to-calm-data-downpour">Security services company uses HBase to calm data downpour</a>. User experiences show that Hadoop’s use can go beyond analytics to include operations. For Omaha, Neb.-based managed security provider Solutionary Inc., Hadoop did a bit of both. As described by the company’s software engineering director, Hadoop and its compatriot HBase columnar database proved to be a worthy alternative to the ever-expanding use of Oracle Database RAC. For another company, Hadoop and HBase were seen as high-horsepower open source alternatives to a proprietary rules-based system.



See this related story: <a href="http://searchdatamanagement.techtarget.com/feature/Ancestrycom-teams-work-together-to-use-Hadoop-framework-for-DNA-app">Ancestry.com teams work together to use Hadoop framework for DNA app</a>



Jack Vaughan is SearchDataManagement’s news and site editor. Email him at <a href="mailto:jvaughan@techtarget.com">jvaughan@techtarget.com</a>, and follow them on Twitter: <a href="https://twitter.com/sDataManagement">@sDataManagement</a>.

Top 5 | Enterprise Hadoop Stories of 2013

Data Scientist was ranked as the <a href="https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm">third of the highest paying jobs</a> in the US by Glassdoor in 2022 with a Median base salary of $120,000. There are numerous aspects such as experience, location, education, etc that play a major role in determining how much a data scientist earns on average. 



Data Scientist has become one of the most in-demand jobs globally due to the growth and expansion of Big data among organizations. In addition, the data scientist jobs are expected to witness a growth of <a href="https://www.bls.gov/ooh/math/data-scientists.htm">35% between 2022 to 2032</a>. 



In this article, we are going to take an in-depth look at the Data Scientist salary in the US in 2024 and highlight various methods that can help you enhance your chances of earning a high-paying data scientist job. 



<figure class="wp-block-image size-large"><img width="1024" height="576" loading="lazy" decoding="async" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxMDI0IiBoZWlnaHQ9IjU3NiI+PHJlY3Qgd2lkdGg9IjEwMCUiIGhlaWdodD0iMTAwJSI+PGFuaW1hdGUgYXR0cmlidXRlTmFtZT0iZmlsbCIgdmFsdWVzPSJyZ2JhKDE1MywxNTMsMTUzLDAuNSk7cmdiYSgxNTMsMTUzLDE1MywwLjEpO3JnYmEoMTUzLDE1MywxNTMsMC41KSIgZHVyPSIycyIgcmVwZWF0Q291bnQ9ImluZGVmaW5pdGUiIC8+PC9yZWN0Pjwvc3ZnPg==" alt="" class="wp-post-619 wp-image-621" data-public-id="Data-Scientist-Salary-in-US-2024/Data-Scientist-Salary-in-US-2024.jpg" data-format="jpg" data-transformations="f_auto,q_auto" data-version="1708189999" data-seo="1" data-responsive="1" data-size="1024 576" data-delivery="upload" onload=";window.CLDBind?CLDBind(this):document.body.appendChild(document.createElement(&#039;script&#039;)).src=&#039;https://1f7ldm7fz58q81zmd6lpnu135.datasciencereport.com/?cloudinary_lazy_load_loader=1&#039;;this.onload=null;" data-cloudinary="lazy" /></figure>



<h2 class="wp-block-heading">How much do Data Scientists make?</h2>



<a href="https://www.bls.gov/ooh/math/data-scientists.htm">According to the U.S. Bureau of Labor Statistics</a>, the Median Pay for a Data Scientist in 2022 was $103,500 per year or $49.76 per hour. 



According to other salary aggregation sites, the average salary of a Data scientist is as follows:&nbsp;



<figure class="wp-block-table"><table><tbody><tr><td>Indeed</td><td>Zippia</td><td>Glassdoor</td><td>US BLS</td><td>Payscale</td></tr><tr><td>$124,172</td><td>$106,104</td><td>$117,664</td><td>$103,500</td><td>$99,344</td></tr></tbody></table></figure>



Data Scientists is a good paying job as it offers higher than average salaries to its employees. There are various factors that contribute to the average salary earned by a Data scientist such as experience, location, qualifications, skills, industry, etc. 



<h3 class="wp-block-heading">Data Scientist Salaries in the U.S By experience</h3>



The experience level of an employee plays a crucial role when it comes to salaries. Usually, the more experience you gain in the data science field, the more you can expect your salary to increase. Employees with more experience tend to take more responsibility and leadership roles and are capable of contributing to complex projects which results in higher compensation. 



