Saturday, May 30, 2020

Answer for Null value in a field specified to have no default?

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Hi Mike!

Thanks for reaching out.

Please refer to the last queries presented here.
https://365datascience.teachable.com/courses/360102/lectures/5528906

Hope this helps but please feel free to get back to us should you need further assistance. Thank you.
Best,
Martin




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Answer for Need help but have not gotten any answer.

https://365datascience.com/dwqa-answer/answer-for-need-help-but-have-not-gotten-any-answer/ -

Hi Mike!

Thanks for reaching out.

Please accept our apologies for the delayed response.

Can you please provide a link to the question you are referring to? I would like to redirect your question to the person who will provide the best and soonest response. Thank you.

Hope this helps.
Best,
Martin




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Answer for SQL exercise error

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Hi Ethan!

Thanks for reaching out.

Please refer to the following thread where this question has already been answered. Thank you.
https://365datascience.com/question/error-code-1452-cannot-add-or-update-a-child-row-insert-statement-exercise/

Hope this helps.
Best,
Martin




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Answer for How to Delete Duplicate record under SQL

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Hi Oshin!

Thanks for reaching out.

In a nutshell, the structure to use is DELETE FROM table_name WHERE condition;.

Please refer to the SQL DELETE Statement section for more information.

Hope this helps but please feel free to get back to us should you need further assistance. Thank you.
Best,
Martin




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Friday, May 29, 2020

Answer for Two sum code

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Hi Thadathon, 

thanks for reaching out!

An ‘else’ statement is not necessary in the Python. This is a particular feature of the Python programming language. Python strives to have clear syntax and be easy to use by programmers, which includes avoiding what are deemed unnecessary words or lines of code. In other languages, such as Java and C++ you need the ‘else’ statement.

But as there is no ‘else statement’ you need to indent your code precisely, so the compiler knows to which statement the ‘return’ is referring. So, if the ‘return’ is connected to the ‘for‘ statement from above, it needs to be indented with the same amount of white space. 

 

Best, 

Eli 




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Answer for Opening a ipynb file on my computer

https://365datascience.com/dwqa-answer/answer-for-opening-a-ipynb-file-on-my-computer/ -

Hi Rashad, 

thanks for reaching out! You need to install Python and Jupyter. You can check out the step by step process for installing Anaconda in Windows here:
https://365datascience.teachable.com/courses/239366/lectures/3736872

 

Best, 

Eli




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Thursday, May 28, 2020

Data Scientist Job Descriptions 2020 – A Study on 1,170 Job Offers

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data scientist job descriptions 2020, data scientist job offers 2020


Data Scientist Job Descriptions 2020


The market for Data Science has been growing extensively over recent years. As a result, the position of data scientist has emerged as a truly attractive career path option with an abundance of rewarding job opportunities.


So, to help you stay at the forefront, we’ve conducted an in-depth study on job offers in the field of data science.


And in this article, we’ll share our insights based on 1,170 data scientist job descriptions in the USA. We’ve extracted valuable information about the companies offering the position, the required educational credentials, and sought-after work experience, as well as the desired skills and techniques involved.


So, let’s explore the intriguing findings together, shall we?


Data Scientist Job Descriptions 2020


What companies were targeted in the research?


The 1,170 data scientist positions in our study were posted by 357 unique companies. This is a positive sign, as:


  • The presence of many different companies means the data is more likely to be a random sample of the market and, therefore, not biased towards the requirements or needs of a single or few companies.

  • This also shows that the website is an active and popular job openings aggregator.

That being said, let’s take a look at the distribution of offers against the size of the company making the offer. Here’s a chart of the number of openings posted by companies with their respective number of employees:


data scientist job descriptions, number of data scientist job offers


It’s easy to see that the majority of job offers come from very big companies, with more than 10,000 employees. This could significantly skew our data towards the necessities of big corporations. However, looking beyond that, 823 of the total 1,170 job offers were posted by companies that didn’t actually have a profile on the website. Therefore, their size hasn’t been determined and is not present in the chart.


With that in mind, we can assume that bigger companies tend to register on more employment websites, while their smaller counterparts do not engage as much. So, these 823 offers could have been made by companies small enough to not register.


But what about the offers themselves? Let’s analyze this!


What are the locations of the data scientist job descriptions in our study?


The data scientist job descriptions we studied originated from 38 states in the US. Here are the top 12: California, Virginia, Washington, New York, Massachusetts, Maryland, Texas, Colorado, Michigan, Ohio, New Jersey, and Florida.


data scientist job descriptions, location of data scientist job offers


And here are the same states highlighted on a map:


data scientist job offers, top 12 states


Now that you have a good idea about the top states by number of offers, let’s move on to the job requirements.


What is the required education in data scientist job descriptions in 2020?


When it comes to education, 544 job offers stated that they require at least a Bachelor’s degree, 367 – a Master’s and 50 were looking for a Ph.D. While at the same time in 209 job offers, the level of education was not stated.


As for the preferred fields of study, here are the results. We collected the data by extracting only the first three mentioned fields. Data science takes the lead, followed by Statistics, Mathematics, Computer Science, and Engineering. IT, Economics, and Physics are much less popular, according to the numbers.


Author’s note: If you have a degree in Computer Science or Economics and you want to learn how to make the switch to data science, check out our dedicated blog posts on Transitioning to Data Science from Computer Science and Transitioning to Data Science from Economics.


data-scientist-job-descriptions, required education


What is the required work experience in data scientist job descriptions in 2020?


We set the years of experience in these 2 categories: ‘years of experience as a data scientist’ and ‘general work experience.’ Bear in mind that in most job offers, general work experience should be in a related field.


What we found out is that on average companies demand that candidates have at least 4.2 years of previous experience as a data scientist and 5.2 years of experience in related fields.


data scientist job descriptions, data scientist work experience


Which are the required programming languages in data scientist job descriptions in 2020?


