Challenges addressed by No Code AI platforms
An AI model building is challenging on three fundamental counts:
- Availability of relevant data in good quantity and quality: The less I rant about it, the better.
- Need for multiple skills: Building an effective and monetizable AI model is not…
Added by Monjima Nandi on August 12, 2021 at 7:00pm —
Natural Language Processing Defeats User Trepidation About Augmented Analytics
Your business users probably fight you on improving data literacy and on implementing digital transformation. Many business users have a fear of analytics and envision having to become a business analyst or a data scientist in order to… Continue
Added by Kartik Patel on February 15, 2021 at 3:00am —
Did you ever wonder what’s a typical day of a data scientist like? A data scientist needs to explore data given to us and provide actionable insights, but how do we do that and is that all we do? Do we just sit in-front of a computer and code all day? Do we spend most of our day reading papers? Or is it something completely different? Let me walk you… Continue
Added by Angelia Toh Choon Muay on March 26, 2020 at 5:12am —
The successful implementation of an augmented analytics solution for business users is not just about choosing a cost-effective tool and completing a timely deployment, nor does the process stop with training. In order to get business users to embrace and adopt self-serve augmented data discovery tools, the enterprise must approach the implementation with appropriate change management processes.
If you want a business user or a team to align with the Citizen Data… Continue
Added by Kartik Patel on May 21, 2019 at 1:30am —
What is Automated Machine Learning? Quite simply, it is the means by which your business can optimize resources, encourage collaboration and rapidly and dependably distribute data across the enterprise and use that data to predict, plan and achieve revenue goals.
With the right tools, today’s average business user can become a Citizen Data Scientist, using data integrated from various sources to learn, test theories and make decisions. AutoML comes into play as… Continue
Added by Kartik Patel on March 13, 2019 at 3:00am —
If Data Science was once the sole domain of analysts and data scientists, Augmented Data Science represents the democratized view of this domain. With Augmented Data Science, the average business user can engage with advanced analytics tools that allow for automated machine learning (AutoML) and leverage sophisticated analytical techniques and algorithms in a guided environment that uses auto-recommendations and suggestions to lead users through the complex world of data… Continue
Added by Kartik Patel on March 11, 2019 at 12:52am —
What does the customer want? And why do they want it?
These are the age old questions that marketers have tried answering to improve their business impact. Customer knowledge is, without doubt, the foundation of marketing success.…
Added by Sudhanshu Ahuja on December 18, 2018 at 5:38pm —
Enterprises are learning fast about the relevance and use of AI and Cognitive Computing platforms. Before adopting AI and Cognitive Computing platforms, enterprises must focus on designing the right strategy for their business. A data-driven strategy is very important to derive the maximum benefit from the platform that will help to interpret data and provide… Continue
Added by Dileep Srinivasan on May 2, 2018 at 5:30pm —
Finding out the difference between data scientists, data engineers, software engineers, and statisticians can be confusing and complicated. While all of them are linked to data in a way, there is an underlying difference between the work they do and manage.
The growth of data and its usage across… Continue
Added by Ronald van Loon on December 19, 2017 at 1:00am —
A data scientist needs to be Critical and always on a lookout for something that misses others. So here is some advice that one can include in the day to day data science work to be better at their work:
1. Beware of the Clean Data Syndrome
You need to ask yourself questions even before you start working on the data. **Does this data make sense?** Falsely assuming that the data is clean could lead you towards wrong Hypotheses. Apart from that, you can discern a… Continue
Added by Rahul Agarwal on March 29, 2017 at 10:00am —
In my experience, some of the most talented analytics professionals I’ve managed were ones that had intimate knowledge of the system limitations required to meet customer needs. These individuals came from a variety of roles, some from engineering, and others from customer service roles. Their strength was in forming specific hypotheses to pinpoint customer experience issues and then leveraging their curiosity to do whatever it took, including learning new statistical techniques and… Continue
Added by Valiance Solutions on August 23, 2016 at 9:00pm —
Data Visualization happens to be an uncomplicated way of assisting the human memory and enhancing decision making. A number of tools are there that are going to cater to the requirements of having a look at humongous statistics to the tune of Peta and Zeta bytes. Number crunching when the amount of data is huge is an incredibly testing job and this is software that is going to be your buddy in addressing testing business dilemmas.…
Added by Pawan Dwivedi on August 3, 2016 at 11:00pm —
Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them. Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges.
On any given day, a data scientist may be required to:
Added by DR. Elvin Prasad on March 23, 2016 at 5:00am —
- Brush up on your math and statistics skills. A good data scientist must be able to understand what the data is telling you, and to do that, you must have solid basic linear algebra, an understanding of algorithms and statistics skills. More advanced mathematics may be required for certain positions, but this is a good place to start.
- Understand the concept of machine learning. Machine learning is emerging…
Added by DR. Elvin Prasad on March 23, 2016 at 5:00am —
In today’s tutorial I am going to teach on how to do basic data manipulation on a sample dataset. I will cater the data from US Census. US Census is one of the richest data source on internet. You can get great insights from American Community Survey about United States of America.
So let’s get started.
Added by Rana Usman on January 26, 2016 at 4:25am —
There are lots of undergraduates are eager to do an internship in the data scientist post in a top companies. But unfortunately, they won't provide "Data science interns" rather they will provide an offer in "Software Engineer interns". If the student is enthusiastic in machine learning and statistical abilities, the company will consider that candidate for a "data analyst" post. The top companies which offers the minimum number of data scientist intern post.
8000+ jobs in 2016 and… Continue
Added by Shankar Krish on January 21, 2016 at 3:30am —
What are we trying to predict? Where is the data? How are we measuring if we are getting it right? Which departments are a part of this Data Science project? Who is my internal customer? When does this have to be completed? How clean is the data?
Data Science projects seem to go through a natural set of phases which model the inquisitive spirit of Data… Continue
Added by Damian Mingle on January 6, 2016 at 4:55am —
DJ Patil is arguably the most well-known data scientist in the world. He’s the Chief Data Scientist for the White House, he built the first data science team at LinkedIn, and along with Jeff Hammerbacher, is credited by Forbes as coining the term data scientist.
So, How does Patil's journey compare to those of the… Continue
Added by Daniel Levine on November 12, 2015 at 10:30am —
The Data Science Association (DSA) and Google is interested in learning more about your experience with tools and training. Click on the link below to take a 10-minute survey for data scientists.…
Added by Michael Walker on October 1, 2015 at 7:53pm —
I recently read a Harvard Business Review (HBR) article “You need an algorithm, not a data scientist”. The author (from an analytics vendor) argues that:
- Companies are increasingly trying to do more analysis of their data to find value and are hiring people…
Added by Guerrilla Analytics on July 21, 2015 at 11:00am —