Now, let’s take a look at the Data scientist salaries in the US by experience: 



<figure class="wp-block-table"><table><tbody><tr><td>Experience&nbsp;</td><td>Salary&nbsp;</td></tr><tr><td>0-1 years</td><td>$96,986</td></tr><tr><td>1-3 years</td><td>$108,197</td></tr><tr><td>4-6 years</td><td>$118,101</td></tr><tr><td>7-9 years</td><td>$124,037</td></tr><tr><td>10-14 years</td><td>$131,327</td></tr><tr><td>15+ years</td><td>$140,079</td></tr></tbody></table></figure>



Source: <a href="https://www.glassdoor.com/Salaries/us-data-scientist-salary-SRCH_IL.0,2_IN1_KO3,17.htm?clickSource=searchBtn">Glassdoor</a>



<h3 class="wp-block-heading">Data Scientist Salaries in the U.S By location</h3>



Another important factor that can impact the average base salary as a Data scientist is your location. There are certain states in the US that tend to pay higher than others. 



Below we have listed down the average base salary of a Data scientist in the U.S by location: 



<figure class="wp-block-table"><table><tbody><tr><td>Location&nbsp;</td><td>Average base salary&nbsp;</td></tr><tr><td>Alabama</td><td>$109,834</td></tr><tr><td>Alaska</td><td>$113,762</td></tr><tr><td>Arizona</td><td>$117,070</td></tr><tr><td>Arkansas</td><td>$113,031</td></tr><tr><td>California</td><td>$144,128</td></tr><tr><td>Colorado</td><td>$110,351</td></tr><tr><td>Connecticut</td><td>$120,781</td></tr><tr><td>Delaware</td><td>$92,155</td></tr><tr><td>Florida</td><td>$108,494</td></tr><tr><td>Georgia</td><td>$108,934</td></tr><tr><td>Hawaii</td><td>$110,985</td></tr><tr><td>Idaho</td><td>$87,115</td></tr><tr><td>Illinois</td><td>$113,048</td></tr><tr><td>Indiana</td><td>$92,429</td></tr><tr><td>Iowa</td><td>$121,715</td></tr><tr><td>Kansas</td><td>$108,706</td></tr><tr><td>Kentucky</td><td>$128,980</td></tr><tr><td>Louisiana</td><td>$97,111</td></tr><tr><td>Maine</td><td>$110,822</td></tr><tr><td>Maryland</td><td>$136,597</td></tr><tr><td>Massachusetts</td><td>$123,158</td></tr><tr><td>Michigan</td><td>$104,884</td></tr><tr><td>Minnesota</td><td>$109,943</td></tr><tr><td>Mississippi</td><td>$128,717</td></tr><tr><td>Missouri</td><td>$107,506</td></tr><tr><td>Montana</td><td>$160,251</td></tr><tr><td>Nebraska</td><td>$122,578</td></tr><tr><td>Nevada</td><td>$99,659</td></tr><tr><td>New Hampshire</td><td>$126,097</td></tr><tr><td>New Jersey</td><td>$103,777</td></tr><tr><td>New Mexico</td><td>$135,364</td></tr><tr><td>New York</td><td>$122,415</td></tr><tr><td>North Carolina</td><td>$109,241</td></tr><tr><td>North Dakota</td><td>$97,967</td></tr><tr><td>Ohio</td><td>$100,464</td></tr><tr><td>Oklahoma</td><td>$99,659</td></tr><tr><td>Oregon</td><td>$127,002</td></tr><tr><td>Pennsylvania</td><td>$107,932</td></tr><tr><td>Rhode Island</td><td>$137,517</td></tr><tr><td>South Carolina</td><td>$82,466</td></tr><tr><td>South Dakota</td><td>$100,736</td></tr><tr><td>Tennessee</td><td>$126,414</td></tr><tr><td>Texas</td><td>$117,732</td></tr><tr><td>Utah</td><td>$118,093</td></tr><tr><td>Vermont</td><td>$140,841</td></tr><tr><td>Virginia</td><td>$130,127</td></tr><tr><td>Washington</td><td>$136,831</td></tr><tr><td>West Virginia</td><td>$77,728</td></tr><tr><td>Wisconsin</td><td>$126,388</td></tr><tr><td>Wyoming</td><td>$33,834</td></tr></tbody></table></figure>



Source: <a href="https://www.indeed.com/career/data-scientist/salaries">Indeed</a>&nbsp;



<h3 class="wp-block-heading">Data Scientist Salaries in the U.S. by industry</h3>



The Industry you work in is another crucial factor that determines the average salary earned by a Data scientist. 



