So my #IBMInsight experience is coming to a close, my brain is full, my feet are tired but my analytical spidey senses are tingling and my hope for the future of #Bigdata analytics is restored! I’m not sure which one was my favorite, each time I left a session or a keynote I thought, this one was the best EVER but then I would attend the next one and come out thinking the same thing WOW!!!

So, 1st I have to say hats off to the IBM marketing team for throwing a KICK ASS event and then I want to talk about something that is near and dear to my heart – Watson Oncology – after I wiped away the tears, I thought about the loved ones I have lost to cancer and how this technology could have made a difference but the time wasn’t right THEN…. Fortunately, the time IS right now! A little more about Watson oncology:

The challenge

According to one expert, only 20 percent of the knowledge physicians use to diagnose and treat patients today is evidence based. Which means that one in five diagnoses is incorrect or incomplete.

And consider that the amount of medical information available is doubling every five years and that much of this data is unstructured. Physicians simply don’t have time to read every journal that can help them keep up to date with the latest advances. Given the growing complexity of medical decision making, how can healthcare providers address these problems?

Solution

Watson has the potential to transform healthcare research, how medical students learn and how payments are processed.

Physicians can use Watson to assist in diagnosing and treating patients by having it analyze large amounts of unstructured text and develop hypotheses based on that analysis.

First, the physician might describe symptoms and other related factors to the system. Watson can then identify the key pieces of information and mine the patient’s data to find relevant facts about family history, current medications and other existing conditions. It combines this information with current findings from tests, and then forms and tests hypotheses by examining a variety of data sources—treatment guidelines, electronic medical record data and doctors’ and nurses’ notes, as well as peer-reviewed research and clinical studies. From here, Watson can provide potential treatment options and its confidence rating for each suggestion.

To learn even more – please check out http://www.ibm.com/smarterplanet/us/en/ibmwatson/implement-watson.html

 

The 2nd BIG Announcement was from IBM and Twitter -> A partnership that will take us all to the next level in social analytics and targets/trends…. The example Twitter’s own Chris Moody gave was a business that sell fryers for restaurants – they wanted to talk to Twitter about doing business, the guys from Twitter were like, not sure what we can do for you but the business guy said, oh yes, what about Tweets about soggy fries? Soggy fries…. Well think about it, when someone complains about soggy fries that correlates to the equipment not working properly, duh right? So it was a huge surprise when IBM’s Ginni Rometty & Dick Costolo announced the landmark alliance between @ibm and @twitter #ibminsight #IBMandTwitter

 

The collaboration will focus on three areas:

Integration of Twitter data with IBM analytics services on the cloud: IBM plans to offer Twitter data as part of select cloud-based services, including IBM Watson Analytics, a new cognitive service in the palm of your hand that brings intuitive visualization and predictive capabilities to business users; and a cloud-based data refinery service that enables application developers to embed data services in applications. Entrepreneurs and software developers will also be able to integrate Twitter data into new cloud services they are building with IBM’s Watson Developer Cloud or IBM Bluemix platform-as-a-service.

New data-intensive capabilities for the enterprise: IBM and Twitter will deliver a set of enterprise applications to help improve business decisions across industries and professions. The first joint solution will integrate Twitter data with IBM ExperienceOne customer engagement solutions, allowing sales, marketing, and customer service professionals to map sentiment and behavior to better engage and support their customers.

Specialized enterprise consulting: IBM Global Business Services professionals will have access to Twitter data to enrich consulting services for clients across business. Additionally, IBM and Twitter will collaborate to develop unique solutions for specific industries such as banking, consumer products, retail, and travel and transportation. The partnership will draw upon the skills of tens of thousands of IBM Global Business Services consultants and application professionals including consultants from the industry’s only integrated Strategy and Analytics practice, and IBM Interactive Experience, the world’s largest digital agency.

“Twitter provides a powerful new lens through which to look at the world – as both a platform for hundreds of millions of consumers and business professionals, and as a synthesizer of trends,” said Ginni Rometty, IBM Chairman, President and CEO. “This partnership, drawing on IBM’s leading cloud-based analytics platform, will help clients enrich business decisions with an entirely new class of data. This is the latest example of how IBM is reimagining work.”

