Introduction

Job Interview Statistics: Job interviews are essential phases in the hiring process where prospective employees can show their worth, and employers can judge their fit. The year 2024 has seen its share of changes in the dynamics of interviews because of evolving work environments, ongoing digital transformations, and shifting economic tides. Herein, you'll see the job interview statistics that should guide job seekers and employers.

Introduction

Online Interview Statistics: Online interviews have become a game-changer for hiring, with 86% of companies now using virtual interviews to find the right talent. Following the COVID-19 pandemic, 55% of employers increased their use of video conferencing tools for interviews, and many have continued this trend. Candidates love the convenience, with 80% saying they prefer virtual interviews.

Not only do online interviews save time and money, but they also open up access to a global talent pool. With technology advancing, tools like artificial intelligence (AI) and machine learning are starting to play a bigger role in making the hiring process even smoother and more efficient.

The "Ultimate Data Science Interview Q&A Treasury" dataset is a meticulously curated collection designed to empower aspiring data scientists with the knowledge and insights needed to excel in the competitive field of data science. Whether you're a beginner seeking to ground your foundations or an experienced professional aiming to brush up on the latest trends, this treasury serves as an indispensable guide. Furthermore, you might want to work on the following exercises using this dataset :

1)Keyword Analysis for Trending Topics: Frequency Analysis: Identify the most common keywords or terms that appear in the questions to spot trending topics or skills. 2)Topic Modeling: Use algorithms like Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) to group questions into topics automatically. This can reveal the underlying themes or areas of focus in data science interviews. 3)Text Difficulty Level Analysis: Implement Natural Language Processing (NLP) techniques to evaluate the complexity of questions and answers. This could help in categorizing them into beginner, intermediate, and advanced levels. 4)Clustering for Unsupervised Learning: Apply clustering techniques to group similar questions or answers together. This could help identify unique question patterns or common answer structures. 5)Automated Question Generation: Train a model to generate new interview questions based on the patterns and topics discovered in the dataset. This could be a valuable tool for creating mock interviews or study guides.

Task 1

Business roles at AgroStar require a baseline of analytical skills, and it is also critical that we are able to explain complex concepts in a simple way to a variety of audiences. This test is structured so that someone with the baseline skills needed to succeed in the role should be able to complete this in under 4 hours without assistance.

Use the data in the included sheet to address the following scenario...

Since its inception, AgroStar has been leveraging an assisted marketplace model. Given that the market potential is huge and that the target customer appreciates a physical store nearby, we have taken a call to explore the offline retail model to drive growth. The primary objective is to get a larger wallet share for AgroStar among existing customers.

Assume you are back in time, in August 2018 and you have been asked to determine the location (taluka) of the first AgroStar offline retail store. 1. What are the key factors you would use to determine the location? Why? 2. What taluka (across three states) would you look open in? Why?

Guidelines:

-- (1) Please mention any assumptions you have made and the underlying thought process -- (2) Please treat the assignment as standalone (it should be self-explanatory to someone who reads it), but we will have a follow-up discussion with you in which we will walk through your approach to this assignment. -- (3) Mention any data that may be missing that would make this study more meaningful -- (4) Kindly conduct your analysis within the spreadsheet, we would like to see the working sheet. If you face any issues due to the file size, kindly download this file and share an excel sheet with us -- (5) If you would like to append a word document/presentation to summarize, please go ahead. -- (6) In case you use any external data source/article, kindly share the source.

Task 4 Cohort

The file CDNOW_master.txt contains the entire purchase history up to the end of June 1998 of the cohort of 23,570 individuals who made their first-ever purchase at CDNOW in the first quarter of 1997. This CDNOW dataset was first used by Fader and Hardie (2001).

Each record in this file, 69,659 in total, comprises four fields: the customer's ID, the date of the transaction, the number of CDs purchased, and the dollar value of the transaction.

CustID = CDNOW_master(:,1); % customer id Date = CDNOW_master(:,2); % transaction date Quant = CDNOW_master(:,3); % number of CDs purchased Spend = CDNOW_master(:,4); % dollar value (excl. S&H)

See "Notes on the CDNOW Master Data Set" (http://brucehardie.com/notes/026/) for details of how the 1/10th systematic sample (http://brucehardie.com/datasets/CDNOW_sample.zip) used in many papers was created.

Reference:

Fader, Peter S. and Bruce G.,S. Hardie, (2001), "Forecasting Repeat Sales at CDNOW: A Case Study," Interfaces, 31 (May-June), Part 2 of 2, S94-S107.