Here are the most quoted programming languages in the 1,170 job offers (there may have been more than one language per offer):


data scientist job description, programming languages


No big surprises here – Python is the most popular one, as expected, followed by R and SQL. The other languages with a significant number of mentions are Scala, Java, and C++.


What are the most cited skills and machine learning techniques in data scientist job descriptions?


We also performed a keyword analysis on the description of the job offers and extracted the most cited skills and machine learning techniques.


data scientist job descriptions, most quoted skills and machine learning techniques


As expected, the most important skills to have are Machine Learning, Statistics, and Python programming, while the most in-demand machine learning techniques are Deep Learning, Clustering, and Natural Language Processing (NLP).


Which are the most quoted database/cloud skills, and data visualization techniques in data scientist job descriptions?


The last parameters we extracted were Database/Cloud skills, data visualization techniques, and whether there is an emphasis on communication or not.


The numbers show you should definitely consider adding Spark, AWS, or Hadoop to your data scientist toolbelt.


Regarding data visualization, it all comes down to Tableau and Power BI. Tableau was mentioned in 228 job offers, whereas Power BI in 79.


database and cloud skills


Are communication skills of major importance in data scientist job descriptions?


That was true in 368 offers, while in the rest 802 there was no mention of communication or teamwork at all.


communication skills


Now, let’s analyze the prior work experience with respect to the education required in data scientist job offers.


Here is what we found:


required work experience and education


As you can see, there is no real significant difference between the preferred work experience for the different degrees. However, there are two very important factors to consider here:


  • The sample size is not large, especially for the Ph.D.

  • This data applies to candidates with a degree that is the minimum requirement. In fact, there were no job postings that did not require university education. As in any other industry, holding a Ph.D. lowers the minimum required experience. However, not dramatically so, especially having in mind that a Ph.D. takes several years to complete.

So, let’s look at how the company size affects the experience required in data scientist job offers.


company size and work experience


For this analysis, we have grouped the companies into 5 categories: small (1 – 100 employees), medium (100 – 1,000 employees), big (1,000 – 10,000 employees), sizeable (10,000+ employees), and those with No size data. It is very important to remember that the sample size here is rather small.


Quite surprisingly, it looks like the smallest companies have the highest requirements for experience. Apart from the sample limitation, we can assume that smaller companies have a limited number of employees. So, to expand and become successful, it needs more experienced professionals. The sizeable companies, in contrast, may not necessarily need an experienced individual but someone they can train to become a useful tool for the company in the future.


And we’ve arrived at the last piece of analysis – what companies of different sizes require as a level of education in data scientist job descriptions.


company size and level of education


Due to the small samples, we have decided to summarize the data in a table, rather than a graph. Smaller companies don’t really look for Ph.D.s and prefer Master’s degree holders. At the other end of the spectrum, the bigger companies have somewhat more balanced requirements with an approximately equal number of positions asking for either a Bachelor’s or a Master’s degree.


That was our compelling look at a sample of 1,170 job offers for the position of data scientist.


We hope you will find this information useful and advantageous for you in your path to landing your dream data scientist job.


Ready to take the next step towards a data science career?


Check out the complete Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses. Build up a step-by-step experience with SQL, Python, R, Power BI, and Tableau. And upgrade your skillset with Machine Learning, Deep Learning, Credit Risk Modeling, Time Series Analysis, and Customer Analytics in Python. Still not sure you want to turn your interest in data science into a career? You can explore the curriculum or sign up for 12 hours of beginner to advanced video content for free by clicking on the button below.


 


 



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Answer for ...confusion

https://365datascience.com/dwqa-answer/answer-for-confusion/ -

Hi Luigi,

Regression analysis is sometimes considered machine learning, other times as a ‘less complicated’ method. 

In the deep learning part we show how to write a regression from scratch. This is how all linear regressions work behind the curtains.

Note that each regression package basically implements the same methodologies and incorporates very similar code to what we show in these lectures!

Best,
The 365 Team




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Answer for Regarding for Data Science Project Ideas

https://365datascience.com/dwqa-answer/answer-for-regarding-for-data-science-project-ideas/ -

Hi Ankit,

Thanks for reaching out.

What do you mean by: “Can anyone now more the latest project name?”. Please elaborate a bit more so we can support you better!

Best,
The 365 Team




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Answer for how do i open a .ipynb file ? what softwares am i suppose to install to complete the whole course ?

https://365datascience.com/dwqa-answer/answer-for-how-do-i-open-a-ipynb-file-what-softwares-am-i-suppose-to-install-to-complete-the-whole-course/ -

Hi Jorge,

In order to work with .ipynb files, you need to have Python and the Jupyter Notebook installed. 

Please refer to this lecture here: https://365datascience.teachable.com/courses/239366/lectures/3736872

Best,

The 365 Team




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Answer for random forest/decision trees

https://365datascience.com/dwqa-answer/answer-for-random-forest-decision-trees/ -

Hi there,

Unfortunately, so far we have not developed any content on random forests. That said, we hope that soon these will be available, too!

Best,

The 365 Team




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Answer for Different dataset for credit risk modelling course

https://365datascience.com/dwqa-answer/answer-for-different-dataset-for-credit-risk-modelling-course/ -

Hi Kunjan,

You can find all the datasets used in the course over here: https://www.dropbox.com/sh/6cc01fcljk457gd/AADD_MJFfVcE5VR3UzlKDATla?dl=0

Best,

The 365 Team




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Answer for Histograms are not displayed in Excel 2016 (german version)

https://365datascience.com/dwqa-answer/answer-for-histograms-are-not-displayed-in-excel-2016-german-version/ -

Hi Robert,

Are you sure you are using the Excel 2016? These graphs have been developed precisely on Excel 2016.