 

To learn even more – please check out  http://www-03.ibm.com/press/us/en/pressrelease/45265.wss

 

My 3rd exciting event was the No Doubt concert, now normally I’m not the concert kinda girl, my OCD and germ issues usually keep me away but NOT THIS time! Rocken with “The #IBMInsight band” before the concert with Kevin, Lillian, Bryan and a few others (see our Twitter pics and feeds using the hashtag #IBMInsight) then the best concert EVER…. Side note, I was having lunch – I looked over and there was No Doubt! That made my event!

IMG_0105

 

So to wrap things up, if you are thinking about attending a big data event for 2014, you need to check out IBM Insight 2015, you won’t regret and you won’t walk away feeling like you wasted your time (like a lot of these events) – I was never under whelmed with the speakers and I leave here knowing that the future is indeed bright and data CAN make a difference!

 

IMG_0108

I was so excited to receive this email from the IBM Insight Committee ->  “You’ve been selected as part of a small set of key influencers to lead the conversation around how data and analytics are driving decisions, fueling processes, and informing interactions.  Along with conference host Jake Porway (@jakeporway), you’ll be joining your peers for the most exclusive “behind the ropes” look at how the transformative role of data is inspiring a global network of individuals and societies to discover new possibilities.”

It’s not every day you are invited to participate in the IBM Insight Event so it was quite an honor, the most exciting part is being part of “IMO” the biggest data events of the year! Just looking at the list of big data and data scientist talent gets me all giddy (yes, I’m a data nerd, LOL)

The Insight curriculum is aligned to three programs that encompass 15+ tracks which help frame sessions and content throughout the event:
#1 We get to enjoy and be witness to some of the latest advancements in analytics strategies, from business intelligence, data discovery and predictive analytics to performance management and risk analytics – #2 Really excited about the way they set this up – learning how to manage a growing diversity of incoming information and extract value for deep insight, enhanced customer experience and business growth. – and lastly (but a biggie in my opinion) #3 We will explore how big data is used to build a modern information management architecture while becoming more agile, efficient and competitive. EXCITING

For me, my interest these days are in “wearable electronics”  – think about the huge amounts of data this is going to create and the implication of its uses. Being that my parents are older, I worry about their health and one way “wearable electronics” can assist is in heart rate analysis – I am super excited for a product to come out so they can monitor their heartbeat and maybe even have it call 911 if needed, wishing, I think not! But, of course everything has its down side as well, my second thought was….. security…. if and when this is completely integrated into our lives will someone be able to hack this info and sell it to doctors and hospitals/insurance companies….etc… Oy’ the horror but welcome to the 21st century, where ever there is good, there is also bad! In my opinion if “wearable technology” can wake someone with “sleep apnea” or save a baby from crib death or yes, even get an ambulance to my house really fast when needed, if we can save one life, it is worth it! What a great time to be alive. See you in VEGAS, Big Data ROCKS :)

Einstein

I was reading an article about the purchase of Mojang, entitled “Entrepreneur Who Just Became A Billionaire Writes Extraordinary Letter About Leaving His Company To Save His Sanity” and this line caught my eye -> “I’m not an entrepreneur. I’m not a CEO. I’m a nerdy computer programmer who likes to have opinions on Twitter.” –  and then it hit me, I’m not an entrepreneur. I’m not a CEO. I’m a data nerd who loves to help companies glean insight from their data. I’m no good at sales, hell, I give away more that I sell and I give breaks to companies that can’t afford me but give them 150%. BUT as some of you read, I just let my biggest client go, why?

I quit the “corporate” world in 2011 to open my own business, I was tired of sitting in meetings thinking to myself “jezzzz, just pull it out already and see whose is bigger” – Time waste and a mental drain, I felt, anything I said would have to be repeated by a man for it to be taken seriously. So, I took the plunge with a positive attitude and 15 years of data experience behind me, how could I loss? The clients came and went, data paraded in front of me endlessly as I took on more and more with less and less companies having the hourly fee I deserved, I guest blogged for free, I wrote white papers and shared at no cost. Then last week, I realized people were taking as much as I would give but was I being a fool or did they appreciate everything I did? I have a tendency to act abruptly and on emotions instead of facts (go figure huh) so I gave it a few days, looked through unanswered emails, read correspondences between me and my clients….. Yes, I was being a fool, instead of doing what I loved, I was doing what my clients wanted.

Fast forward to today, did I cut off my nose to spite my face, maybe but… now, I can go back to doing what I love and charging what I want…. the clients will be far and few between but I will have my sanity and self-respect back and that is worth more than money! For all those that want to start your own data science businesses, never back away from your principles and the fact that you are worth every penny you charge but have a little saved up for those rainy days! Data science may be the most talked about trend in data and it may be sexy but companies are slow to open the purse strings until ROI and value added are proven and not just talked about.