Below we have mentioned the Top 5 paying Data Scientists Salary in the US by industry: 



<figure class="wp-block-table"><table><tbody><tr><td>Industry&nbsp;</td><td>Salary&nbsp;</td></tr><tr><td>Information Technology</td><td>$128,037</td></tr><tr><td>Media &amp; Communications</td><td>$117,664</td></tr><tr><td>Retail &amp; Wholesale</td><td>$117,664</td></tr><tr><td>Real Estate</td><td>$115,165</td></tr><tr><td>Financial Services</td><td>$115,657</td></tr></tbody></table></figure>



<h3 class="wp-block-heading">Job outlook for data scientists in the U.S</h3>



According to the <a href="https://www.bls.gov/ooh/math/data-scientists.htm">US Bureau of Labor Statistics</a>, Data Scientists&#8217; jobs are predicted to witness a major growth of 35% between 2021 to 2031. Various jobs in the data science field are expected to witness notable growth including Data analyst jobs which are likely to grow by 23%.&nbsp;&nbsp;



<figure class="wp-block-image size-large"><img width="1024" height="576" loading="lazy" decoding="async" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxMDI0IiBoZWlnaHQ9IjU3NiI+PHJlY3Qgd2lkdGg9IjEwMCUiIGhlaWdodD0iMTAwJSI+PGFuaW1hdGUgYXR0cmlidXRlTmFtZT0iZmlsbCIgdmFsdWVzPSJyZ2JhKDE1MywxNTMsMTUzLDAuNSk7cmdiYSgxNTMsMTUzLDE1MywwLjEpO3JnYmEoMTUzLDE1MywxNTMsMC41KSIgZHVyPSIycyIgcmVwZWF0Q291bnQ9ImluZGVmaW5pdGUiIC8+PC9yZWN0Pjwvc3ZnPg==" alt="" class="wp-post-619 wp-image-622" data-public-id="How-much-do-Data-Scientists-make/How-much-do-Data-Scientists-make.jpg" data-format="jpg" data-transformations="f_auto,q_auto" data-version="1708190094" data-seo="1" data-responsive="1" data-size="1024 576" data-delivery="upload" onload=";window.CLDBind?CLDBind(this):null;" data-cloudinary="lazy" /></figure>



Due to high demand and technical skill set requirements, the jobs offered in the data science field will pay extremely well. Especially, with Data science dominating almost every industry such as Retail, Healthcare, Entertainment and media, Technology, etc.&nbsp;



<h3 class="wp-block-heading">Data Scientist Salaries in the U.S. by Education</h3>



It is no surprise that the qualification or education of a Data Scientist is one of the prime factors that determine the annual salary of an individual. The bachelor’s degree earned by a data scientist is what demonstrates that a person is qualified to perform the job.&nbsp;



Earning a Master’s degree can play a significant role in helping data scientists secure a higher-paying job in comparison to those who only have a bachelor’s degree.



1. Associate Degree in Data Science



An Associate degree in Data Science is a UG two-year program that provides foundational knowledge to data science enthusiasts. This program enables students to work as a data scientist or analyst.&nbsp;



According to reports by Salary.com, an Associate Degree in Data Science can land you a job as a Data Scientist I with an annual salary of $70,542 to $74,823. Meanwhile, a Data Scientist II can earn up to $85,841 to $91,655.



2. Bachelor&#8217;s Degree in Data Science



A Bachelor’s degree in Data Science in the US can help you secure a job as a Data Scientist I with an annual salary of $71,749 to $75,676. While a Data Scientist II can earn an annual salary of $86,316 to $92,113.&nbsp;



3. Master&#8217;s Degree in Data Science



A Master’s Degree in Data Science can be earned by a student after the completion of their bachelor’s degree. Here’s how much you can expect to earn as a Data Scientist after completing your Master’s in Data science.&nbsp;



<figure class="wp-block-table"><table><tbody><tr><td>Data Scientist I&nbsp;</td><td>$72,847 to $76,581</td></tr><tr><td>Data Scientist II&nbsp;</td><td>$87,107 to $92,877</td></tr></tbody></table></figure>



4. Doctorate in Data Science



Having a doctorate in Data Science can add major value to your resume and help you land a good job opportunity as a Data scientist. Here’s how much you can expect to earn as a Data Scientist according to salary.com:&nbsp;



<figure class="wp-block-table"><table><tbody><tr><td>Data Scientist I&nbsp;</td><td>$73,945 to $77,485</td></tr><tr><td>Data Scientist II&nbsp;</td><td>$87,423 to $93,182</td></tr></tbody></table></figure>



<h2 class="wp-block-heading">How to increase your data scientist salary</h2>



You can increase your data scientist salary by investing in new skills, capabilities, and certifications, and focusing on your professional development can make you a valuable employee and contribute to a salary raise. Here are some of the methods through which you can increase your data scientist salary:&nbsp;



<h3 class="wp-block-heading">Build new skills as a data scientist</h3>



One of the best ways to increase your data scientist salary is by building a new skill. Acquiring a new skill as a data scientist can offer an advantage to you professionally and contribute to your salary growth. Here are some of the top new skills that you can learn:



