
<p>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. </p>



<p>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>. </p>



<p>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. </p>



<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"><strong>How much do Data Scientists make?</strong></h2>



<p><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. </p>



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



<figure class="wp-block-table"><table><tbody><tr><td><strong>Indeed</strong></td><td><strong>Zippia</strong></td><td><strong>Glassdoor</strong><strong></strong></td><td><strong>US BLS</strong><strong></strong></td><td><strong>Payscale</strong></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>



<p>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. </p>



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



<p>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. </p>



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



<figure class="wp-block-table"><table><tbody><tr><td><strong>Experience&nbsp;</strong></td><td><strong>Salary&nbsp;</strong></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>



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



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



<p>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. </p>



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



<figure class="wp-block-table"><table><tbody><tr><td><strong>Location&nbsp;</strong></td><td><strong>Average base salary&nbsp;</strong></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>



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



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



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



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



<figure class="wp-block-table"><table><tbody><tr><td><strong>Industry&nbsp;</strong></td><td><strong>Salary&nbsp;</strong></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"><strong>Job outlook for data scientists in the U.S</strong></h3>



<p>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;</p>



<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>



<p>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;</p>



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



<p>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;</p>



<p>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.</p>



<p><strong>1. Associate Degree in Data Science</strong></p>



<p>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;</p>



<p>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.</p>



<p><strong>2. Bachelor&#8217;s Degree in Data Science</strong></p>



<p>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;</p>



<p><strong>3. Master&#8217;s Degree in Data Science</strong></p>



<p>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;</p>



<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>



<p><strong>4. Doctorate in Data Science</strong></p>



<p>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;</p>



<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"><strong>How to increase your data scientist salary</strong></h2>



<p>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;</p>



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



<p>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:</p>



<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"><strong>IBM Data Science</strong></h3>



<p>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;</p>



<p>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;</p>



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



<p>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;</p>



<p>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:</p>



<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"><strong>Top Paying Companies For a Data Scientist in United States</strong></h2>



<p>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;</p>



<figure class="wp-block-table"><table><tbody><tr><td><strong>Company&nbsp;</strong></td><td><strong>Compensation&nbsp;</strong></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>



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



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



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



<p>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;</p>



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



<p>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;</p>



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



<p>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;</p>



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



<p>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;</p>


Data Scientist Salary in US 2024


<p>“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…”</p>



<p>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.</p>



<p>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.</p>



<p>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.”</p>



<p>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.</p>



<p>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.</p>



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



<p><strong>Figure 1: </strong>Aryng’s BADIR Methodology</p>



<p>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.</p>



<p>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.</p>



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



<p>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>.</p>



<p>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.</p>



<p>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.</p>


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


<p>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.</p>



<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>



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



<p>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.</p>



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



<p>The Final List comes from Datamation.com:</p>



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



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



<p>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.</p>



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



<p>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.</p>



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



<p>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.</p>



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



<p>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.</p>



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



<p>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.</p>



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



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



<p>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.</p>



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



<p>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.</p>



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



<p>MongoDB was designed to support hu<strong>mongo</strong>us 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.</p>



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



<p>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.</p>



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



<p>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.</p>



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



<p>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.</p>



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



<p>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.</p>



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



<p>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.</p>



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



<p>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.</p>



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



<p>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.</p>



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



<p>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.</p>



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


50+ Open Source Tools for Big Data


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



<p><strong>Will it be an official Masters? No, but an official Masters is not always what is needed</strong>, 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;</p>



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



<p>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.</p>



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



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



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



<p>We need more Data Scientists.</p>



<p><em>…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.</em></p>



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



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



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



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



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



<p><strong><em>Academic credentials are important but not necessary for high-quality data science.</em></strong><em> 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.</em></p>



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



<p><em>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 </em><strong><em>autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.</em></strong></p>


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


<p>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.</p>



<p>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.</p>



<p>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.</p>



<p>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.</p>



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



<p>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.</p>



<p>Here are the case studies:</p>



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



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



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



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



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



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



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



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



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



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



<li></li>
</ol>


Case Studies | 10 Big Data Projects in the Insurance Industry


<p>The demand for skilled data scientists and data engineers remains <a href="http://articles.economictimes.indiatimes.com/2015-01-22/news/58343917_1_big-data-analytics-professionals-data-science">strong in 2015</a>.&nbsp; Big data analysis is becoming a necessity for companies to prosper in all industries from <a href="http://blogs-images.forbes.com/louiscolumbus/files/2014/12/top-20-industries-hiring.jpg">agriculture to finance</a>. Large international companies to new startups have been reaching out to bootcamps, immersion, and fellowship programs for qualified professionals. The requirements for enrollment are more stringent than those of <a href="http://www.skilledup.com/articles/the-ultimate-guide-to-coding-bootcamps-the-exhaustive-list/">coding bootcamps</a> but, students who make it through will be positioned for one of the <a href="http://rht.mediaroom.com/index.php?s=533&amp;item=72">highest paying jobs</a> in the tech field. Take a look at what these data science and engineering schools have to offer.</p>



<h2 class="wp-block-heading"><a href="http://insightdataengineering.com/">Insight Data Engineering</a> &amp; <a href="http://insightdatascience.com/">Data Science Fellows</a> Silicon Valley, CA, NYC<img loading="lazy" decoding="async" alt="Insight-logo" src="https://lh7-us.googleusercontent.com/FKUHAhr9snwDzrRhUDT3t3m2dXfS4HBpCxS4epaMImTGVQHIy5IbRrQ6TSrv4ILOD9CSwUA9FD3xcuk9uR5Xkj6VGhVBGWjhkvJBOg-EkPgwNN1Kp2ssj0XMqlPaInnfzJLPjgOpNBEnyLhhqQTSkGU" width="624" height="337"></h2>