Task 6 Zupee.csv

I have merged all three datasets into one file and also did some feature engineering.
Available Data: You will be given anonymized user gameplay data in the form of 3 csv files. Fields in the data are as described below: Gameplay_Data.csv contains the following fields: * Uid: Alphanumeric unique Id assigned to user * Eventtime: DateTime on which user played the tournament * Entry_Fee: Entry Fee of tournament * Win_Loss: ‘W’ if the user won that particular tournament, ‘L’ otherwise * Winnings: How much money the user won in the tournament (0 for ‘L’) * Tournament_Type: Type of tournament user played (A / B / C / D) * Num_Players: Number of players that played in this tournament

Wallet_Balance.csv contains following fields: * Uid: Alphanumeric unique Id assigned to user * Timestamp: DateTime at which user’s wallet balance is given * Wallet_Balance: User’s wallet balance at given time stamp

Demographic.csv contains following fields: * Uid: Alphanumeric unique Id assigned to user * Installed_At: Timestamp at which user installed the app * Connection_Type: User’s internet connection type (Ex: Cellular / Dial Up) * Cpu_Type: Cpu type of device that the user is playing with * Network_Type: Network type in encoded form * Device_Manufacturer: Ex: Realme * ISP: Internet Service Provider. Ex: Airtel * Country * Country_Subdivision * City * Postal_Code * Language: Language that user has selected for gameplay * Device_Name * Device_Type

Build a basic recommendation system which is able to rank/recommend relevant tournaments and entry prices to the user. The main objectives are: 1. A user should not have to scroll too much before selecting a tournament of their preference 2. We would like the user to play as high an entry fee tournament as possible

The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS) which is part of the Centers for Disease Control and Prevention (CDC). The National Health Survey Act of 1956 provided for a continuing survey and special studies to secure accurate and current statistical information on the amount, distribution, and effects of illness and disability in the United States and the services rendered for or because of such conditions. The survey referred to in the Act, now called the National Health Interview Survey, was initiated in July 1957. Since 1960, the survey has been conducted by NCHS, which was formed when the National Health Survey and the National Vital Statistics Division were combined. NHIS data are used widely throughout the Department of Health and Human Services (DHHS) to monitor trends in illness and disability and to track progress toward achieving national health objectives. The data are also used by the public health research community for epidemiologic and policy analysis of such timely issues as characterizing those with various health problems, determining barriers to accessing and using appropriate health care, and evaluating Federal health programs. The NHIS also has a central role in the ongoing integration of household surveys in DHHS. The designs of two major DHHS national household surveys have been or are linked to the NHIS. The National Survey of Family Growth used the NHIS sampling frame in its first five cycles and the Medical Expenditure Panel Survey currently uses half of the NHIS sampling frame. Other linkage includes linking NHIS data to death certificates in the National Death Index (NDI). While the NHIS has been conducted continuously since 1957, the content of the survey has been updated about every 10-15 years. In 1996, a substantially revised NHIS questionnaire began field testing. This revised questionnaire, described in detail below, was implemented in 1997 and has improved the ability of the NHIS to provide important health information.

This submission includes all assessment data from the paper "Think-aloud interviews: A tool for exploring student statistical reasoning", as well as code necessary to reproduce the figures and tables presented in the paper, and the supplemental materials for the paper. See the file README.txt for full description of all files. Each student interviewed has been given a fictitious name.

This data collection consists of semi-structured interviews designed to cover processes in five domains of integration (social, cultural, structural, civic and political, identity) with sections on life before and after marriage. The data deposited consists of the transcripts of the recorded semi-structured interviews with British Pakistani Muslim and British Indian Sikh spouses, and migrant Pakistani Muslim and migrant Indian Sikh spouses. This research explored the relationships between marriage migration and integration, focusing on the two largest UK ethnic groups involved in transnational marriages with partners from their parents’ or grandparents’ countries of origin: British Pakistani Muslims and British Indian Sikhs.

Comprehensive Software Engineering Interview Questions Dataset

Description: Overview This dataset is an extensive collection of software engineering interview questions, designed to mirror the complexity and depth of questions asked in interviews at top tech companies, including FAANG (Facebook, Amazon, Apple, Netflix, Google). It encompasses a wide range of topics, from algorithms and data structures to system design and machine learning. The dataset is curated to assist candidates in preparing for technical interviews and to provide educators and interviewers with a resource for assessing technical skills.