<ul>
<li>Deep learning or Machine learning&nbsp;</li>



<li>Artificial Intelligence&nbsp;</li>



<li>Risk analysis</li>



<li>Programming languages&nbsp;</li>



<li>Software engineering&nbsp;</li>



<li>Data mining&nbsp;</li>



<li>Big Data&nbsp;</li>
</ul>



<h3 class="wp-block-heading">IBM Data Science</h3>



Another excellent way to increase your salary is by advancing your education and gaining a new certification. <a href="https://www.coursera.org/professional-certificates/ibm-data-science?action=enroll&amp;trk_ref=articleProductCard">IBM Data Science Professional Certificate</a> is an excellent certification that develops in-demand skills and hands-on experience. This is a beginner-level course, which you can complete in 5 months by providing 10 hours a week.&nbsp;



By the end of this course, you will master the most up-to-date practical skills and knowledge that are utilized by data scientists in their everyday roles. This way you can elevate your chances of obtaining a high-paying job.&nbsp;



<h3 class="wp-block-heading">Enhancing your data scientist resume</h3>



If you want to increase your data scientist salary then you need to focus on enhancing your data scientist resume. A well-crafted resume helps create a good impression of the employee in front of the recruiter and increases the chances of securing a good-paying job.&nbsp;



One of the ways to enhance your resume is by earning a professional certificate in the data science field. Achieving a certificate in data science can help you showcase your technical skills and prove your potential in front of the hiring manager. Below we have listed down some of the best Professional certificates you can apply for:



<ul>
<li><a href="https://www.coursera.org/specializations/boulder-data-structures-algorithms">Data Science Foundations: Structures and Algorithms Specialization &#8211; University of Colorado Boulder</a></li>



<li><a href="https://www.coursera.org/specializations/data-science-fundamentals-python-sql">Data Science Fundamentals with Python and SQL Specialization &#8211; IBM</a></li>



<li><a href="https://www.coursera.org/specializations/bi-foundations-sql-etl-data-warehouse">Business Intelligence Foundations with SQL, ETL, and Data Warehousing &#8211; IBM</a></li>



<li><a href="https://www.coursera.org/professional-certificates/azure-data-scientist">Microsoft Azure Data Scientist Associate (DP-100)</a></li>



<li><a href="https://www.coursera.org/specializations/machine-learning-introduction">Machine Learning Specialization &#8211; Stanford</a></li>
</ul>



<h2 class="wp-block-heading">Top Paying Companies For a Data Scientist in United States</h2>



Data Scientist is one of the most in-demand job roles in the United States. In 2022, Glassdoor named Data Scientist as the third highest-paying job in the US. Below we have mentioned the Top 10 highest-paying companies for Data Scientists in the United States:&nbsp;



<figure class="wp-block-table"><table><tbody><tr><td>Company&nbsp;</td><td>Compensation&nbsp;</td></tr><tr><td>Advent International</td><td>$780,417</td></tr><tr><td>Hudson River Trading</td><td>$600,000</td></tr><tr><td>Netflix</td><td>$500,000</td></tr><tr><td>Coupang</td><td>$455,000</td></tr><tr><td>Airbnb</td><td>$407,000</td></tr><tr><td>Coinbase</td><td>$401,000</td></tr><tr><td>Snap</td><td>$400,000</td></tr><tr><td>Jump Trading</td><td>$387,500</td></tr><tr><td>Stitch Fix</td><td>$382,000</td></tr><tr><td>Instacart</td><td>$375,200</td></tr></tbody></table></figure>



Source: <a href="https://www.dice.com/career-advice/which-companies-pay-data-scientists-the-most">Dice.com</a>



<h2 class="wp-block-heading">FAQ&#8217;s</h2>



<h3 class="wp-block-heading">Who is a Data Scientist?</h3>



A Data Scientist is an analytical expert who collects, analyzes, and interprets data and is responsible for solving complex issues and helping drive decision-making in an organization or business.&nbsp;



<h3 class="wp-block-heading">Senior data scientist salary in the US in 2024</h3>



The Average Annual Salary for a Senior Data Scientist in the United States is $2,16,519 according to <a href="https://www.glassdoor.co.in/Salaries/us-senior-data-scientist-salary-SRCH_IL.0,2_IN1_KO3,24.htm">Glassdoor</a>.&nbsp;



<h3 class="wp-block-heading">Highest paying data scientist salary in U.S. 2024</h3>



The Highest-paying data scientist salary in the U.S. in 2024 is $1,89,245 per year according to <a href="https://www.glassdoor.co.in/Salaries/us-data-scientist-salary-SRCH_IL.0,2_IN1_KO3,17.htm#:~:text=What%20is%20the%20highest%20salary,is%20%241%2C31%2C172%20per%20year.">Glassdoor</a>.&nbsp;



<h3 class="wp-block-heading">Entry-level data scientist salary in the US in 2024</h3>