Best,

The 365 Team




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Answer for loan_data_input_test, loan_data_input_train, loan_data_target_train, loan_data_target_test dataset

https://365datascience.com/dwqa-answer/answer-for-loan_data_input_test-loan_data_input_train-loan_data_target_train-loan_data_target_test-dataset/ -

Hi Kunjan,

I’d be happy to troubleshoot the error for you. Could you share more details about that?

The 365 Team




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Wednesday, May 27, 2020

Certificates

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Tuesday, May 26, 2020

How to Write a Winning Data Science Cover Letter (2020)

https://365datascience.com/data-science-cover-letter/ -

how to write a data science cover letter, how to write a winning data science cover letter, great data science cover letter, perfect data science cover letter


“Why do I need to write a data science cover letter?”


Even if it isn’t listed as a prerequisite, a data science cover letter can still be a vital step in your job application process.


A well-crafted data science cover letter has the power to distinguish you from the crowd. It speaks volumes about you as a professional. What’s more, it creates context around your resume and lets the potential employer see beyond the bulleted lists with qualifications and accomplishments.


If written properly, a data science cover letter gives insight into your personality and shows how you’ll fit the company’s team and culture. But, most importantly, it gives you a chance to address a company’s pain-point and demonstrate you have what it takes to offer a solution. And that’s precisely what can move your application to the top of the pile.


So, how to write a successful data science cover letter?


This thorough data science cover letter guide will help you build a cover letter to land a job in data science from scratch. It will take you through all the necessary steps:


  • initial research and target job deconstruction;

  • sections and content of a data science cover letter;

  • data science cover letter formatting;

  • tips and mistakes to avoid when writing your data science cover letter.

So, quit staring at a blank page wondering what to write. It’s time to call your great storyteller alter-ego and let’s get down to it.


How to Write a Cover Letter for a Data Science Job?


A great data science cover letter should convey that you’re the perfect fit for the company. But you can’t create that impression if you’re not an expert on your target company first, right? So, before you start writing, here’s the initial step you need to take.


Research the company


That’s a must for any job candidate. But it’s especially important in the data science field. Why? Because data science serves a lot of industries. So, you must be informed about how things go down in a variety of businesses, be it security, stocks trading, food innovation, or consulting. Moreover, you should be familiar with their main competitors on the market and the technology your target company uses. And yes, there are clever ways to incorporate this knowledge into your cover letter. (And we’ll discuss how to do that to score yourself some extra points later in the article.) Fortunately, there are plenty of places where you can find all the information you need.


Why Start with the Company’s Website?


The company’s website is the first place you should look. Because it is all in there – services, projects, product descriptions, news… And of course, the “About Us” page and the company’s Mission Statement, where you can find out more about the company culture and their core values. So, here’s a quick tip: just learn these by heart. Then make sure you mention some central details in your data science cover letter. That’s how you’ll prove your avid interest in this particular job opportunity at this company. After all, no employer wants to be just one out of many.


Example:

Spoonshot’s constant commitment to leveraging AI technology to help solve the F&B industry biggest challenges is why I’m so excited to apply for this position. My 3 years of experience in the food industry and my passion for data-driven research for answering hard questions with data have always driven me forward in my ambition to develop novel techniques to understand food data and build applications to address business problems.


Stackshare, G2 Track or similar sites:


Stackshare and G2 Track are crowdsourced platforms where companies’ team members share the technology they use in their workplace (Including top Fortune 500 companies like Amazon and Walmart). You can explore the company profile to find the application and data tools, utilities, devops, and business tools it employs on a daily basis. And, if you’re proficient in any of those, definitely add it to your data science cover letter.


Example:

With 3 years of experience in Tensorflow and Pytorch, I am confident I will be an excellent fit for Cinnamon’s next AI Research Engineer. My hands-on experience in infrastructure construction (AWS, GCP, Docker) and understanding key Machine Learning concepts has provided me with the innovative and technical skills necessary to successfully provide your company with appropriate technical solution approach to client issues.


Social media


Don’t forget to check out the company’s LinkedIn, Instagram, Facebook, and Twitter. These will get you up to speed with their latest projects and upcoming initiatives. Moreover, it might also give you a sneak peek into some recent team events and help you a sense of what the company culture is like.


Annual report


If you’re applying for a job at a publicly traded company, their annual report is a real gold mine. That’s where you can get an insider’s look at the industries the company is involved in, their business segments, a management’s discussion and analysis (MD&A) of the business financial condition… and even results over the past couple of years. You can also find the list of the board of directors, and executives, along with their occupations. In addition, the annual report shares details about their product lines, operating locations, and project leads. Not only is that a bonus for your cover letter, but it will also inform your data science interview preparation.


Read through the job description to tailor your cover letter.


This is super-important. Similar to your resume, you should target the data science position you’re applying for. This doesn’t mean you should go overboard with self-praise. Just tie your skills and education to the company’s business goals, or to a pressing issue you believe you can solve.


Example:

I know that HEALTH[at]SCALE’s current plans involve designing and implementing new predictive machine learning and artificial intelligence algorithms to improve outcomes and economics of care. This project is a perfect match for my interests and an exciting opportunity to identify and formulate analytical problems underlying major healthcare challenges and match the world’s patients to the best treatments possible. I would be happy to leverage my knowledge of machine learning, optimization toolkits, Python, and R to achieve groundbreaking results with this initiative. 


How to organize a data science cover letter?


This is the most essential part of writing a great data science cover letter. Your cover letter must be coherent and impeccable. Each paragraph should be well-thought-out to serve a particular purpose. So, you need an opening directed to the right person, an introduction that creates interest and curiosity, body paragraphs that bind your qualifications and skills to the company’s targets and plans for development. And, last but not least, a strong closing paragraph with a must-have call to action. Continue reading…


How to format a data science cover letter?


