Data Science Hacks

Lessons

This topic contains 11 replies, has 2 voices, and was last updated by  Bob Hayes 2 years, 3 months ago.

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  • #12423

    Vishal Kumar
    Participant

    Lets share some quick / snappy lessons

    • This topic was modified 2 years, 11 months ago by  Vishal Kumar.
  • #12425

    Vishal Kumar
    Participant

    Knowing the business side could save your project

    Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

  • #13080

    Vishal Kumar
    Participant

    Data aids, not replace judgement
    Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which utlimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

  • #17698

    Vishal Kumar
    Participant

    Grow at the speed of collaboration
    A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

    • This reply was modified 2 years, 7 months ago by  skbhate.
  • #17928

    Vishal Kumar
    Participant

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

    • This reply was modified 2 years, 7 months ago by  admin.
  • #18146

    Vishal Kumar
    Participant

    Fix the Culture, spread awareness to get awareness
    Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

    • This reply was modified 2 years, 7 months ago by  admin.
    • This reply was modified 2 years, 7 months ago by  admin.
    • This reply was modified 2 years, 7 months ago by  admin.
  • #19740

    Vishal Kumar
    Participant

    Save yourself from zombie apocalypse from unscalable models:

    One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

  • #20166

    Vishal Kumar
    Participant

    Data Analytics Success Starts with Empowerment:

    Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

  • #20714

    Vishal Kumar
    Participant

    Winter is coming, warm your Analytics Club
    Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

    • This reply was modified 2 years, 6 months ago by  admin.
  • #21139

    Vishal Kumar
    Participant

    Finding a success in your data science ? Find a mentor

    Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

  • #24176

    Vishal Kumar
    Participant

    Keeping Biases Checked during the last mile of decision making
    Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

    • This reply was modified 2 years, 4 months ago by  admin.
  • #25000

    Bob Hayes
    Participant

    Data Have Meaning
    We live in a Big Data world in which everything is quantified. While the emphasis of Big Data has been focused on distinguishing the three characteristics of data (the infamous three Vs), we need to be cognizant of the fact that data have meaning. That is, the numbers in your data represent something of interest, an outcome that is important to your business. The meaning of those numbers is about the veracity of your data.

    Numbers could reflect the quality of a manufacturing process. They could represent the health of customer relationships. They could even reflect the quality of healthcare that patients receive. Further, each of these outcomes can be measured using different metrics. For example, the health of the customer relationship could be gleaned from customer survey ratings, Twitter feeds or transcripts from call center conversations, to name a few. Yet, these different data sources provide vastly different perspectives of the “health of the customer relationship.” One number might reflect good customer health; another number might represent poor customer health. Appreciating these differences is the crux of understanding the veracity of your data and metrics.

    Don’t forget that a number (metric) is more than just a number; it represents something of interest to your company. Be clear about your metrics and what they mean to your business. You can read more about veracity here.

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