The average salary for an Entry-Level or Beginner-level Data Scientist in the United States in 2024 is $1,22,833 per year.&nbsp;

Data Scientist Salary in US 2024

“I came across the term “data scientist” a few years ago when somebody (from the valley, of course) asked me, “So are you a data scientist?” And my immediate answer was, “No, I am not a scientist.” Although I already had spent a decade in the data space, driving business impact through analytics, I did not see myself as a scientist…”



My answer today is not all that different. To me, scientist conjures up an image of fully antiseptic lab environment, white lab coats and pipettes. Marry data to that term, and it still sounds very white lab coat-ish, with a definite R&amp;D bent and with graphs running on a big screen monitor. A few other data science leaders in the Silicon Valley, like <a href="https://www.linkedin.com/today/post/article/20130215205002-50510-the-data-scientific-method">Daniel from LinkedIn</a>, have similar interpretations of the term data scientist.



But words are just words. What is the big deal? Actually, there is a big deal in the middle of all this. I frequently keynote at analytics conferences, and one of the things I hear a lot from the data scientist/analytics professionals is that many of them are producing a lot of analytics insights using state-of-the art-algorithms, BUT nobody in the organization really cares! This I have heard from data scientists, spanning the breadth of apparently “data-driven” Fortune 1000 companies including LinkedIn, Facebook, Visa, eBay, Apple, Oracle, and SAP, to name a few.



So what is going on? On one hand, we see reports about the massive dearth of data scientist (Source: <a href="http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation">McKinsey’s Big Data report</a>). On the other hand, the work they are doing is hardly being leveraged. Why? The reason is what I call “the MISSING green track.”



Let me explain. Although the word “analytics” conjures up the image of graphs, data, numbers and complex algorithms, it is only part of the story.&nbsp; At Aryng, we use a structured approach to analytics (see Figure 1) that includes a green track and a blue track. When analytics is done right, the blue track, the process of getting insights from the data, needs to happen in parallel with the green track, the process that drives decision making and impact in the organization.



The green track is all about what one needs to do to bridge the gap to the business, to understand the business priorities, to work within business constraints, to bring along the key stakeholders and to make the right handshakes at the right time so when one is ready with insights from the data, the stakeholders are ready and poised to make decisions and take actions based on those insights, thus driving impact through data.



<figure class="wp-block-image"><img decoding="async" src="https://lh7-us.googleusercontent.com/Qt-e5cRu10vnh5NURMI3hW3AYbc-Z3rSjqQYbJ-RJ-hIG0tl2zzzor-Pz9n__rECeK6q5qmjQ5rFsR3cxiLQTu-PiwU2NTJKYDYOb5YhIC9RLFTSk609g9gvxbP8R8ggpjPiEvITi8Ngkn2tx5RwYuw" alt=""/></figure>



Figure 1: Aryng’s BADIR Methodology



Today, data scientists are well trained, or perhaps over trained, on the blue track; but the green track often eludes them, mostly because it is not taught as a science in the universities. Nevertheless, green track is a science and is completely learnable (check out Aryng’s <a href="http://www.aryng.com/landing/DTD-Week-Analytics-Workshop-for-Product-Marketing-Apr-15-19.html?utm_source=beye&amp;utm_medium=Partner&amp;utm_campaign=Art">Data-to-Decisions Week</a> – a week for complete hands-on education on analytics and testing – with green and blue tracks). Unless an insight sees the light of the day by way of getting transformed into a decision, it is a complete waste of resources and time.



Unless analytics drives business impact, it is not analytics. It is just statistics; it is just data science. That brings me back to the term data scientist, which sounds academic and all too blue track to me. To me, data science + decision science = analytics. But again, words are just words. As long as both green track and blue track processes are followed, data will lend itself to decisions – call it data science or call it analytics.



<a href="http://www.b-eye-network.com/view/16873">Link to original article </a>_____________________________________________ Notes from the author:



For more details on blue and green tracks, which are part of BADIR – the 5-step process from “data to decisions,” feel free to <a href="http://www.aryng.com/analytics-whitepaper.html#badir?utm_source=beye&amp;utm_medium=Partner&amp;utm_campaign=Art">download this white paper on BADIR</a>. And if we can help your organization in the journey towards being data-driven, with green track married to blue track, feel free to <a href="http://www.aryng.com/contact.html?utm_source=beye&amp;utm_medium=Partner&amp;utm_campaign=Art">contact us</a>.