Formatting can speak louder than words. Therefore, a clean and stylish cover letter consistent with your resume exudes professionalism and a serious approach to the job application process. Luckily, there are simple rules you can follow to create an elegant and sharp cover letter. Continue reading…


Data science cover letter tips and mistakes to avoid


There are certain things that can make or break a cover letter. By all means, your resume should be succinct; explain what you bring to the table; and underscore the strong sides of your personality. But what are the other do’s you should strive to have? And, more importantly, what mistakes should you steer clear of? Continue reading…


Ready to take the next step towards a data science career?


Check out the complete Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses. Build up a step-by-step experience with SQL, Python, R, Power BI, and Tableau. And upgrade your skillset with Machine Learning, Deep Learning, Credit Risk Modeling, Time Series Analysis, and Customer Analytics in Python. Still not sure you want to turn your interest in data science into a career? You can explore the curriculum or sign up 12 hours of beginner to advanced video content for free by clicking on the button below.



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Data Science Cover Letter Dos and Don’ts

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data science cover letter dos and don'ts , dos and don'ts in a data science cover letter, data science cover letter tips


A well-thought-out cover letter can give your job application a powerful edge, especially in the highly competitive field of data science. But what are the most crucial data science cover letter dos and don’ts?


In this article, we list the must-have ingredients of a winning data science cover letter, and the common pitfalls you should avoid at all costs.


Let’s get right down to it.


What Are the Data Science Cover Letter Dos?


Here’s our top 10 of data science cover letter dos you shouldn’t skip:


1. Mark how the hiring manager wants to receive your data science cover letter


If the company request data science cover letters as attachments, there will be no significant changes required on your part. Just convert your work into a suitable file format and upload where designated. However, if the hiring manager has specified that your cover letter should be sent as the body of an email, consider the following:


  • Compose a professional subject line with the job you’re applying for, your name, and the job number (if there’s any listed in the job ad), e.g. Data Analyst-Mike Green-4351;

  • Move your contact information to the bottom of the letter, right below the sign-off. Headers work great for attached CVs but it doesn’t make much sense to use them in an email.

2. Tailor your data science cover letter to the specific job ad


This applies to each and every job posting you take a shot at. So, if you’re serious about getting a specific job, be specific. Explain how your skills and qualifications will contribute to the well-being of the business. Research your target company and make sure your cover letter answers their goals and needs. And focus your content around why they’ll benefit from hiring you, instead of falling victim to irrelevant self-praise. (The latter will most probably backfire).


3. Use a powerful first sentence


The first sentence after the greeting can make or break your data science cover letter. So, make sure you start it in a memorable way. You can open your data science cover letter with an impressive achievement of yours, or by directly addressing an employer’s pain-point and how you can help resolve it. Of course, if you were referred by someone who works there, definitely mention that in the beginning. An advanced tactic you can use is to research the hiring manager themselves and stroking their ego by sharing your admiration for their own achievements. In addition, your data science cover letter can only win if you make enthusiasm a recurring theme in your writing style.


4. Demonstrate excellent writing skills


Before you can actually speak to the hiring manager, it’s your writing that does the talking. View your data science cover letter as a means to prove your great communication skills  – keep the tone direct and professional. However, it’s better to avoid complex words – they will only weigh down your content. So, keep your phrases focused and friendly, just like you.


5. Use keywords from the job posting


Highlight the most important skills, experience, and education in the job description and include as many of those keywords as possible. Not at the expense of honesty, of course. This will help your data science cover letter pass the ATS (Applicant Tracking System) check. Moreover, it will help you score high with your potential employer.


6. Great layout


When it comes to layout, good is never good enough. Strive for perfection – choose the same style you used in your resume; select an elegant, easy-to-read font; aim for a single-page length; make spacing work to your advantage, along with margins and alignment; and make sure everything stays in place with a compatible file format.


7. Use the cover letter to straighten out red flags


Your data science cover letter gives you a chance to get a handle on red flags, such as employment gaps or lack of relevant degrees in advance.


You can use your narrative to your benefit by briefly mentioning the reason why you were out of the workforce for a few months or years. For example, maybe you traveled extensively or needed to stay at home for family reasons.


Regarding lack of required education, emphasize your practical experience and bring forward the transferrable skills that make you the best fit for the job, despite the lack of a shiny degree.


8. Quantify, quantify, quantify


It isn’t flattery but metrics that will get you anywhere, especially when it comes to your projects and the business goals they contributed to. Be your own private eye and apply measurable evidence to every accomplishment you decide to include in your data science cover letter.


9. Finish off with a concrete intention to follow-up


Show that you value your time by making your intentions clear. There’s nothing wrong with writing that you’ll call the hiring manager next Wednesday to discuss a possible interview with them (just make sure you keep your promise).


10. Proofread


Ernest Hemingway said, “After you write, read”. But in the context of composing a data science cover letter, that would sound more like: “After you write, proofread”. A single spelling mistake can send your cover letter straight to the trash folder. So, make sure you spend just as much time checking, as you did writing.


What Are the Data Science Cover Letter Don’ts?


Is there’s anything more important than what you include in your data science cover letter? Yes – what you leave out.


Here are 10 examples of data science cover letter don’ts you should steer clear of:


1. Don’t write a memoir


A single one-sided page with up to 400 words of strong content is all you need for an impactful cover letter. Omit any details that you already stated on your resume. Your cover letter should indeed support the content of the resume. But it also has a story of its own. And that should be a brief and strategically planned story that highlights your personality and relevant accomplishments.


2. Don’t exaggerate or try to sweet talk the hiring manager


As mentioned, don’t write anything that you can’t back up with relevant metrics. Rest assured, empty claims won’t make the right impression.


Also, it’s good to show that you know the business of the company and you appreciate their success. However, go easy on the compliments. Try to balance things out, or you risk sounding fake.


3. Don’t copy your resume


A well-crafted data science cover letter can put you way ahead of the competition unless it’s a copy-paste. Your cover letter is your resume’s sidekick that matches its style and adds to its superpower (or lends a helping hand where your resume lacks in context).