Have fun and enjoy what you do :)

Great Lists to Follow

There are some great lists out there to follow for Data Science and Technology – Check out a few I have been honored to be listed on – Please feel free to check out everyone on these lists, some really great folks out there!

 

50 Data Science Gurus (and 10 Organizations) You Must Follow On Twitter ->
http://blog.automatedinsights.com/post/95129473817/50-data-science-gurus-and-10-organizations-you-must

 

PRESENTING: The 100 Most Influential Tech Women On Twitter ->
http://www.businessinsider.com/most-influential-tech-women-on-twitter-2014-5

 

10 Big Data Pros to Follow on Twitter  ->
http://www.informationweek.com/big-data/big-data-analytics/10-big-data-pros-to-follow-on-twitter

 

4 Women Leading the Way in Business Intelligence ->
http://plotting-success.softwareadvice.com/4-women-leading-bi-0114/

 
Top 200 Thought Leaders in Big Data & Analytics
http://analyticsweek.com/top-200-thought-leaders-in-bigdata-analytics/

 

The Women Behind The Data
http://blog.sqreamtech.com/2014/02/the-women-behind-the-data/
https://smallbusiness.yahoo.com/advisor/women-behind-data-231516131.html

 

Top 5 Data Science Gals
http://datascience101.wordpress.com/2012/12/27/top-5-data-science-gals/

 

Big Data: Experts to Follow on Twitter
http://www.techopedia.com/2/28887/trends/big-data/big-data-who-to-follow-on-twitter

 

 

I appreciate all the mentions and inclusions to these lists and WOW, I’m in great company!

 

Lastly, this is not a list but some advice ->  “An understanding of math is important,” he says, “but equally important is understanding the research. Understanding why you are using a particular type of math is more important than understanding the math itself.”
http://www.wired.com/2013/04/phd-data-scientist/

 

Have fun with DATA :)

 

 

I was recently interviewed by Software Advice for an article about women in STEM fields and in this case specifically about business intelligence. As they stated in the article it is difficult to break into a predominately male field but don’t let that discourage you! There are many women who have done just that and very successfully I might add. So, thanks to Alan S. Horowitz and his crew for the honor of being included in their blog  4 Women Leading the Way in Business Intelligence Excerpt -> “Though clearly hard to break into, technology can be a highly attractive field for women, as it provides a much greater opportunity to stand out than other industries” From Irene Lewis at Software Advice “Your insight into the industry and your expertise in navigating it is inspiring to men and women alike. And thank you for tweeting it out!” Thanks Software Advice and Best Wishes in all you do! Since not every detail was specific, I wanted to add a bit about myself for those that would like to know more, after reading the above article of course ;o) Being a single mother of two sons was a challenge but no one has ever accused me of backing down from a challenge, eager to learn and grow, I entered the University of Tennessee in the spring of 1993. I worked in the Developmental Math Lab my entire tenure with the University of Tennessee, assisting students with all levels of mathematics. Upon graduating with a double major, Applied Mathematics and Economics in 1998, I moved to the Chicago area to start my career in analytics. During the past 16 years, I have worked with many Fortune 100 and 500 companies including but not limited to, Discover Financial Services, J&J, Hershey, Kraft, Kellogg’s, SCJ, McNeil and Firestone, Tandus Worldwide, Terenine and even thought they are not Fortune  companies, both the University of Chicago and the University of Tennessee. Acting as a liaison between the IT department and the Executive staff, I am able to take huge complicated databases, decipher business needs and come back with intelligence that quantifies spending, profit and trends. Being called a data nerd is a badge of courage for this curious Mathematician/Economist because knowledge is power and companies are now acknowledging its importance. Data, what can it do for you today? Specialties: * Comprehensive Customer Satisfaction and Retention Analysis, * Brand Research & Competitive Analysis, * Employee Retention Research, * Survey Creation & Analysis (New Product and Branding), * Database creation and mining, * Social Media & Coupon, Incentive Promotions, * Project Management (Scrum Certified)   * Social Media Marketing * Statistical concepts to solve business challenges. * Advanced knowledge of data warehousing. * Target Audience Analysis * Predictive modeling, forecasting, and data mining. * Develop data strategy, analysis, objectives and business requirements   So, as you see, a woman can succeed in a “MAN’S” world, good luck ladies! If you’d like to see more check me out on LinkedIn at

