Sapphire HR Solutions’ Post

PXT Select surveys found that hiring managers who were most confident in their processes relied more on data than instinct. Data, of course, includes the usual stuff: job skills, experience, and education. But what other kinds of data about potential hires can be helpful? What would be beneficial to learn that doesn’t fit on a résumé or isn’t discussed in an interview? Ask us about a demo today!

Thank you to Break Into Data, Meri Nova, Dawn Choo, Venkata Naga Sai Kumar Bysani, and Karun Thankachan for a wonderful presentation around the different data roles and how to prepare for interviews. Furthermore, they took the time to create a preparation roadmap. There was a lot of great information that was presented in a nuanced way. And they were so fun together! Here's what I found to be most helpful for me: 1. The breakdown of the different data roles. 2. How interview preparation differs from role to role and how to prepare appropriately. 3. Resources to assist in the appropriate preparation for your goal role. Thank you again!

Wondering if OpenAI is "open and shut"??? Wondering if Stability AI is "actually stable"??? Wondering if a DataRobot is going to take over the world??? Not sure - but we will have an INFORMED DISCUSSION about all of these topics at my upcoming workshop, "Application Basics – Integrating AI in Application Pipelines"! In addition to the focus on application flows and pipelines, terminology and definitions, and case studies, I have added a SPECIAL LECTURE focused on advising those curious about charting the career course in a predominantly AI direction. Get this valuable knowledge you won't learn in college! Join us! Register here: https://lnkd.in/erjiF5fr Aryaman Belgaumkar Lorne Saubel Swarnali Goswami Bhanudas Waskar Yaregal Admasu (MSc) Wasif Maqsood Shweta Todkar Rebecca Cook Neco Turkienicz Stefanie Veras Lord Arash Nassouri

What is one method you've implemented for feature selection in a large dataset, and why did you choose it? Submit your answer on Featured, and get featured on Big Data Interviews: https://lnkd.in/gb6PTD8C Deadline: 2024-Jul-19 #BigData

Now that we're done with the summit - we've entered our period of reflection. After a series of Plenary Sessions, Round Tables, and 60 recorded interviews, we're back to doing what we do best—providing Fathers and Practitioners with resources that make a difference. This reminds us of this tool we created that looks at the meaning of data. As Dads and practitioners, we often collect data in different ways at different scales. Here's row: the correct data can provide you with empowering insights.

One of the frequently asked question in interview and very important concept that needs to be cleared is join. Most of the time interviewer gave two different set/dataframes and ask the output of different type of joins. "Venn diagram of sets" : if this concept is clear , then it will be very easy to understand. Let us take an example of two such dataframes and understand different type of joins : >>> a.show() +----+ | id| +----+ |  1| |  1| |  2| |NULL| |NULL| +----+ >>> b.show() +----+ | id| +----+ |  1| |  2| |  2| |  0| |NULL| +----+ 1. Inner join : >>> a_inner_b=a.join(b,['id'],'inner') >>> a_inner_b.show() +---+ | id| +---+ | 1| | 1| | 2| | 2| +---+ 2. Left Join : >>> a_left_b=a.join(b,['id'],'left') >>> a_left_b.show() +----+ | id| +----+ |  1| |  1| |  2| |  2| |NULL| |NULL| +----+ 3. Right Join : >>> a_right_b=a.join(b,['id'],'right') >>> a_right_b.show() +----+ | id| +----+ |  1| |  1| |  2| |  2| |  0| |NULL| +----+ 4. Self Join : >>> a_self_a=a.join(a,['id']) >>> a_self_a.show() +---+ | id| +---+ | 1| | 1| | 1| | 1| | 2| +---+ 5. Left Anti Join: >>> b_anti_a=b.join(a,['id'],'left_anti') >>> b_anti_a.show() +----+ | id| +----+ |  0| |NULL| +----+ 6. Left Semi Join : >>> b_semi_a=b.join(a,['id'],'left_semi') >>> b_semi_a.show() +---+ | id| +---+ | 1| | 2| | 2| +---+ 7. Cross Join : >>> a_outer_b=a.crossJoin(b) >>> a_outer_b.show(26) +----+----+ | id| id| +----+----+ |  1|  1| |  1|  2| |  1|  2| |  1|  0| |  1|NULL| |  1|  1| |  1|  2| |  1|  2| |  1|  0| |  1|NULL| |  2|  1| |  2|  2| |  2|  2| |  2|  0| |  2|NULL| |NULL|  1| |NULL|  1| |NULL|  2| |NULL|  2| |NULL|  2| |NULL|  2| |NULL|  0| |NULL|  0| |NULL|NULL| |NULL|NULL| +----+----+ One can notice how repetitive keys get multiplied in different type of join and how NULL do not take part in the join. Also, I feel "left-anti" & "left-semi" join is one kind of filter operation which can be very helpful to identify/eliminate duplicate rows based on key column. Note : I am not explaining join definition in detail, rather giving example of how it works. If one can not understand any type of join, I will be happy to help there.

Ready for a game-changing business move? Dive into vulnerability assessments with these 6 expert tips! Identify key areas, analyze past data, and seek external perspectives. Utilize surveys, conduct interviews, and evaluate feedback. Uncover weaknesses and set the stage for growth and improvement. Your business's success lies in understanding its vulnerabilities! #BusinessGrowth #VulnerabilityAssessments #ExpertTips #xiarch #madewithpredisai

For download: The In-depth Interview Method https://bit.ly/2wzIey3 - The Focus Group Method https://bit.ly/3akMzCV - Qualitative Data Analysis https://bit.ly/2zius3I - #Qualitative Research: Transparency & Reporting https://bit.ly/2Tc5IBr - Reflexivity https://bit.ly/2zE0BTV

Here's a step-by-step guide to planning for data collection: 1- Determine the objectives of the data collection and what information you need to gather. 2- Identify the sources of data, such as surveys, interviews, focus groups, or existing data sources. 3- Choose the most appropriate data collection method based on the objectives and sources. 4- Develop a data collection instrument, such as a questionnaire, interview guide, or observation checklist. 5- Design a sampling plan, considering factors such as the population size, characteristics, and accessibility..

The poll posted last week is showing the below results, so far. In case, you have not yet voted, please participate (just one click 😊) - link in the comment. The analytics shall not end here, look out this space to read more on what happens behind the scenes and how these factors actually derive the interview results! Feel free to add your thoughts, we are here to learn and grow together 🤝