If you are an <a href="http://searchdatamanagement.techtarget.com/definition/business-intelligence">business intelligence</a> (BI) executive frustrated with low ROI from your data investment, in spite of a large data science and BI&nbsp; team, then I invite you to join us for a half-day <a href="http://www.aryng.com/landing/DTD101-Data-Driven-Executive-Analytics-Workshop-Apr-5.html?utm_source=beye&amp;utm_medium=Partner&amp;utm_campaign=Art">Data-Driven Executive Workshop</a> on April 5th, 2013, in Santa Clara, CA. This workshop will guide you on what is analytics (and what is not analytics), how organizations such as yours leverage data as an asset, how to measure your organization’s analytics maturity and then how to transition your organization towards higher analytics maturity, such that all the decision makers in the organization, irrespective of where they sit, have the right tools to make smarter, data-driven decisions.



If you are a <a href="http://searchdatamanagement.techtarget.com/definition/business-intelligence">BI</a> manager and want to deliver more than just data to your stakeholders and want to learn the green as well as blue track, then I invite you to attend our <a href="http://www.aryng.com/landing/DTD-Week-Analytics-Workshop-for-Product-Marketing-Apr-15-19.html?utm_source=beye&amp;utm_medium=Partner&amp;utm_campaign=Art">Data-to-Decisions Week</a>&nbsp; alongside product and marketing managers, where you can learn how to drive decisions using insights from analytics and testing.

Data Science Vs. Data Analytics: a Refresher on the Differences

Hi Everyone,&nbsp; I’ve seen a few great lists lately of Open Source Tools for Big Data.&nbsp; So I thought I would share the best of what I’ve seen and use a little crowdsourcing from readers to see what’s missing and create a UPDATED master list.



<h3 class="wp-block-heading">Here is a very helpful landscape style visual of the Open Source Tools from the blog:  Big Data Start-ups</h3>



<figure class="wp-block-image"><img decoding="async" src="https://lh7-us.googleusercontent.com/vS9TfVbZMy7iqo-XUMXiEPITdHAEsBYvRJsXHeICIeK_IBu7RYfGeIVmSY2u4FWyrUPsJg1DX7tpWqdbBMZpY6P8lfX8UBmYsXpMMdj9zlrkZpC4hO1GzjZVxxcoHU4Yzkf1yCVwU2R2_zuIg2zBLg4" alt="Open Source Tools"/></figure>



Next we have another list that looks pretty solid from By Fari Payandeh at <a href="http://bigdatastudio.com/2013/09/01/the-best-of-open-source-for-big-data/">Blog: Big Data Studio</a>



Fari writes: It was not easy to select a few out of many Open Source projects. My objective was to choose the ones that fit Big Data’s needs most. What has changed in the world of Open Source is that the big players have become stakeholders; IBM’s alliance with Cloud Foundry, Microsoft providing a development platform for Hadoop, Dell’s Open Stack-Powered Cloud Solution, VMware and EMC partnering on Cloud, Oracle releasing its NoSql database as Open Source.



<figure class="wp-block-image"><img decoding="async" src="https://lh7-us.googleusercontent.com/7sZLzBPLaZIGcCAQLryW4FM4Ob3NwGhp5i9oY_cuieyOovCuoIzIrMZt_fYW8g3yRl9pBcyGlw7n_ZkT8il83PjfYJz5cLnKNkuc5nlmBMIHLIpcpb2a3QAVmJGc3vw4yIPZv-2zZOJOPsM0cHUWDe4" alt="bigdata-opensource-final5"/></figure>



The Final List comes from Datamation.com:



<h3 class="wp-block-heading">50 Top Open Source Tools for Big Data</h3>



1. <a href="http://hadoop.apache.org/">Hadoop</a>



You simply can’t talk about big data without mentioning Hadoop. The Apache distributed data processing software is so pervasive that often the terms “Hadoop” and “big data” are used synonymously. The Apache Foundation also sponsors a number of related projects that extend the capabilities of Hadoop, and many of them are mentioned below. In addition, numerous vendors offer supported versions of Hadoop and related technologies. Operating System: Windows, Linux, OS X.



2. <a href="http://hadoop.apache.org/mapreduce/">MapReduce</a>



Originally developed by Google, the MapReduce website describe it as “a programming model and software framework for writing applications that rapidly process vast amounts of data in parallel on large clusters of compute nodes.” It’s used by Hadoop, as well as many other data processing applications. Operating System: OS Independent.



3. <a href="http://www.gridgain.com/">GridGain</a>



GridGrain offers an alternative to Hadoop’s MapReduce that is compatible with the Hadoop Distributed File System. It offers in-memory processing for fast analysis of real-time data. You can download the open source version from GitHub or purchase a commercially supported version from the link above. Operating System: Windows, Linux, OS X.



4. <a href="http://hpccsystems.com/">HPCC</a>



Developed by LexisNexis Risk Solutions, HPCC is short for “high performance computing cluster.” It claims to offer superior performance to Hadoop. Both free community versions and paid enterprise versions are available. Operating System: Linux.