4. Don’t snatch decisions from the hiring manager


If there’s one sure way to aggravate a hiring manager, that would be making a decision instead of them. Don’t write things like “I’m sure you’ll see I’m the best candidate for the job”. The goal of your data science cover letter is to prove that you are the perfect fit, not to show off and hijack the role of a decision-maker.


5. Don’t sound needy


Remember, the company needs you because you are a great data science professional. Never state that you are in a tight spot financially or that you want the job, so you can enhance your relevant experience on your resume.


6. Don’t use buzz words and data science slang


It’s good to show that you know your stuff. However, in your cover letter, as on the job, you’ll communicate with non-technical executives and coworkers all the time. And chances are that the hiring manager you’re addressing doesn’t have the advanced technical background that you have. So, be professional and avoid tech slang that would leave anyone but people on your team perplexed.


7. Don’t mention salary expectations


…because that would be getting ahead of yourself. It’s not only redundant this early in your job application process, but it could cost you the interview (where you’ll get that question anyway). And you can never win – if you state a lower number, you will lose your chance to negotiate a higher paycheck. State a higher number, and you may never get that interview invitation you’re hoping for.


8. Don’t get too personal


It’s true that your cover letter is about creating a narrative about yourself, and that doesn’t exclude adding a little something from your personality. But limit your examples to those that serve the purpose. Namely, showing that you have the necessary character traits and experience to meet the expectations for this role. The hiring manager isn’t interested in where you spend your family vacation last summer (unless during that vacation you built a machine learning algorithm that increased your current employer’s revenue by 25%).


9. Don’t forget to match the cover letter format to your resume


The devil is in the details.  And using the same style in both your cover letter and your data science resume is a detail you don’t want to miss. You’ve spent precious time writing the best content possible, so why would you risk it going unnoticed? After all, your job application’s cohesive look demonstrates professionalism and elegance at first glance. And very often, that’s all it takes for a hiring manager to start reading.


10. Don’t expose your weaknesses


Sincerity is a much-appreciated quality by employers but not to the extent where you confess all your professional “sins”, such as lacking expertise in a certain area, or past failures on the job. Always remember that the goal of your cover letter is to make an impeccable first impression that would urge the employer to think “I must interview this person right away”.


These data science cover letter dos and don’ts can make all the difference in how your job application is perceived.


So, now that you know the basic guidelines, be sure to check out the other articles we dedicated on the topic:


How to Write a Data Science Cover Letter


How to Organize a Data Science Cover Letter


How to format a data science cover letter


If you’re curious to lift the curtain and see what the data science interview has in store for you? Visit our in-depth guide Data Science Interview Questions and Answers You Need to Know in 2020.


Ready to take the next step towards a data science career?


Check out the complete Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses. Build up a step-by-step experience with SQL, Python, R, Power BI, and Tableau. And upgrade your skillset with Machine Learning, Deep Learning, Credit Risk Modeling, Time Series Analysis, and Customer Analytics in Python. Still not sure you want to turn your interest in data science into a career? You can explore the curriculum or sign up for 12 hours of beginner to advanced video content for free by clicking on the button below.



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How to Format a Data Science Cover Letter?

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how to format a data science cover letter, formatting a data science cover letter, data science cover letter format, data science cover letter formatting tips


When you format a data science cover letter, there are 6 keys to success:


  • unified look with your resume;

  • easy-to-read font;

  • single-page length;

  • consistent spacing;

  • 1-inch-margins and left alignment;

  • compatible file type.

Why is the format of a data science cover letter important?


The format of your data science cover letter is critical to making a positive first impression. A clean and polished format keeps the focus on the content and conveys attention to detail.


Conversely, a sloppy layout signals a lack of professionalism and can instantly eliminate you from the race for your dream job.


So, how to format a data science cover letter for the win?


Let’s go through them together.


Match your data science resume style


Using the same style in both your cover letter and your data science resume will give your job application a cohesive and elegant look. Also, make sure you align both with the image of the employer. A conservative company won’t appreciate ornate fonts and extravagant design. Keep in mind that your cover letter is also a business letter; and, above all, a powerful tool to get a data science interview invitation. So, its style should reflect that.


Choose a crisp font


With so many font styles out there, choosing the best one could be a challenge.


Our advice is: Keep it simple and clean.


Yes, when you format a data science cover letter, legibility is a top priority.


Flashy custom fonts and special characters are not only distracting, but they’re also hard to read, both for Applicant Tracking Systems (ATS) and humans. There’s nothing wrong with classics like Calibri, Verdana, and Cambria (among many others). Another way to ensure your data science cover letter is readable and scanner-friendly is to select the right font size. Stay on the safe side and go for 10-12 pt. That’s how you’ll tick two boxes: easy-to-read and easy-on-the-eyes.


Cut down the length


Less is more. A single one-sided page with 250-400 words is completely adequate for an efficient cover letter. Resist the temptation and leave out any details that the hiring manager can find on your resume. Of course, you can be brave and experiment with a super-concise cover letter of 150 words. But while this one is sure to be read, you run the risk of omitting some important information.


Remember that spacing is important, too


When it comes to spacing, consistency is key. To achieve a coherent look, opt for single-line spacing after each section of your data science cover letter (contact information, greeting, introduction, body, closing, and sign-off). Remember, packing your cover letter with quality content doesn’t equal typing one giant wall of text. That would make it look cramped and messy. On the other hand, spacing and shorter paragraphs balance the page and let your document breathe. Plus, they make the content much easier to process.


It’s okay to adjust the margins… But keep the alignment to the left


But don’t go lower than ¾ or ½ inch… And only if you really need the extra space. When you format a data science cover letter, you need to make sure it doesn’t end up looking cluttered and squished. In other words, it’s best to stick to the business letter format rules and set 1-inch margins on all sides. That leaves plenty of margin space for printing and creates an elegant layout. Speaking of best practices, always left-align your cover letter content. No justification and indentation needed, as they go against the standards.