<p>Insight has two separate six-week programs designed to bridge the gap between traditional education and a lucrative career.&nbsp; Both are project-based learning programs with no traditional lecture classes. The data engineering program is for those interested in building infrastructures with large code bases. The data science program is for those who want to acquire experience in data analysis, experimental design, or machine learning. Upon graduation, graduates get positioned directly into partnered companies of Insight.</p>



<figure class="wp-block-table"><table><tbody><tr><td>FORMAT</td><td>In-person</td></tr><tr><td>COST</td><td>Free</td></tr><tr><td>REQUIRED EXPERIENCE</td><td>Completed or are approaching completion of a Ph.D.</td></tr><tr><td>LANGUAGES</td><td>Python, R, Git, Flask, Javascript, MySQL, Hadoop, Hive</td></tr><tr><td>CLASS SIZE</td><td>n/a</td></tr><tr><td>CLASS LENGTH</td><td>Six weeks</td></tr><tr><td>HOURS/WEEK</td><td>Full-Time</td></tr><tr><td>SESSIONS/YEAR</td><td>Two</td></tr><tr><td>APPLICATION</td><td>Standard application</td></tr><tr><td>JOB ASSISTANCE</td><td>Yes</td></tr><tr><td>TUITION REIMBURSEMENT</td><td>n/a</td></tr><tr><td>FINANCIAL AID</td><td>Need-based travel and living expense scholarships available</td></tr><tr><td>PAYMENT PLAN</td><td>n/a</td></tr><tr><td>SELECTIVE</td><td>Yes</td></tr><tr><td>DESCRIPTION</td><td>Data Science Fellowship</td></tr><tr><td>CONTACT</td><td><a href="mailto:info@insightdatascience.com%20">info@insightdatascience.com&nbsp;</a></td></tr></tbody></table></figure>



<h2 class="wp-block-heading"><a href="http://www.thisismetis.com/data-science">Metis</a> NYC<img loading="lazy" decoding="async" alt="Metis-logo" src="https://lh7-us.googleusercontent.com/z6Cz7zFvTWZ-EvHFZFIwgVpmLxfSer-ffiZ52tOb44i_U2GT3S1HyE25U0uhq1-pi8F3suRmzHyp1tr7nDHP6IM9myMbnIXXa-xt4GACyoYeNlk-jmjNvbxVrvM3Ugy5fFUPgnokWWwr4Rs8EWXFn7U" width="624" height="253"></h2>



<p>In 2015, Kaplan’s Metis bootcamp has decided to focus all its expertise into it’s data science bootcamp. Its course is designed to teach someone to think and act like a true data scientist. Students are taught to discover the questions that will deliver the greatest value from their data. The coursework provides a very strong foundation amongst many essential tools and algorithms. At the end of the 12-week course Metis holds a career day visited by over 30 companies. That is more companies than the average amount of students enrolled. Metis holds the only data science bootcamp accredited by the Accrediting Council for Continuing Education &amp; Training (ACCET), a U.S. Department of Education nationally recognized agency.</p>



<figure class="wp-block-table"><table><tbody><tr><td>FORMAT</td><td>In-person</td></tr><tr><td>COST</td><td>$14,000</td></tr><tr><td>REQUIRED EXPERIENCE</td><td>Applicants must have some previous experience programming (writing code) and studying or using statistics.</td></tr><tr><td>LANGUAGES</td><td>Python (Scikit-learn, Statsmodels, Numpy, Pandas, Matplotlib, Flask), Javascript (d3.js), and other software (Git, Hadoop, Hive, SQL, Mongo)</td></tr><tr><td>CLASS SIZE</td><td>18-24</td></tr><tr><td>CLASS LENGTH</td><td>12 weeks</td></tr><tr><td>HOURS/WEEK</td><td>Full-time</td></tr><tr><td>SESSIONS/YEAR</td><td>Four</td></tr><tr><td>APPLICATION</td><td>Standard application with self assessment + coding challenge + interview</td></tr><tr><td>JOB ASSISTANCE</td><td>Provide coaching, technical interviewing workshops, salary negotiation support, and access to employers. Career day, students present 1 of 5 different projects to 25-30 different employers</td></tr><tr><td>TUITION REIMBURSEMENT</td><td>No</td></tr><tr><td>FINANCIAL AID</td><td>$2,000 scholarship for women, underrepresented minority groups, and veterans or members of the U.S. military.</td></tr><tr><td>PAYMENT PLAN</td><td>Available. Financing partners available.</td></tr><tr><td>SELECTIVE</td><td>Yes</td></tr><tr><td>DESCRIPTION</td><td>Data Science Bootcamp</td></tr><tr><td>CONTACT</td><td><a href="http://www.thisismetis.com/data-science/?utm_source=datasciencereport&amp;utm_medium=website_ad&amp;utm_campaign=ds">Contact Form</a></td></tr></tbody></table></figure>



<p><a href="http://www.skilledup.com/articles/list-data-science-bootcamps/"><strong>VIEW COMPLETE LIST HERE (SkilledUp.com)&nbsp;</strong></a></p>



<p><strong>About SkilledUp:</strong></p>



<p>SkilledUp curates the abundance of online and alternative education sources to help build a bridge between education and actual job skills. From technology and business to general education and creative arts—our catalogue of the world’s best online courses is growing daily.</p>



<p><em>“We love being able to provide you with alternative educational opportunities. These courses can be fun, challenging, interesting and sometimes free—but most importantly, they can provide true return on your investment of both time and money.” – Nick Gidwani, SkilledUp President</em></p>


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