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How Educators Teach: Specific MethodsOptions for making your class sessions more efficientWays to encourage student participation in your classesAdvantages of collaborative studyingExamples of effective clinical practicesThis chapter discusses several types of teaching science within the original formats: lectures, discussion classes, and laboratories. How is it possible to help your students learn science better plus more efficiently in each file format? Although there is no universal simplest way to teach, experience demonstrates some general principles apply (American Association for your Advancement of Science)Teach scientific means of thinking.Actively involve students in their own learning.Help students to produce a conceptual framework along with to develop problem fixing skills.Promote student debate and group activities.Aid students experience science inside varied, interesting, and pleasurable ways.Assess student understanding at frequent intervals through the entire learning process.LECTURESEvidence from a number of disciplines suggests that oral presentations to large groups of passive students contribute very little to real learning.Enhancing Learning in Large ClassesDespite the limitations of traditional lectures, many institutions are forced to offer high-enrollment introductory science courses. Many professors who teach these courses feel that lecturing is their only option, and can only dream of what they could accomplish in smaller classes.Hints for More effective LecturingWhen lecturing is the chosen or necessary teaching method, one way to keep students engaged is to pause periodically to assess student understanding or to initiate short student discussions (see sidebars).Asking QuestionsWhether in lecture, discussion sections, laboratories, or individual encounters, questioning is an important part of guiding students' learning. When students ask questions, they are often seeking to shortcut the learning process by getting the right answer from an authority figure.DemonstrationsDemonstrations can be very effective for illustrating concepts in class, but can result in passive learning without careful attention to engaging students. They can provoke students to think for themselves and are especially helpful if the demonstration has a surprise, challenges an assumption, or illustrates an otherwise abstract concept or mechanismDISCUSSIONSSmall group discussion sections often are used in large-enrollment courses to complement the lectures. In courses with small enrollments, they can substitute for the lecture, or both lecture and discussion formats can be used in the same class period.Why Discussion?Focused discussion is an effective way for many students to develop their conceptual frameworks and to learn problem solving skills as they try out their own ideas on other students and the instructor.Planning and Guiding DiscussionsProbably the best overall advice is to be bold but flexible and willing to adjust your strategies to fit the character of your classCOLLABORATIVE LEARNINGCollaborative learning "is an umbrella term for a variety of educational approaches involving joint intellectual effort by students, or students and teachers together.LABORATORIESIt is hard to imagine learning to do science, or learning about science, without doing laboratory or field work. Experimentation underlies all scientific knowledge and understanding. Laboratories are wonderful settings for teaching and learning science.Developing Effective LaboratoriesImproving undergraduate laboratory instruction has become a priority in many institutions, driven, in part, by the exciting program being developed at a wide range of institutions.Lab ReportsThe various methods by which students report their lab work have different pedagogical objectives. The formal written report teaches students how to communicate their work in journal style, but students sometimes sacrifice content for appearance.Teaching Labs with Teaching AssistantsMany benefits of carefully planned laboratory exercises are realized only if the instructional staff is well prepared to teach. Often the primary, or only, lab instruction comes from graduate or undergraduate teaching assistants or from faculty members who were not involved in designing the lab.

