Applying for a job is stressful enough. Applying a job for a Data Scientist position is way more excruciating because there are many more things to prepare as compared to the usual job application. In my time, I have seen many Data Scientist resumes and know what I want to look for in the aspiring Data Scientist. Moreover, with a pandemic comes special needs.
At the very least, I know what to have in your resume when you are looking for a Data Scientist job in a pandemic because I managed to move into new employment during the pandemic (although I already have some experience).
Nevertheless, I want to share what you shouldhave in your resumeto apply for a Data Scientist position. For the record, depending on where you are applying for the Data Scientist position, you should tailor the resume to fulfil the job application.
The top of the page is the most important thing in your resume. Not just for Data Scientist, but also for any resume.
Many of the people in HR that I know say that when selecting the candidate for the interview process, they only take a glance at the candidate resume and decide in an instant which resume is worth taking their time to look at deeper.
So, what kind of header and description is the eye-catcher? I would give you an example of my own resume and the kind that I look for in a resume.
So, above is the header and description example that I used for the Data Science job application. Let’s break it down one by one.
#towards-data-science #data-science #data #employment
ในการเขียนเรซูเม่นั้น มีกันพูดถึงกันอยู่แพร่หลายว่าคุณจะต้องอัพเดทเรซูเม่ใหม่ทุกครั้งที่สมัครงานใหม่ เพื่อให้ได้ข้อมูลที่ละเอียดที่สุด และส่งผลให้คุณได้งานไปในที่สุด แต่ว่าจริงๆแล้วจะต้องอัพเดทเรซูเม่เมื่อไหร่ดีนะ?
เป็นที่พูดกันในวงกว้างของมืออาชีพในการเขียนเรซูเม่ว่า จริงๆแล้วคุณไม่จำเป็นจะต้องอัพเดททุกครั้ง ทุกวันก็ได้นะ ถ้าไม่มีข้อมูลอะไรใหม่ จะอัพเดทไปทำไม และแนะนำให้คุณอัพเดทเรซูเม่เฉพาะเวลาที่มีอะไรใหม่เข้ามาเท่านั้น เช่น
และขอให้พึงคิดเอาไว้ว่า การอัพเดทเรซูเม่ จะต้องคิดถึงตำแหน่งงานที่สมัคร และผู้อ่านเรซูเม่เป็นที่ตั้ง เขียนเรซูเม่ให้ไปในแนวทางที่ว่า คุณสามารถใช้ความรู้ความสามารถของคุณในตำแหน่งงานที่สมัครได้อย่างดีเยี่ยม ประสบการณ์ของคุณมีค่า และสามารถสร้างมูลค่าให้กับบริษัทได้ ถ้าคุณเจอเรซูเม่แบบนี้ คุณก็อยากรับเขาเขาทำงานใช่ไหม
มืออาชีพทางด้านการหางาน สมัครงาน ได้ใช้ประสบการณ์ทั้งชีวิตคลุกคลีอยู่กับการหางานให้ลูกค้าของตัวเองนั้น ได้คิดค้นกฎ 20-80 ขึ้นมาเพื่อแนะแนวทางในการหางานสำหรับมือใหม่เอาไว้ดังนั้
20% ของเวลาในส่วนแรก ให้คุณใช้เวลาในการหางานในเว็บไซต์รับสมัครงานต่างๆ เลือกงานที่ตัวเองสนใจ อ่าน job description ให้ดี และดูว่าตัวเองมีคุณสมบัติครบถ้วนไหม เลิกสมัครงานแบบหว่าน ๆ โดยที่ตัวเองคุณสมบัติไม่ถึงเสียที
80% ของเวลาในส่วนที่เหลือ ใช้ไปกับการเตรียมตัววางแผนการสื่อสารกับ HR เพื่อให้ได้งาน นี่รวมไปถึงการให้เวลาส่วนมากไปกับการเขียนเรซูเม่ที่ดี และวางแผนเตรียมตัวการสัมภาษณ์งาน
ถ้าคุณยังไม่แน่ใจนักว่าการเขียนเรซูเม่ที่ดีเป็นอย่างไร คุณสามารถใช้เครื่องมือช่วยเขียนเรซูเม่ได้ที่ www.resume.in.th ซึ่งคุณจะทำเรซูเม่ที่มีประสิทธิภาพดีขึ้นมาได้ในเวลาอันรวดเร็ว นอกจากนี้คุณยังสามารถกลับเข้าไปอัพเดทเรซูเม่ของคุณได้อย่างง่ายดายอีกด้วย
เพียงเท่านี้ คุณก็น่าจะได้ไอเดียในภาพรวมแล้วล่ะ ว่าคุณจะต้องอัพเดทเรซูเม่ของคุณบ่อยขนาดไหน และเมื่อไหร่เป็นเวลาที่ดีที่จะอัพเดทมัน จากนี้ไปก็แค่ขอให้คุณทำตามเทคนิคที่เราเพิ่งแนะนำไปก่อนหน้านี้ให้ครบถ้วน ค้นคว้า ค้นหา และอ่านให้ทะลุ หลังจากนั้นใช้เวลาวางแผนการเขียนเรซูเม่และการสัมภาษณ์งาน เพียงแค่นี้ งานในฝันงานไหนคุณก็สมัครได้ไม่มีพลาดทั้งนั้น
#resume #job #career #employment #jobs #hiring
If you are a human resources manager, your job is obsolete — you are no longer needed. It is only a matter of time before you are shown the door. Automation in human resources — the process of enhancing the efficiency of the HR departments by freeing employees from tedious manual tasks — is already here.