Dataset Details Number of Questions: 250 Categories Covered: Algorithms, System Design, Machine Learning, Data Structures, Distributed Systems, Networking, Low-level Systems, Security, Database Systems, Artificial Intelligence, Data Engineering. Difficulty Level: Primarily Hard. Format: The dataset is structured in a tabular format with columns for Question Number, Question, Brief Answer, Category, and Difficulty. Usage Scenarios: Interview preparation for candidates, educational resource for learning advanced software engineering concepts, tool for interviewers to structure technical assessments. Potential Analysis Users can perform various analyses, such as:

Category-wise Distribution: Understand the focus areas in software engineering roles. Difficulty Analysis: Gauge the complexity level of questions typically asked in high-end tech interviews. Trend Analysis: Identify trends in technical questions over recent years, especially in rapidly evolving fields like Machine Learning and AI. Inspiration This dataset is intended to inspire:

Job Candidates: To prepare comprehensively for technical interviews. Educators: To structure curriculum or coursework around practical, interview-oriented learning. Researchers: To analyze trends in technical interviews and skill requirements in the tech industry. Interviewers/Hiring Managers: To formulate effective interview strategies and questionnaires.

2019–present. The National Health Interview Survey (NHIS) is a nationally representative household health survey of the U.S. civilian noninstitutionalized population. The NHIS data are used to monitor trends in illness and disability, track progress toward achieving national health objectives, for epidemiologic and policy analysis of various health problems, determining barriers to accessing and using appropriate health care, and evaluating Federal health programs. NHIS is conducted continuously throughout the year by the National Center for Health Statistics (NCHS). Public-use data files on adults and children with corresponding imputed income data files, and survey paradata are released annually. The NHIS data website (https://www.cdc.gov/nchs/nhis/documentation/index.html) features the most up-to-date public-use data files and documentation for downloading including questionnaire, codebooks, CSV and ASCII data files, programs and sample code, and in-depth survey description. Most of the NHIS data are included in the public use files. NHIS is protected by Federal confidentiality laws that state the data collected by NCHS may be used only for statistical reporting and analysis. Some NHIS variables have been suppressed or edited in the public use files to protect confidentiality. Analysts interested in using data that has been suppressed or edited may apply for access through the NCHS Research Data Center at https://www.cdc.gov/rdc/. In 2019, NHIS launched a redesigned content and structure that differs from its previous questionnaire designs. NHIS has been conducted continuously since 1957.

This dataset comprises images of interview candidates categorized into two labels: "confident" and "not confident." The dataset aims to capture and analyze facial expressions of individuals during a simulated interview scenario, providing insights into the perceived confidence levels based on facial cues.

Labels:

Confident: Images of candidates exhibiting facial expressions associated with confidence, such as positive demeanor, assertive posture, and self-assured facial features.

Not Confident: Images of candidates displaying facial expressions indicative of a lack of confidence, potentially including signs of nervousness, uncertainty, or discomfort during the interview.

Image Content:

Each image in the dataset represents a candidate's facial expression during a specific moment of the interview. The dataset emphasizes capturing variations in facial expressions, emphasizing features related to confidence.

Data Collection:

The images were likely collected through photography or video recording during simulated interview scenarios. Candidates may have been instructed to express confidence or lack thereof based on the context of the interview.

Attributes:

Image Files: The dataset contains image files in common formats (e.g., JPEG, PNG). Labels: Each image is associated with a label ("confident" or "not confident") based on the observed facial expression. Facial Features: The dataset may include variations in facial features, expressions, and poses to represent diverse scenarios.

Potential Use Cases:

Emotion Recognition Research: Researchers can use the dataset to explore the relationship between facial expressions and perceived confidence during interviews.

Machine Learning Model Training: The dataset can be employed to train machine learning models for facial expression classification or emotion recognition tasks.

Interview Training: The dataset may be useful for developing educational tools to help individuals improve their interview skills by analyzing facial expressions.

Limitations:

The dataset may be limited in size, and the subjective nature of confidence assessment based on facial expressions can introduce variability. The representativeness of confident and not confident labels may depend on the context of the simulated interviews.

Suhelali/Interview-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

Exploring the potential for nutrient circularity in the beef production system requires an understanding of current practices. Manure nutrients produced in feedlots are an ample source of fertilizer for phosphorus deficient crop and hay lands. However, it is unclear how far manure nutrients are travelling from feedlots, what crops they’re being applied to, and whether those grains are in turn integrated into the feedlot operations. The purpose of these interviews was to ascertain the above information from feedlot managers. In addition, we sought contextual information (provenance of cattle, cattle weights/ages, manure treatment, regulations/guidelines, processing facility destination, barriers, suggestions for improvements). To answer our question about potential manure nutrient circularity, we focused and report here the elements pertaining to feed/grain provenance, crops manure was applied to, and export distance for manure.