Einstein2

I was recently contacted by a recruiter concerning a Director of Analytics position at a Private University in Louisville Kentucky; they wanted to know if I could recommend someone for the position since I had a background in Education and Data Science. I dug through my contacts and supplied them with 4 names of experts that would be more than qualified for what they were looking for, all of them Data Scientist with at least 15 years of experience. Well, finally I heard from the last of those I had recommended, and each, for various reasons, were disqualified for the position, HUH?? I called the recruiter and asked what was going on, she said…. “her client, was looking for someone with more experience in fundraising” so, let me get this right….. you disqualified some of the greatest minds in Data Science because of lack of fundraising experience, really.

This is not the first time I have seen “truly amazing” people overlooked for a position due to “them” NOT having some little missing detail on their resume. It makes me question the people doing the hiring; do they even know what they want? Can they recognize an experience candidate or are they going on gut feelings and preconceptions of what “they” think the position needs.

Would you recognize a true Data Scientist if you met one? If you wanted to add data science or analytics to your University or Corporation where would you go, to a head hunter or a recruiter? Probably, but what makes you think they are qualified to find you the best Data Scientist out there when most of them are still trying to figure out what Data Science is!

If you really want to hire the best, I recommend you research the position first, how can you find the perfect candidate if you don’t? Buzzword like “Data Science” and “Big Data,” are added to everyone’s resume in analytics, this DOES NOT make them qualified, stop searching for the obvious and look for words that REAL Data Scientist would use – probability, models, machine learning, statistics, data engineering, pattern recognition, learning, visualization, data warehousing, are some examples.

In conclusion of my rant, I’d like to make one point…… If you really want to hire an expert in Data Science don’t go for the one with the biggest blog or the one that writes the most books, honestly a great data person doesn’t have the time or desire to write blogs and books, we’d rather be doing what we love; playing with data. If I had my choice on hiring the best, I would check out LinkedIn, find all the candidates I wanted and then call and verify reference before I even set up the first interview. Too many people out there pad and just flat out lie about their skills so verify everything! Sometimes I wonder if anyone follows up on anything anymore! A 10 minute phone call will stop you from figuring out how to get rid of someone that sucks, believe me, I see it happen frequently. There are some truly gifted individuals out there, don’t overlook them because you are mesmerized by your own agenda.

 

Data Science ROCKS!

What can your data do for you?

What can your data do for you?

 

 

Recently I did a webinar with Kalido and enjoyed it tremendously, they were kind enough to give me a summary of the webinar, thanks Kalido http://www.kalido.com/

My Favorite Quote from the Webinar “Big Data needs Data Science but Data Science doesn’t need Big Data” Carla Gentry aka @data_nerd

Data science has been around for decades, and it’s not just big data. I hear a lot of people clumping these two together like they go hand-in-hand, which I agree with to an extent. However, big data needs data science but data science doesn’t necessarily need big data. Most of the data a typical company handles on a daily basis or house internally is not big data. Even Facebook and Google break up or segment their data into workable pieces. Data science is big, small, structured, unstructured, messy, clean, etc… It’s more than just analytics. As a data scientist, you’ll become a liaison between the IT department and the C suite. You have to talk both languages and you have to understand the hierarchy of data, you can’t be just an architect or data expert.

 

What really matters in data science is the team effort and your role as a liaison. Your company has large amounts of data and you want to make sure your queries are correct. Whatever tool you use, make sure you have your data cleansed. You want to know that it’s normalized and indexed so that things run smoother. You want to be able to give insight, which requires knowledge of your audience. If your audience is the C suite of a multi-million dollar company, you’re going to need everything you have to back up your conclusions. Be able to prove it and be prepared for questions.

 

What sort of personality makes for an effective data scientist?

Definitely curiosity, I remember in college, my professors shut the door if they saw me coming because telling me that a2 + b2 = C2 was never enough. I wanted to know why. So the biggest question in data science is “why?” Why is this happening? If you notice that there’s a pattern, ask “why?” Is there something wrong with the data or is this an actual pattern going on? Can we conclude anything from this pattern? A natural curiosity will definitely give you a good foundation.

 

For aspiring data scientists, where can they begin?