Human resources management — or rather, human-managed hiring and onboarding — is dead. I don’t know the exact date; but what I am sure about is that one day they dragged it out of its cubicle; took it the back of the building, near the loading docks, and put a metaphorical bullet in its skull.
They then dumped its body in a recycling bin, along with decommissioned 80386-based PCs, daisy wheel printers and boxes of un-opened DynaTAC 8000X “mobile” phones. After years of faithful service, it met its end by being unceremoniously dumped like yesterday’s coffee.
My apologies for by bedside manners, but Automation in human resources — the process of enhancing the efficiency of the HR department by freeing employees from tedious manual tasks — is already here.
“Kitty couldn’t fall asleep for a long time. Her nerves were strained as two tight strings, and even a glass of hot wine, that Vronsky made her drink, did not help her. Lying in bed she kept going over and over that monstrous scene at the meadow.”²,³
If you are a human resources manager, your job is obsolete — you are no longer needed. It is only a matter of time before you are shown the door, that is, if you have not been fired already. Do not be shocked. This was a long time coming and you saw the first signs long ago — you just refused to believe you were next.
Allow me to explain: a recent study¹ on the changing roles of the Human Resources professional found that 50% of respondents believed that traditional Human Resources functions would, “…continue to grow into a more strategic function as administrative responsibilities are automated or outsourced to others…”
In early 2008, newspapers around published a story that created a minor buzz. It claimed that a Russian publishing house — Astrel SPb — was releasing a book completely written by a computer.
The book was to be called True Love and was a variation of the classic novel Anna Karenina⁴ written in the style of Haruki Murakami⁵. It was widely reported the publisher claimed a group of Russian developers and philologists collaborated to create a computer program that generated the manuscript.
In early 2016, Nishad Nawaz, a researcher at The Kingdom University in Bahrain published a paper⁶ titled, Automation of the HR functions enhance the professional efficiency of the HR Professionals-A Review. In the abstract, Mr. Nawaz states,
“…In many organizations, the human resource department is responsible for many strategic tasks from managing the hiring to [the] termination of employee[s], for example monitoring of employees’ at all the levels, handling payroll, managing employee[s’] benefits and so on. To make this work easier[,] organizations across the world are investing in HR automation [to] [carry] out the best human capital decision[s]…”
#human-resources #machine-learning #automation #jobs #employment #hr-by-spreadsheet #hr-by-algorithm #hackernoon-top-story
Faced with a fast-spreading disease, a slew of misinformation and unclear guidance from all levels of government, the national economy contracted as businesses temporarily or permanently shut down. It is apparent that **certain sectors of the economy **have been hit differently by the response to the virus, and this article will explore employment trends from January to June 2020 for three cross-sections: state, race, and industry.
Data for this analysis was retrieved from the Bureau of Labor Statistics. You can download the data and check out a python notebook at my github repo.
This notebook describes several examples of queries to the US Bureau of Labor Statistics API and cleans the data into a…
Some states have been slower to recover from the knee-jerk job market crash that followed the outbreak of COVID-19. Nevada, who has almost 300,000 workers in the hospitality and leisure industries, saw unemployment hit 30 percent in April 2020.