Save as… the appropriate file type


Saving your data science cover letter in the right file format is vital.


To make sure the reader can open and view it, rely on text type formats, such as .doc or pdf. Both are widely accepted and usually cause no compatibility issues.


How to format a data science cover letter: Additional resources


If you need help to format a data science cover letter, you can browse the wide choice of cover letter builders available online. But how do you pick the best out of many? To make your search easier, we’ve made a quick list of the cover letter builders that offer the best features and useful relevant resources.


  1. Kickresume

  2. ResumeGenius

  3. Wozber

  4. ResumeLab

  5. Zety

Your data science cover letter is an effective tool in your job application process.

Now that you know how to format it to make a strong first impression, be sure to check out the rest of our articles on the topic:


How to Write a Data Science Cover Letter


How to Organize a Data Science Cover Letter


Data Science Cover Letter Dos and Don’ts


If you’re eager to lift the curtain and see what the data science interview has in store for you, visit our in-depth guide Data Science Interview Questions and Answers You Need to Know in 2020.


Ready to take the next step towards a data science career?


Check out the complete Data Science Program today. Start with the fundamentals with our Statistics, Maths, and Excel courses. Build up a step-by-step experience with SQL, Python, R, Power BI, and Tableau. And upgrade your skillset with Machine Learning, Deep Learning, Credit Risk Modeling, Time Series Analysis, and Customer Analytics in Python. Still not sure you want to turn your interest in data science into a career? You can explore the curriculum or sign up for 12 hours of beginner to advanced video content for free by clicking on the button below.



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How to Organize a Data Science Cover Letter?

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how to organize a data science cover letter, what to include in a data science cover letter, what to write in a data science cover letter, paragraphs in a data science cover letter


There are 7 easy steps you can follow to organize a compelling data science cover letter:


  1. Write your contact information at the top;

  2. Address the right recipient by name;

  3. Craft a memorable introduction;

  4. Demonstrate the value you can add to the company in a short and direct body paragraph;

  5. Devise an effective closing paragraph with a strong call-to-action;

  6. Use a professional sign-off.

  7. Proofread (and then proofread again).

We already walked you through the first steps to writing a great data science cover letter. So, now it’s time to focus on the essential elements of your cover letter and the content that will make it shine.


What to include in a data science cover letter?


How to Organize a Data Science Cover Letter: Contact Information


Do you know what hiring managers hate? Rummaging through the content of a data science cover letter to find your contact information. So, do them (and yourself) a favor and put your name and contact information at the top. The easier you make it for your potential employer to reach out, the better. Your contact information should include your name, phone number, and a professional email with your first and last name. Clean and easy-to-find.


How to Organize a Data Science Cover Letter: Opening/Greeting


This is your chance to make a great first impression. Whatever you do, don’t start with “To whom this may concern”. It will make you look sloppy and unprofessional. Instead, do your homework and find out who you’re addressing. Yes, it may take some phone calls and a few Google or LinkedIn searches. However, it’s worth the effort, especially if most candidates have written a blunt generic opening.


Now, depending on the company culture, you could address the recruiter/hiring manager by their first or their last name.


But what if you can’t find the hiring manager’s name?


In that case, go with a safe option like “Dear Data Science Team Hiring Manager”, “Dear Hiring Manager”, or simply write “Dear [Company Name] team”


How to Organize a Data Science Cover Letter: Intro paragraph


Your cover letter introduction should tell your potential employer the following 5 things:


  • Who you are;

  • Your profession/expertise;

  • What role you’re applying for;

  • How you discovered the job posting (especially if you were referred by a current employee of the company);

  • Why you’re interested in the company/job and what makes you a perfect fit for that position.

However, being informative isn’t always enough. Therefore, an underlying goal of your cover letter introduction is to entice the hiring manager. You want them to keep reading to learn more about you. So, think of a unique opening line that would grab their attention. For example, you can include an impressive achievement of yours.


Even if you have no experience in the field, and you’re applying for an entry-level data scientist position, you can still make this work.


Just emphasize on your degree, personal or group projects, volunteering, and relevant certifications. Another way you could go is to mention an important accomplishment or recent success of the target company (or the hiring manager themselves) they’re proud of. If you were referred by a current employee or an important client, make sure you write that in, too. But don’t go overboard with humor or self-praise. Show that you’re enthusiastic about the company. Let them know you’re aware of their needs and you’re following their latest developments. Tell them what you can offer them to help them achieve their goals All the while, Do your best to sound natural and leave the strict formalities behind. Go for simpler words. This will help you achieve a friendlier tone.


How to Organize a Data Science Cover Letter: Body paragraph


This is the most crucial part of your data science cover letter. Fortunately, there are a few rules of thumb that will help you present yourself in the best light possible:


Less is more


It’s easy to get carried away when you want to make a good impression. But there’s a thin line between showcasing your skill set and just bragging about your accomplishments. Be short and direct. And only include meaningful achievements in light of business success you can provide relevant context for.


Don’t copy your resume


…But do borrow some tangible metrics from it, especially when it comes to relevant projects you’ve worked on and the impact you’ve had on achieving your current/former employer’s business goal. It’s a numbers game, so make sure you quantify the results you’ve accomplished.


Show you’re the solution to their problems


Employers hire people to solve specific challenges. It could be improving an algorithm for an AI-powered app; or implementing changes to their database management system to increase efficiency… Or increase their revenue by developing a machine learning solution from scratch. Whatever it is, it’s your job to research the urgent business needs of the company. Once you’ve discovered their pain-point, explain how you can use your expertise to help. You can even take it one step further by finding information about the company’s future goals. Then use your relevant work history to prove you can help them get there.