This digital archive contains interview data with the Karelian minority speakers in Russia. The archive consists of audio files (available in .wav format) containing individual and focus group interviews with the speakers of four different age groups. All interview files were named using a special coding system. Each file name includes: a) a country where the research was conducted; b) the speech community studied; c) the form of an interview; d) the type of the target group; e) the age group and gender; f) the date of the interview (DDMMYEAR). For the full list of files, as well as code values please refer to the descriptions under “ELDIAdata: Metadata”.

Case files related to filling job vacancies, held by hiring official and interview panel members. Includes:rn- copies of records in the job vacancy case file (item 050 and 051)rn- notes of interviews with selected and non-selected candidatesrn- reference check documentation

CPQA Formula: Total recruiting spend ÷ Number of qualified applicants Qualified-to-Interview Rate (QIR): Percentage of qualified applicants who progress to interviews Interview-to-Offer Rate: Percentage of interviewed candidates who receive offers Offer-to-Hire Rate: Percentage of offers that convert to hires Qualified-to-Hire Cost: Total spend ÷ hires originating from qualified applicants

This data set contains instances of a field interview conducted by an MCP officer with an individual subject. Update Frequency: Daily

Interviews

These are the anonomyised transcripts generated from the interviews conducted for this study.

The interview data was gathered for a project that investigated the practices of instructors who use quantitative data to teach undergraduate courses within the Social Sciences. The study was undertaken by employees of the University of California, Santa Barbara (UCSB) Library, who participated in this research project with 19 other colleges and universities across the U.S. under the direction of Ithaka S+R. Ithaka S+R is a New York-based research organization, which, among other goals, seeks to develop strategies, services, and products to meet evolving academic trends to support faculty and students.

The field of Social Sciences has been notoriously known for valuing the contextual component of data and increasingly entertaining more quantitative and computational approaches to research in response to the prevalence of data literacy skills needed to navigate both personal and professional contexts. Thus, this study becomes particularly timely to identify current instructors' practices and strategies to teach with data, as well as challenges and opportunities to help them advance their instructional efforts. The fundamental goal of this study is fourfold: 1) Explore the ways in which instructors teach undergraduates with data, 2) Understand instructors' support needs going forward, 3) Develop actionable recommendations for stakeholders, and 4) Build relationships within UCSB and across higher education institutions. The findings of this study will help to inform new services, policies, and practices not only at the University of California, Santa Barbara Library (UCSB Library), and the broader campus community, but also at other institutions seeking to advance their data instruction in the Social Sciences.

These interview data are part of the project "Looking for data: information seeking behaviour of survey data users", a study of secondary data users’ information-seeking behaviour. The overall goal of this study was to create evidence of actual information practices of users of one particular retrieval system for social science data in order to inform the development of research data infrastructures that facilitate data sharing. In the project, data were collected based on a mixed methods design. The research design included a qualitative study in the form of expert interviews and – building on the results found therein – a quantitative web survey of secondary survey data users. For the qualitative study, expert interviews with six reference persons of a large social science data archive have been conducted. They were interviewed in their role as intermediaries who provide guidance for secondary users of survey data. The knowledge from their reference work was expected to provide a condensed view of goals, practices, and problems of people who are looking for survey data. The anonymized transcripts of these interviews are provided here. They can be reviewed or reused upon request. The survey dataset from the quantitative study of secondary survey data users is downloadable through this data archive after registration. The core result of the Looking for data study is that community involvement plays a pivotal role in survey data seeking. The analyses show that survey data communities are an important determinant in survey data users' information seeking behaviour and that community involvement facilitates data seeking and has the capacity of reducing problems or barriers. The qualitative part of the study was designed and conducted using constructivist grounded theory methodology as introduced by Kathy Charmaz (2014). In line with grounded theory methodology, the interviews did not follow a fixed set of questions, but were conducted based on a guide that included areas of exploration with tentative questions. This interview guide can be obtained together with the transcript. For the Looking for data project, the data were coded and scrutinized by constant comparison, as proposed by grounded theory methodology. This analysis resulted in core categories that make up the "theory of problem-solving by community involvement". This theory was exemplified in the quantitative part of the study. For this exemplification, the following hypotheses were drawn from the qualitative study: (1) The data seeking hypotheses: (1a) When looking for data, information seeking through personal contact is used more often than impersonal ways of information seeking. (1b) Ways of information seeking (personal or impersonal) differ with experience. (2) The experience hypotheses: (2a) Experience is positively correlated with having ambitious goals. (2b) Experience is positively correlated with having more advanced requirements for data. (2c) Experience is positively correlated with having more specific problems with data. (3) The community involvement hypothesis: Experience is positively correlated with community involvement. (4) The problem solving hypothesis: Community involvement is positively correlated with problem solving strategies that require personal interactions.