#covid19 #economics #data-visualization #employment #economy #data visualization
In response to Covid19 governments all over the world scrambled to enact policies to protect the health of their citizens. These policies were primarily directed towards mitigating the health crisis, but they used a rapid rise in unemployment almost everywhere in the world. Governments that did not pre-empt this crisis in employment were forced to enact additional policies to mitigate the employment crisis.
In this project_ by Omdena, we used publicly available data to look at countries around the world and analyze their vulnerability to the employment crisis caused by the COVID-19 pandemic._
Figure 1. Context of the problem: The COVID-19 pandemic caused governments to enact policies to mitigate the health crisis, which led to (in many countries) an economic crisis as well; In many cases, governments were forced to enact additional policies.
In order to analyze the situation in a particular country, and be able to compare policies between them, the University of Oxford released a dataset comprising the government responses by country, updated on a daily basis, and showing the changes in three main groups of policies: Containment and closure, economic response or fiscal measures, and health-related policies. 
Among the Confinement policies, there are three specific ones that, according to the ILO , have had the most impact on the world of work. These three policies are:
● C2 (Workplace closedown)
● C5 (Closedown of public transportation)
● C7 (Restriction in the internal movement of citizens)
Each of these have different stringency values, ranging from 0 (no enactment of restriction) to 2 (or 3 in the case of C2) being the highest level a mandatory closedown with few exceptions. With these levels in mind, countries are categorized according to the level of lockdown they have.
The categories are:
Full lockdown: All three policies are in mandatory closing.
**Partial lockdown: **At least one of the policies is mandatory (the rest are working either just on a recommend closing level or are not enacted).
Weak lockdown: None of the policies is mandatory.
The Stringency of the policies applied by a country determines a stringency index, which is calculated by the creators of the policy dataset .This index goes from 0, which is no policies being taken, to 100, meaning the country is at its maximum stringency and lockdown level. This index is updated on a daily basis, creating what we refer to as the Stringency curve.
Since this article focuses on those populations that have a higher vulnerability regarding unemployment during the COVID-19 crisis, some indicators need to be introduced that help represent the vulnerability of the population within a country. The indicators considered here are the following.
● **High Impacted Sector Exposure. **The 14 economic sectors considered by the ILO, have been aggregated into 5 categories according to the level of impact that the current economic crisis has brought to them in particular (measured from real-time and financial data for each sector from the ILO). (Source: ILO Monitor. COVID-19 and the world of work. Second Edition ). From this, high-impact sectors are those that lie on the most negatively impacted side of this scale (these are Accommodation and food services, Real estate, Manufacturing, and Wholesale and retail trade). It is worth mentioning that other sectors, like Education and Agriculture, are also impacted by the crisis, but in general to a lower extent than those mentioned above.
● **Inequality-adjusted Human Development Index (IHDI). **The IHDI is an indicator available currently for 150 countries, that takes into account the country’s average achievements in health, education, and income. Furthermore, it weights these three dimensions with their distribution across the population (their level of inequality). It can, therefore, be considered as a general measure of the resiliency of each country to adverse effects on health, education, and income. (Definition is taken from the United Nations Development Program ).
● **Informality Rate. **The informality rate is the percent of people participating in the informal economy, out of the total labor force of that country. There are several criteria defined by the ILO to consider an employee as being an informal worker, such as; No contributions to social security and not an entitlement of a worker to paid annual leave and or sick leave. (Source: ILO. Women and Men in the informal economy: A statistical picture ).
● **Stringency Index. **This index measures the severity of implementation of the affecting policies, as mentioned before.
In order to be able to jointly use these variables to compare across countries, we define two indicators:
**Impact Weighted Informality Rate: **% share of high impact sectors * Informality rate
This indicator aims to define vulnerability in terms of the exposure of a country to the most adversely affected economic sectors and the share of workers in the informal economy that are likely affected by those adverse effects. The higher the value of this indicator, the higher the vulnerability.
**Impact Weighted IHDI: **% share of high impact sectors *(1-IHDI)
This indicator aims to define vulnerability in terms of the exposure of a country to the most adversely affected economic sectors and the resilience of that country to those adverse effects. The higher the value of this indicator, the higher the vulnerability.
Considering the indicators of the vulnerability described above, we dive into how these look on a global and then on a regional basis.