5. <a href="https://github.com/nathanmarz/storm#readme">Storm</a>



Now owned by Twitter, Storm offers distributed real-time computation capabilities and is often described as the “Hadoop of realtime.” It’s highly scalable, robust, fault-tolerant and works with nearly all programming languages. Operating System: Linux.



<h3 class="wp-block-heading">Databases/Data Warehouses</h3>



6. <a href="http://cassandra.apache.org/">Cassandra</a>



Originally developed by Facebook, this NoSQL database is now managed by the Apache Foundation. It’s used by many organizations with large, active datasets, including Netflix, Twitter, Urban Airship, Constant Contact, Reddit, Cisco and Digg. Commercial support and services are available through <a href="http://wiki.apache.org/cassandra/ThirdPartySupport">third-party vendors.</a> Operating System: OS Independent.



7. <a href="http://hbase.apache.org/">HBase</a>



Another Apache project, HBase is the non-relational data store for Hadoop. Features include linear and modular scalability, strictly consistent reads and writes, automatic failover support and much more. Operating System: OS Independent.



8. <a href="http://www.mongodb.org/">MongoDB</a>



MongoDB was designed to support humongous databases. It’s a NoSQL database with document-oriented storage, full index support, replication and high availability, and more. Commercial support is available through <a href="https://web.archive.org/web/20160216225100/http://www.10gen.com/subscription">10gen</a>. Operating system: Windows, Linux, OS X, Solaris.



9. <a href="http://neo4j.org/">Neo4j</a>



The “world’s leading graph database,” Neo4j boasts performance improvements up to 1000x or more versus relational databases. Interested organizations can purchase advanced or enterprise versions from<a href="https://web.archive.org/web/20160216225100/http://neotechnology.com/">Neo Technology</a>. Operating System: Windows, Linux.



10. <a href="http://couchdb.apache.org/">CouchDB</a>



Designed for the Web, CouchDB stores data in JSON documents that you can access via the Web or or query using JavaScript. It offers distributed scaling with fault-tolerant storage. Operating system: Windows, Linux, OS X, Android.



11. <a href="http://www.orientdb.org/index.htm">OrientDB</a>



This NoSQL database can store up to 150,000 documents per second and can load graphs in just milliseconds. It combines the flexibility of document databases with the power of graph databases, while supporting features such as ACID transactions, fast indexes, native and SQL queries, and JSON import and export. Operating system: OS Independent.



12. <a href="http://code.google.com/p/terrastore/">Terrastore</a>



Based on Terracotta, Terrastore boasts “advanced scalability and elasticity features without sacrificing consistency.” It supports custom data partitioning, event processing, push-down predicates, range queries, map/reduce querying and processing and server-side update functions. Operating System: OS Independent.



13. <a href="https://github.com/twitter/flockdb">FlockDB</a>



Best known as Twitter’s database, FlockDB was designed to store social graphs (i.e., who is following whom and who is blocking whom). It offers horizontal scaling and very fast reads and writes. Operating System: OS Independent.



14. <a href="http://hibari.github.com/hibari-doc/">Hibari</a>



Used by many telecom companies, Hibari is a key-value, big data store with strong consistency, high availability and fast performance. Support is available through <a href="http://www.geminimobile.com/">Gemini Mobile</a>. Operating System: OS Independent.



15. <a href="http://wiki.basho.com/Riak.html">Riak</a>



Riak humbly claims to be “the most powerful open-source, distributed database you’ll ever put into production.” Users include Comcast, Yammer, Voxer, Boeing, SEOMoz, Joyent, Kiip.me, DotCloud, Formspring, the Danish Government and many others. Operating System: Linux, OS X.



16. <a href="http://hypertable.org/">Hypertable</a>



This NoSQL database offers efficiency and fast performance that result in cost savings versus similar databases. The code is 100 percent open source, but paid support is available. Operating System: Linux, OS X.



<a href="http://www.datamation.com/data-center/50-top-open-source-tools-for-big-data-2.html">Click Here to See #17 to&nbsp; 50</a>

50+ Open Source Tools for Big Data

Michael Berry is taking a stand against the big data hype. More data, said the analytics director for travel website TripAdvisor, doesn’t always mean better business results. Case in point: big data and <a href="http://searchcrm.techtarget.com/definition/predictive-analytics">predictive analytics</a>.



“Many predictive analytics applications turn out not to need all of the data,” Berry said during his keynote talk at Predictive Analytics World. So the real task for <a href="http://searchcio.techtarget.com/video/HMS-CIO-Were-big-data-scientists-not-big-data-practitioners">data scientists</a> et al. isn’t figuring out how to analyze all the available data; instead, it’s figuring out how much data it takes to see something worth noting. The bad news?



“There’s not a simple answer to that question,” Berry said.