Use the job description to your advantage


Make no mistake, Applicant Tracking Systems (ATS) will leave no word in your data science cover letter unchecked. So, incorporate as many keywords from the job description as appropriate. In fact, this is the part of writing your data science cover letter where direct copy-paste is highly encouraged. Just go right ahead, it’s guilt-free!


Work experience isn’t everything


Are you a recent graduate with no professional experience in data science? Keep your chin up because you still have plenty to offer. When it comes to entry-level positions, employers look for 3 things – suitable education, skills, and desire to learn quickly. Focusing on these in your data science cover letter will make up for the lack of 5-page work history.


In case you’re transferring into data science from a different field, emphasize on the data science certifications and skills you’ve acquired. These not only open the door for you, but also demonstrate a commitment to your new profession. (Data science isn’t a field you can enter without any relevant qualifications, so additional courses and online trainings are key). There’s also something else you can capitalize on – your transferrable skills. So, refer to your data science resume and include the most suitable examples for the particular job posting. And don’t forget to mention the reason for your career change. Your potential employer will appreciate that you’re proactive and enthusiastic about what you do.


Show some personality


Your data science cover letter isn’t just a supplement to your resume. It’s a brief story about who you are, how you can make a difference, and why you’re the perfect fit for the job. So, let your personality shine through. Add a layer to your cover letter by touching on certain interests that relate to the role; hint at your sense of humor; share a particular detail you like about the company and their culture… Anything that will make a really good story of what makes you “you” in your working life.


How to Organize a Data Science Cover Letter: Closing paragraph


The closing paragraph in a data science cover letter serves a two-fold purpose:


  • To remind the employer why you’re the best candidate for the job;

  • To prompt the employer to get in touch with you with a concrete call-to-action.

Make it clear that you’ll be happy to be interviewed. You can also tell them that you’ll follow-up in a week if you don’t hear back. And, of course, don’t forget good manners – thank the hiring manager for taking the time to read your cover letter.


Sign-off


Your sign-off should be sharp and professional, just like you. Anything other than “Sincerely”, “Regards”, and “Best regards”, followed by your first and last name, would be redundant.


Proofread


How to proofread a cover letter? A typo or a spelling mistake in your data science cover letter can cost you the interview. That’s why we prepared a list of proofreading tips you can use to submit a polished and mistake-free cover letter:


See it in print


Printing out your resume in a larger font is a legit strategy for catching errors, missing punctuation marks, and even text inconsistencies. You can highlight the edits you need to make with a colored pen. This way, you’ll find the changes easily once you get back to your cover letter file.


Your voice is your top editor


Reading your data science cover letter out loud might feel awkward, especially if there’s no love lost between you and the theater. However, that’s one of the best ways to detect bad phrasing and spelling mistakes. And it’s so much better to notice them before your potential employer does, right? Once you’ve finished your monologue, you can read your cover letter out loud once again. Only this time, start from the bottom to the top. It’s fun and it will help you spot that one typo you’ve previously missed.


Phone-a-friend


Everyone has that one spelling-bee friend. So, why not put their talent to good use and ask them to review your cover letter? Very often, it takes seconds for an extra set of eyes to spot an error you weren’t even aware of. Once your data science cover letter has passed this final test, you can finalize your proofreading efforts with some free tools like Grammarly, ProWritingAid or WhiteSmoke.


Your data science cover letter is a powerful tool in your job application process.

Now that you know how to organize it and fill it in with killer content, make sure you check out the rest of our articles on the topic:


How to Write a Data Science Cover Letter


How to Format a Data Science Cover Letter


Data Science Cover Letter Dos and Don’ts


Curious to discover what the data science interview has in store for you? Visit our detailed guide Data Science Interview Questions and Answers You Need to Know in 2020.


And, if reading this piece helped you identify some key skills you need to add to your data science toolbox, take a look at the courses in the 365 Data Science Training. You can explore the curriculum or sign up 12 hours of beginner to advanced video content for free by clicking on the button below.


 


 


 



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Monday, May 25, 2020

Certificates

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Friday, May 22, 2020

How to Limit Your Rate of Requests When Scraping?

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how to limit your rate of requests when scraping, limiting your rate of requests when web scraping


In our last two tutorials, we talked about requests headers and how you can scrape data locked behind a login. But how can you limit your rate of requests that you send to a particular server?


Now, I can almost hear you asking why you need to reduce the number of requests when scraping in the first place.


Let me explain.


Why Limit Your Rate of Requests?


First, let’s consider the matter from an ethical point of view. Your program should be respectful to the site owner.


Remember that every time you load a web page, you’re making a request to a server. When you’re just a human with a browser, there’s not much damage you can do.


With a Python script, however, you can execute thousands of requests a second, intentionally or unintentionally. The server then needs to process every request individually. This, combined with the normal user traffic, can result in overloading the server. And this overload can manifest in slowing down the website or even bringing it down altogether.


Such a situation usually degrades the experience of real users and can cost the website owner valuable customers.


Obviously, we don’t want that. In fact, if done intentionally, this is considered a crime – the so-called DDOS attack (Deliberate Denial of Service), so we better avoid it.


Given the potential damage this easy technique can do, servers have started employing automatic defense mechanisms against it.


One form of such protection against spammers may be to temporarily block a user from the service if they detect a big amount of activity in a short period of time.


So, even if you are not sending huge numbers of requests, you may get blocked as a preventive measure. And that’s precisely why it is important to know how to limit your rate of requests.


How to Limit Your Rate of Requests When Scraping?


Let’s see how to do this in Python. It is actually very easy.