There’s actually not that much wrong with the actual interviews; there’s plenty wrong with getting to that stage, thoughI’ll talk about technical interviews, although you shouldn’t blow off non-technical ones. Those are important, too.Technical ones come in four (main) flavors:“You know it or you don’t”. These are the types of questions you’ll get if you do a web search for “data science interview questions”. Stuff like “How does the CART algorithm decide which features to use to split on?”, “When would you use Naive Bayes”, and “Explain overfitting to your grandmother”. Either you know it or you don’t.“Tell me about…” These questions go beyond classroom knowledge to find out how familiar you are with the practice of data science. Stuff like “Tell me about the time you had a particularly difficult data set to clean” or “What’s your favorite R package and why?” (I was asked this last one once).The third type is the most difficult: whiteboard problem-solving problems. These are basically oral exams where you are given a mini data science problem to work out in front of the interviewer. These will take you 25 minutes, 30 minutes, even longer to do. There’s a real art to doing these correctly.Technical Presentations. Like it sounds, you get up and flip through slides of one of your personal projects, past work, or maybe an esoteric method. These are less common but actually very good for the interviewer: you get to talk about something you ought to know like the back of your hand. Obviously, communication skills (written, oral, and interpersonal) are key here, as well as staying cool under pressure.I’m not including take-home problem sets. You could argue that’s a fifth type of interview.There are three other types I’ve encountered that I do have problems with:Computer science algorithms: This is stuff like bubblesort and A*. Obviously, it depends on what job you are interviewing for and what work they do, but this kind of stuff is mostly irrelevant for most data science. Fortunately, you’ll rarely encounter theseCoding problems in front of someone. Coding is pretty difficult even when you are alone. This is just too stressful. You can argue the whiteboard ones are, too (#3 above), but the point of having someone watch you is so you can explain your thought process and ask questions (something you’ll have to do when you get the job). I think these types of questions are a mistake.Roundtable brainstorming sessions. You get in a room with several of their data scientists and they describe a problem they are working on. Then they ask how you would go about solving it. I used to look forward to these types of questions because coming up with ideas is one of my strengths. I’ve come to conclude these are just a scammy way of getting free consulting. I’ve had a number of these where they were hurriedly scribbling down everything I said. When the recruiter called to find out if they liked me and wanted to make an offer, they wouldn’t even reply. I suspect there was never a job in the first place. Not much you can do here.But back to the main four: I think they are all pretty good and test different aspects of being a data scientist. They aren’t perfect but put together they do a decent job. Not as decent as letting you come in for a full day and work side-by-side with your future teammates, but it’s rare to find a company that will do that.But you asked about what’s wrong, so here’s what I’ll say:On-site interviews can be grueling. You will go from door-to-door getting grilled each time. You’ll never experience that in a real situation.Sometimes they’ll invite you for lunch. Be careful not to “let your hair down”. You’re still being evaluated. They want to try to get to know “the real you”.On that same topic, they are evaluating you not only on technical capabilities but “cultural fit”. That is, would they actually like working with you. If you are still new to interviews, you may be overly “stiff” and not likable.I recently conducted 20 mock whiteboard interviews between people currently looking for jobs and interviewers who actually interview candidates as part of their job (and videotaped the entire thing). Most people just don’t know how to handle these situations well, even if they know the stuff. I think these questions are very good; I’m just saying that they require significant interviewing skills in addition to knowing the technical material.The non-technical questions are pretty tricky. Again, if you know what you’re doing, this can be a goldmine for you. For example, you’re bound to be asked: “Tell me about yourself”. You ought to be cheering when you hear this question because it’s an excellent time to demonstrate skills as opposed to asserting them (e.g., initiative, communication). But most technically competent people don’t know how to answer these,I know this is long but data science interviews are unusually difficult. There’s a lot to know about them. I do share a lot of information on how to get a job as a data scientist. You should follow me on social media, my blog, my email list, etc. to find out more.The question was specifically about interviews. The process of getting to the interviews in the first place is terribly unfair and you really need to have serious job hunting skills to get that far. Here’s a hint: if you are simply submitting resumes to job portals, you’ll never get an interview. They won’t even look at it. These companies just get too many applications.But that’s an entirely different post.Good luck! It’s extremely difficult to get a job as a data scientist, but the rewards are worth it!