However, testing the predictive model’s performance by incrementally adding more data can shed light on when enough is enough. For example, when Berry wanted to know the standard bid by travel agency partners for a specific hotel and specific customer, he began computing averages: The first two bids compared to the first three bids compared to the first four bids and so on until he hit a steady plateau at 100,000. If he kept going to 200,000 bids, the average would change, sure, but not enough to matter.



“That’s the way data tends to be: When you have enough of it, having more doesn’t really make much difference,” he said.



So if more data doesn’t matter, what does? “So many things,” Berry said. Working with <a href="http://searchdatamanagement.techtarget.com/feature/Business-data-quality-measures-need-to-reach-a-higher-plane">clean data</a>, doing unbiased sampling, hiring staff dedicated to data quality and creative thinking.



That’s right, there’s a big place in predictive analyses for those <a href="http://searchbusinessanalytics.techtarget.com/news/2240181721/Data-science-team-building-101-Cross-functional-talent-key-to-success">soft data science skills</a>, such as figuring out what variables can make the model stronger or what new patterns might be discovered by combining different kinds of data together. Examples?



“Someone had to think of the idea of wind chill factor,” Berry said, before combining actual temperature and wind speed to reveal a new data point: What the weather will actually feel like.



<h3 class="wp-block-heading">More big data delusions</h3>



Berry wasn’t the only presenter who badmouthed the state of <a href="http://searchbusinessanalytics.techtarget.com/news/2240100743/Predictive-analytics-and-big-data-The-good-the-bad-and-the-ugly">big data and predictive analytics</a>. Karl Rexer, founder of the consulting firm Rexer Analytics, went so far as to suggest that the current crop of data scientists suffers from a bit of delusional thinking.



In his 2013 Data Miner Survey, respondents indicated that the size of data sets is getting bigger. But when Rexer asked them how many records are in a typical data set they use for analyses, “We get the same answer we got in 2007,” he said.



That’s not to say big data is a farce or to give short shrift to the <a href="http://searchcio.techtarget.com/news/2240178592/Hadoop-framework-breathes-new-life-into-Ancestrycom-legacy-tools">interesting work some are doing</a> in this space, he said. “But for the typical analytic predictive modeling/data mining/whatever-you-want-to-call-it project, I would say the overall sample size used for those data mining projects is not increasing.”



<h3 class="wp-block-heading">Name that acronym</h3>



<a href="http://searchcio.techtarget.com/news/2240186181/Learning-native-tongue-of-your-business-is-key-to-reviving-role-of-IT">Translating the language</a> of analytics into something the business can understand is challenging. One way Paychex, a payroll, human resources and benefits service provider, deals with the language barrier is by, well, using language the business suggests.



<h4 class="wp-block-heading">PREVIOUSLY ON THE DATA MILL</h4>



‘Cookie stuffing&#8217;: A <a href="http://searchcio.techtarget.com/opinion/Cookie-stuffing-A-data-scientist-takes-on-seamy-side-of-online-ads">data scientist</a> tackles sleazy side of online ads



Do businesses have the patience for good <a href="http://searchcio.techtarget.com/opinion/Does-business-have-the-patience-for-data-science">data science</a>?



How semi-structured data drives <a href="http://searchcio.techtarget.com/opinion/Semi-structured-data-is-king-of-LinkedIns-recommendation-engine">LinkedIn analytics</a>



“When we build a model, we’ll run a naming contest for the users,” Tom Kern, a risk-modeling analyst for Paychex, said at Predictive Analytics World. Kern’s department will send users an email with a short description about the model and suggest a couple of words to get them started. The users have to come up with “an acronymic name,” he said. So there’s SAM, the sales anticipation model, and TIM, the territory identification and mapping model. “Still working on a TOM,” he quipped.



If the business users’ suggestion is chosen, they get a gift card, and the company gets its users, such as the sales staff, to think about what the predictive model really does.



<h3 class="wp-block-heading">The Tide turns</h3>



The Procter &amp; Gamble Co., one of the biggest consumer goods retailers in the world, announced plans to release a lower-priced version of the laundry detergent Tide in an effort to attract mid-tier customers. Bold move or bad decision?



“One of the big concerns? If you launch products like these, not only are you going to attract folks you currently don’t have, but you’re going to encourage consumers to trade down,” said Shel Smith, partner and founder of <a href="http://searchcio.techtarget.com/news/2240204410/Zipcar-CMO-taps-data-driven-marketing-to-personalize-the-business">marketing analytics</a> firm Twenty-Ten Inc.



That’s especially true after a recession that forced so many consumers to be price-conscious. But Smith, for one, has faith in P&amp;G’s strategy. He believes the company will use predictive modeling, lots of data and highly targeted marketing to make new customers but keep the old.



“There’s something they know that we don’t in terms of their ability to maintain the existing franchise, but go after new consumers by being far more surgical,” he said.

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