In Figure 2, countries that appear colored are those that, as of May 1, 2020, are reported to be under full lockdown measures by the Oxford policy database, based on the definition provided above. Furthermore, these countries have little or no income support from their governments (meaning less than 50% or no loss of wage is being compensated by government financial support). As of May 1, 2020, most governments globally have attained a plateau in their stringency curve, which means most of the strict lockdown measurements are already enacted and have been for at least a month in most countries. In figure 2, the color of the country represents its IHDI index, and the intensity of it represents the share of the population working in highly impacted sectors. The more intense (darker) the color, the higher the people that work in sectors like Accommodation, Manufacturing, Real Estate, or Retail, which are those most severely affected by the crisis.
Figure 2. Countries under full lockdown measures (as of May) with little or no income support colored by IHDI index. The intensity of color represents the share of its population working in highly impacted sectors.
The scatterplot in Figure 3 shows the same countries shown on the map, describing on the x-axis the share of the population in highly impacted sectors, and their stringency index on the y-axis. The sizes of the circles in the scatterplot represent the joint indicator **Impact Weighted Informality Rate **defined above. The following table represents the top 15 countries ranked by both this indicator and the Impact Weighted IHDI, in descending order of value (vulnerability).
#vulnerability #employment #data-analysis #covid19 #informality #data analysis
About six months ago, Eddie Kirkland was itching to make a change in his life.
At that point, he says, he’d taken “a really strange career path.” His degrees were in management and theology. His resume consisted of nonprofit work, quite a bit of time as a working musician, and most recently six years as the head priest at a church.
Back in college, Eddie remembered, he’d enjoyed his study of statistics. So he started looking into statistics-related careers. “Lo and behold,” he says, “[I found that] there’s this massive data science world that has opened up since I left.”
“As I started exploring it, I thought ‘I think I have enough of a foundation to kind of understand the concepts.’ There’s a lot of skills that I knew I needed. [But] even though my degree is in management and my graduate degree is in theology, I think I might actually be able to make a really cool path here with some online learning.”
He also knew that he had an unusual opportunity. Having taken a sabbatical, he had some time off from full-time work. “I thought to myself, I’m never going to have this chance again,” he says. “If I’m going to quit my job and change career paths, I need to know if this is really what I want.”
When he started looking for the best data science MOOCs and online courses, “Dataquest came up as one of the top results.” But having no experience with programming, he still wasn’t sure how he’d feel about the programming-heavy curriculum.
“I basically went into it thinking okay, I’m going to try this and I’m just going to see,” he says. “It sounds fun, but it could be terrible, so let me try it and see if I still like it a month from now.”
And because Dataquest offers a lot of free content, trying it was easy. “I loved that there were a few free modules that kind of helped me get my feet wet,” Eddie says.
It wasn’t long after getting his feet wet that Eddie was diving in head-first. “Within the first month and a half I had purchased the full version and I was up ‘till 2 in the morning doing Python code and just loving it. Like absolutely loving it. Nobody is making me do it. It’s all on my own time, and I just really, really loved it.”
#student stories #data engineer #eddie kirkland #employment #full time #data analysis
According to the new jobs data, the US Bureau of Labor Statistics finds that 13% of Americans are unemployed and, while that overall number represents significant hardship, the problem extends even deeper in some areas of the workforce . Compared to this time last year, some occupations like “computer and mathematical occupations” have risen from 1% to 4% unemployment but others like “food preparation and serving related occupations” skyrocketed from 5% to a crippling 37% unemployment (05/19 vs 05/20) . Nobody is doing well but some parts of the economy are worse off than others. In fact, due to the shape of that unemployment, inequality may be amplifying COVID’s economic impact. Therefore, recovery may require understanding not just_ how man_y people lost their jobs but_ wher_e the impact is deepest within the workforce.
Link to full image. In May 2020, unemployment had gone up in every major BLS occupational group compared to the year prior (showing non-seasonally adjusted rates but comparing years). The pre-pandemic wages of those professions and their uninsured rates may also prove crucial during this recovery.
First, occupations with the largest increases in unemployment are also those with lower incomes. The data show a significant negative correlation between an occupation’s increase in its unemployment rate (compared to the prior year) and its typical (pre-pandemic) median income (p < 0.05; Spearman’s rank-order; unemployment compared 05/19 to 05/20) . In other words, the parts of the workforce with less financial resources needed to prepare for or to cope with an event like this may also be the same areas seeing the largest economic pressure in terms of elevated unemployment. Considering that a full 39% of Americans already couldn’t comfortably handle an unexpected $400 expense even before the pandemic, the possibility that COVID’s economic impact is falling hardest on those with the least financial resources suggest that many are confronting severe hardship and possible debt .
#data #economics #data-science #inequality #employment #data analysis