There are many positions you can get into to learn data science; it’s not just for data engineers. Personally, I started as a junior analyst. Everyone has to start at the ground floor but there are so many resources and open-source data places you can go to practice. Most IT departments aren’t going to give you access to their live database, but they may give you access to their development database where you can go in and practice. Any position that you get into, go tell your boss that you’re interested in becoming a data scientist. Sign up for courses, learn programming languages and learn business. You have to know about budgets and various business aspects, not just the analysis part and not just the IT part. Data science is a wonderful field, and I encourage anyone that has a curiosity about data analysis, hypothesizing, statistics, to give it a shot. Just know that it won’t happen overnight.

Data Science

Data Scientist, Analytical-Solution

 

Over the years I have done quite nicely for myself as the Founder of Analytical Solution, as everyone says, wish I had done it sooner. But every once in a while I reach out to other businesses in order to do something different or I see potential I could add by joining forces with them, unfortunately each time I have tried, I have been dismiss or ignored, curious since Data Scientist are supposed to be in such demand? In all honesty, if I never added another client to my base, I would be fine financially but what fun would that be? After 2 years of “going it alone” I miss comradery and giggling because someones chair made a strange noise, giggle, you know what I mean. I am a NERD and we are not solitary creatures.

 

My most recent dissing, giggle, was from a company in Louisville, not far from me, I could have popped over, done some analysis, crunched some numbers, built a model, taught them about databases, ETL, Modeling… the possibilities are endless because I’ve been doing this for so many years but the guy just blew me off, like it was everyday that a Data Scientist / Economist / Mathematician with over 15 years of experience contacts him. No biggie, but what if he had joined forces with me?

 

Recently Big Data Republic started a Big Data 100 who to follow list, all the Data people are smiling or joining PeerIndex to raise their scores, giggle, it has actually been fun finding new data people on Twitter to follow (which was their original intent, not to exclude or make anyone upset) but I digress – the point is, I’m on this list and every time someone clicks on this site, there is my name, they can drill down by clicking my name or icon to receive more information. Imagine all the thousands of chances missed by NOT partnering with me, the info under my name could have said Founder of Analytical Solution and Partner XXXXX blah blah, you get the point. So, before you pass up an opportunity, the next time someone emails, tweets, or calls, give them a chance, you never know what could come of it :)

It’s not everyday someone with my experience wants to share the wealth of knowledge accumulated working with Fortune 100 and 500 companies, Colleges, Financial Institutions and Econometrical Consulting Firms. If you are in the Louisville area and would be interesting in speaking – send me an email to carla.gentry@analytical-solution.com (who knows, it could be the opportunity of a life time)

Being a Data Scientist

 Data Scientists

Being a “Data Scientist” Is As Much About IT As It Is Analysis by Carla Gentry, aka @Data_nerd

IBM defines the data scientist as -> A data scientist represents an evolution from the business or data analyst role.

 

The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.

 

Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization. The data scientist role has been described as “part analyst, part artist.”

 

Anjul Bhambhri, vice president of big data products at IBM, says, “A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.”…

 

A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data.

 

Data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. Armed with data and analytical results, a top-tier data scientist will then communicate informed conclusions and recommendations across an organization’s leadership structure.

 

IBM hits the nail on the head with the above definition. Having worked with traditional data analysts as well as programmers, developers, architects, scrum masters, and data scientists — I can tell you they don’t all think alike. A data scientist could be a statistician but a statistician may not be completely ready to take on the role of data scientist, and the same goes for all the above titles as well.

 

Beth Schultz from All Analytics mentioned that we are like jacks of all trades but masters of none; I don’t completely agree with this comment, but do agree that my ETL skills are not as honed as my analysis skills, for example. My definition of the data scientist includes: knowledge of large databases and clones, slave, master, nodes, schemas, agile, scrum, data cleansing, ETL, SQL and other programming languages, presentation skills, Business Intelligence and Business Optimization — plus the ability to glean actionable insight from data. I could go on and on about what the data scientists needs to be familiar with, but the analysis part has to be mastered knowledge and not just general knowledge. If you want to separate the pretenders from the experienced in this business, ask a few questions about how data science actually works!

 

When I start working with a new data set (it doesn’t matter how much or what kind), the first question I usually ask is, what kind of servers do you own?

Why would you need to know about the servers to work with data? I ask this question so I will know what kind of load it can handle – is it going to take me 9 hours to process or 15 minutes? How many servers do you have? I ask this because if I have 4 or 5 servers, I can toggle or load balance versus having only 1 that I have to babysit.