Suppose you have a setup with a “for loop” in which you make a request every iteration, like this:


limit your rate of requests, for loop, python, web scraping


Depending on the other actions you take in the loop, this can iterate extremely fast. So, in order to make it slower, we will simply tell Python to wait a certain amount of time. To achieve this, we are going to use the time library.


python time library


It has a function, called sleep that “sleeps” the program for the specified number of seconds. So, if we want to have at least 1 second between each request, we can have the sleep function in the for loop, like this:


python time library sleep function


This way, before making a request, Python would always wait 1 second. That’s how we will avoid getting blocked and proceed with scraping the webpage.


So, this is one more web scraping roadblock you now know how to deal with.


I hope this tutorial will help you with your tasks and web scraping projects.


Eager to scrape data like a pro? Check out the 365 Web Scraping and API Fundamentals in Python Course.


The course is part of the 365 Data Science Program. You can explore the curriculum or sign up for 12 hours of beginner to advanced video content for free by clicking on the button below.



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Thursday, May 21, 2020

Certificates

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Certificate

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Answer for Lesson "Indexing Elements". It seams that 'Holidays!'[6] gives 'y', not y like "correct" in Quis

https://365datascience.com/dwqa-answer/answer-for-lesson-indexing-elements-it-seams-that-holidays6-gives-y-not-y-like-correct-in-quis/ -

Hi Vasil!

Thanks for reaching out.

Please don’t ignore the use of the print() function! Take it into account and retry.

Hope this helps.
Best,
Martin




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Answer for I am confused using Count(*)

https://365datascience.com/dwqa-answer/answer-for-i-am-confused-using-count/ -

Hi Mike!

Thanks for reaching out.

In theory, there’s not much of a gap. COUNT(*) returns the number of all rows of the given field from the data set, including NULL values. If you are using a specific field as an argument (i.e. e.g. COUNT(emp_no)), the NULL values will be ignored and only the number of the non-null values will be retrieved.  

Hope this helps.
Best,
Martin




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Answer for Making prediction with Scikit learn

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Hi Gurpinder,

Thanks for reaching out.

Please note that sklearn expects your data to be in 2D form.

To achieve that you can write:


reg.predict([[1740]])

And the problem will be fixed. 

Note that when we were recording the video, it was working even without the square brackets.

Best,
The 365 Team




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Answer for When performing the linear model with l2 norm loss and gradient decent loss function reaches infinity and then nan

https://365datascience.com/dwqa-answer/answer-for-when-performing-the-linear-model-with-l2-norm-loss-and-gradient-decent-loss-function-reaches-infinity-and-then-nan/ -

Hi there,

The reason for that is that your loss function is not converging to 0, but rather – diverging to infinity.

This usually happens when the learning rate you have chosen is too big. Please try with some lower number, e.g. 0.0001.

Best,

The 365 Team




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Answer for Load Dataset

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Hi Glen,
You can find all Credit Risk Modeling files over here: https://www.dropbox.com/sh/7oslws1xhsm1zbf/AABkdWDKqpdcGmY1NbXAnkrBa?dl=0
Hope this solves your issue!
Best,
The 365 Team




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Answer for Problem with TensorFlow2.0: ImportError: DLL load failed.

https://365datascience.com/dwqa-answer/answer-for-problem-with-tensorflow2-0-importerror-dll-load-failed/ -

Hi Volkmar,

Thanks for reaching out.

DLL errors are not very easy to fix actually. They happen because of improper installation or some loss of data afterwards.

I highly suggest that you completely reinstall the whole Anaconda distribution – that would be the easiest way to tackle the issue.

Best,

The 365 Team




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Answer for Trigger before insert

https://365datascience.com/dwqa-answer/answer-for-trigger-before-insert-5/ -

Hi Yunfeng!

Thank you very much for your reply.

I am sorry the previous suggestion did not help. In that case, can you please post the entire code you’ve executed, as well as a screenshot containing the error message that you get (or you can also paste the error message, if you prefer)? I would like to execute the query on my end to see what we can do.
Thank you!

Looking forward to your reply.

Best,
Martin




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Answer for What is S&P term?

https://365datascience.com/dwqa-answer/answer-for-what-is-sp-term/ -

Hi Samuel,

Thanks for reaching out. 

The S&P500 or simply the S&P measures the stock performance of 500 large companies listed on stock exchanges in the United States. 

It is a very good proxy of the US (and world) economy and is the preferred index to look at when getting acquainted with time series data.

Best,

The 365 Team




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Answer for Anaconda Navigator doesn't have VS Code installed (Capstone project)

https://365datascience.com/dwqa-answer/answer-for-anaconda-navigator-doesnt-have-vs-code-installed-capstone-project/ -

Hi Marko,

I believe Stephen summed it up quite well!

Best,

The 365 Team




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Answer for QR Code

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Hi Ammar,

Thanks for this suggestion.

We are actually going to implement that very soon.

Best,

The 365 Team




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Answer for Number of records missing

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Hi Manish,

Which lecture are you referring to?

Could you please share a screenshot?

The 365 Team




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Answer for Installation of Tensorflow

https://365datascience.com/dwqa-answer/answer-for-installation-of-tensorflow/ -

Hi Porage,

Could you please open your base environment in Anaconda and write:
pip install ipykernel

and
conda install nb_conda_kernels

Then restart Anaconda and the Jupyter notebook.

Let me know if the problem persists.

Best,

The 365 Team




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Wednesday, May 20, 2020

Answer for Trigger before insert

https://365datascience.com/dwqa-answer/answer-for-trigger-before-insert-3/ -

Hi Yunfeng!

Thank you very much for your reply.

Yes, whether you use capital or small letter matters. You need to use small letters in this example.


%y-%m-%d

Regarding the error message – have you executed the queries you’ve provided in the given order? The error message is actually suggesting that you had probably used the INSERT statement to insert a different record:


INSERT INTO employees
VALUES (‘999996’, ‘1990-03-01’, ‘JOHN’, ‘SMITH’, ‘M’, ‘2020-May-8th’);

Instead, we must use numbers to express the dates – 2020-05-08.

Hope this helps.
Best,
Martin




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