Must-Haves⦁ Contact Information⦁ Skills⦁ Experience⦁ EducationInclude These⦁ Ensure that everything listed on your resume correlates to the job you are applying to.⦁ Your resume should be either one page or two full pages (not 1.5 pages).⦁ Your resume should include at least 5 keywords from the job description. Also, all bullets need to be lined up correctly.⦁ The font on your resume should be easy to read and not too small (preferably size 12).⦁ Your resume should clearly show that you meet at least the minimum qualifications (preferably meeting additional qualifications).⦁ Your resume should have a summary section at the very top that shows that you meet the requirements of the job. The summary section identifies how you can help solve the company’s problems. Hint: Your resume should not say “I am looking for a job in ……”⦁ Each bullet on your resume should start with a positive action verb.⦁ Correct verb tense should always be used. If it’s a past job, all verbs should be past tense. If you are currently working there, use present tense. This seems easy but you would be amazed at how much this is missed!⦁ Your experience section should clearly detail results and accomplishments using numbers whenever possible (preferably quantified with percentages and numbers).⦁ Your resume should shows how you have added value and solved problems at every company you list. It should not just be a list of tasks.⦁ Your education section should be clear with your degree, school name and applicable courses, projects or training that relate to the job you are applying to.⦁ There should be absolutely no misspellings or grammatical errors.⦁ You should leave the reader wanting to know more about you and it should include something unique and memorable about you. Remember, they are looking for someone who can spend 40+ hours a week with. Show them that you are fun to work with too!⦁ Save a pdf copy⦁ Include Appropriate Links⦁ Use hard numbers⦁ Delete graduation year⦁ Arrange according to relevance⦁ User your name in file name⦁ Try to keep as 1`pageAvoiding These⦁ Avoid meaningless adjectives. “seasoned manager”-“influential leader”⦁ Cut out “creative.”Recruiters have seen this word so much they will completely gloss over it.⦁ Remove “results-oriented.”Instead, highlight your skills and accomplishments by using the names of the projects or campaigns you worked on⦁ Take out “passionate.”The verity of your enthusiasm can easily be checked through your social media profiles. If you really love what you do, your Facebook and Twitter accounts would show work-related status updates⦁ Rid your resume of “responsible for.”doing what he’s paid to do—no more, no less⦁ Get rid of “guru.”Calling yourself a guru on your resume makes you sound like somebody trying hard to look smart.⦁ Axe “excellent oral and written communication skills.”f your resume and cover letter fail to communicate why you should get an interview, then what’s the point of putting “excellent communication skills” on paper?⦁ Abbreviations.⦁ Generic resume lines with no purpose.⦁ Objective⦁ References Provided Upon Request⦁ Inconsistent format, grammar tense⦁ “too much” or “too little” information.⦁ “periods” if you’re not using complete sentences.⦁ Middle & High school Information⦁ Average or Poor GPA⦁ Passive Language⦁ Photo / any info that can cause discrimination⦁ Series of short-lived jobs⦁ Objective Statement⦁ Obvious skills⦁ No more than 2 pages\⦁ Over using the same words⦁ Emphasize results rather than responsibilities⦁ These words:⦁ Tries, loves, responsibilities include,⦁ Your address⦁ Eliminate soft skillsDon't Forget!⦁ Your resume is the true first impression⦁ Cut out anything irrelevant.⦁ Save time by using old job posting descriptions.⦁ Pay attention to tense.⦁ Less is more, but don't get carried away⦁ Action words make people excited!⦁ Numbers add value. (No pun intended)⦁ Pull keywords from the job posting.⦁ Don't forget to mention your skills.⦁ Ask yourself, "would I want to read this resume?"⦁ Do the little things very wellRandom Resume Facts⦁ On Average, your resume will get read in about 6 seconds.⦁ Recuriters like seeing volunteer work⦁ 144 people apply for each entry-level position posted⦁ 89 people apply for ach professional-level position⦁ 35% of candidates are qualified for the position they seek,⦁ (some candidates spend as little as 50 seconds reading job description before they apply⦁ It costs a company about $3,479 to hire a new employee⦁ 1/6 credit social meida with helping them land their current job⦁ Youtube video resume is over 1million plus hits⦁ employers would value video resumes is the ability to assess pro presentation & demeanour⦁ 89% of employers said they would watch a video resume if it were submitted to them⦁ 76% of resumes are ignored if the email address is unprofessional,⦁ 93% of employers reported using social media for recruiting⦁ Linked in is the most popular (keep and maintain a professional profile on all social media⦁ 70% employers prefer traditional resume to an infographic, online profile or video resumeTop 5 hard skills⦁ Experience 95%⦁ Technical ability 74%⦁ Training 58%⦁ Education 35%⦁ Certification 18%