What kind of environment will I be working in? I ask this because I need to know if they have a test environment versus a live environment, so I can play without crashing every server in the house and ticking a lot of people off. If you are working with lots of data, lower peak times or low load times are better for live, as compared to test or staging environments where you can “play” without fear. This way, you won’t “bring down the house”.

It’s a good idea for you Chief Marketing Officers (CMOs) to let your Data Scientist work in the evening hours and/or on weekends, at their homes if applicable. This, of course, requires setting up a VPN connection and it also depends on how secure the data connections are, as well as how much processing I can do before I crash them, – um, I mean, what is the speed and capacity to process? If a dial-up connection is all that’s available, forget it.

As a side note, I’ve crashed many a server in my day – how do you think I learned all this stuff? Back in the Nineties, someone would crash the mainframe and we would all head to Einstein’s Deli in Oak Park, IL but today, this might be frowned upon. But I digress, back to more IT related things.

Another handy thing to find out is how the databases are joined. By that I mean, what variables do they have in common (i.e., “primary keys”)? Are the relationships one-to-one, one-to-many, or many-to-many? Why would you ask this? Some programmers (I don’t mean this in general) don’t completely understand relational databases, especially when it comes to transactional data and data that needs to be refreshed often. You have to set up a database like you would play chess: think at least three moves ahead.

Additionally, some programmers/developers use too many JOIN statements in their scripts, which cause large amounts of iterations. Since these tend to increase run time and are not very efficient, you don’t want to be linking too many of these babies together and then running complex algorithms or scripts.

Sometimes, it’s better to start from scratch and build your own data source. When writing scripts to extract or refresh data, don’t forget a few keys things: normalize, index, pick your design based on what you know about the data and what is being requested of it.

Servers are important, and if dealing with large databases, load balance or toggle whenever possible. Also, star schema versus snowflake schema is important, so please put some serious thought into this. Ask yourself, do I need it fast or efficient? Believe me, I always pick efficient (I am a nerd, after all) but if the client needs it ASAP, then the client shall have it ASAP.

With knowledge of the client’s IT setup from a data management/quality perspective, you’ll be equipped to handle most situations you run into when dealing with data, even if the Architect and Programmer are out sick. Your professional knowledge is going to be a big help in getting the assignment or job complete.

Happy data mining and please play with data responsibly!

About the Author

During the past 16+ years, Carla Gentry has worked with Fortune 100 and 500 companies including but not limited to, Discover Financial Services, J&J, Hershey, Kraft, Kellogg’s, SCJ, McNeil and Firestone. Acting as a liaison between the IT department and the Executive staff, she is able to take huge complicated databases, decipher business needs and come back with intelligence that quantifies spending, profit and trends. Being called a data nerd is a badge of courage for this curious Mathematician/Economist because knowledge is power and companies are now acknowledging its importance. To find out more about what Carla does, please visit her profile on LinkedIn ->https://www.linkedin.com/in/datanerd13

Seems anyone can create a list of people to follow – unfortunately when I see list of Top Data people or Data Scientist, it’s always male dominated, not saying they don’t deserve it because they do, but there are many ladies out there that code their butts off and glean insight like a ROCK STAR. So, to correct this, I have compiled a list of great women in Data to follow.

 

Please follow these users for the latest in Data Science, Analysis, and Quality!

 

Karen Lopez

@datachick

 

Shelly Lucas

@pisarose

 

Big Data Gal

@BigDataGal

 

Dr Emily R Coleman

@e_r_coleman

 

Jacqueline Roberts

@JackieMRoberts

 

Gwen Thomas

@gwenthomasdgi

 

Melinda Thielbar

@mthielbar

 

April Reeve

@Datagrrl

 

Sarah Schmidt

@uptimedb2dba

 

Angela Dunn

@blogbrevity

 

Cathy O’Neil

@mathbabedotorg

 

NISS SAMSI

@NISSSAMSI

 

Isabel Elaine Allen

@DataCooker

 

Beth Schultz

@Beth_Schultz

 

Emily Carter

@EmRCarter

 

Noreen Seebacher

@writenoreen

 

Blaine Kohl

@bmkohl

 

Loretta Mahon Smith

@silverdata

 

Mandi Bishop

@MandiBPro

 

and last but not least – ME :) 15 years of crunching data and gleaning insight

Carla Gentry CSPO

@data_nerd

 

I hope you enjoy following these users as much as I do and if I forgot any Data Ladies, please accept my apologies – and send me a Tweet @data_nerd so I can start following you :)