You are excited. You have got that much awaited interview call for that dream analytics job. You are confident you will be perfect for the job. Now all that remains is convincing the interviewer. Don’t you wish you knew what kind of questions they are going to be ask?



As co-founder and one of the chief trainers at Jigsaw Academy, an online analytics training institute, I regularly get calls from our students days before their scheduled interview asking me just this. I am going to share with you just what I share with them. Here you go. Below are a few of the more popular questions you could get asked and the corresponding answers in a nutshell.&nbsp;



Question 1. Can you outline the various steps in an analytics project?



Broadly speaking these are the steps. Of course these may vary slightly depending on the type of problem, data, tools available etc.



1. Problem definition – The first step is to of course understand the business problem. What is the problem you are trying to solve – what is the business context? Very often however your client may also just give you a whole lot of data and ask you to do something with it. In such a case you would need to take a more exploratory look at the data. Nevertheless if the client has a specific problem that needs to be tackled, then then first step is to clearly define and understand the problem. You will then need to convert the business problem into an analytics problem. I other words you need to understand exactly what you are going to predict with the model you build. There is no point in building a fabulous model, only to realise later that what it is predicting is not exactly what the business needs.



2. Data Exploration – Once you have the problem defined, the next step is to explore the data and become more familiar with it. This is especially important when dealing with a completely new data set.



3. Data Preparation – Now that you have a good understanding of the data, you will need to prepare it for modelling. You will identify and treat missing values, detect outliers, transform variables, create binary variables if required and so on. This stage is very influenced by the modelling technique you will use at the next stage.&nbsp; For example, regression involves a fair amount of data preparation, but decision trees may need less prep whereas clustering requires a whole different kind of prep as compared to other techniques.



4. Modelling – Once the data is prepared, you can begin modelling. This is usually an iterative process where you run a model, evaluate the results, tweak your approach, run another model, evaluate the results, re-tweak and so on….. You go on doing this until you come up with a model you are satisfied with or what you feel is the best possible result with the given data.



5. Validation – The final model (or maybe the best 2-3 models) should then be put through the validation process. In this process, you test the model using completely new data set i.e. data that was not used to build the model. This process ensures that your model is a good model in general and not just a very good model for the specific data earlier used (Technically, this is called avoiding over fitting)



6. Implementation and tracking – The final model is chosen after the validation. Then you start implementing the model and tracking the results. You need to track results to see the performance of the model over time. In general, the accuracy of a model goes down over time. How much time will really depend on the variables – how dynamic or static they are, and the general environment – how static or dynamic that is.



Question 2. &nbsp; What do you do in data exploration?



Data exploration is done to become familiar with the data. This step is especially important when dealing with new data. There are a number of things you will want to do in this step –



a.&nbsp; &nbsp; &nbsp; &nbsp; What is there in the data – look at the list of all the variables in the data set. Understand the meaning of each variable using the data dictionary. Go back to the business for more information in case of any confusion.



b.&nbsp; &nbsp; &nbsp; &nbsp; How much data is there – look at the volume of the data (how many records), look at the time frame of the data (last 3 months, last 6 months etc.)



c. &nbsp; &nbsp; &nbsp; &nbsp; Quality of the data – how much missing information, quality of data in each variable. Are all fields usable? If a field has data for only 10% of the observations, then maybe that field is not usable etc.



d.&nbsp; &nbsp; &nbsp; &nbsp; You will also identify some important variables and may do a deeper investigation of these. Like looking at averages, min and max values, maybe 10th and 90th percentile as well…



e.&nbsp; &nbsp; &nbsp; &nbsp; You may also identify fields that you need to transform in the data prep stage.



&nbsp;Question 3: What do you do in data preparation?



In data preparation, you will prepare the data for the next stage i.e. the modelling stage. What you do here is influenced by the choice of technique you use in the next stage.



But some things are done in most cases – example identifying missing values and treating them, identifying outlier values (unusual values) and treating them, transforming variables, creating binary variables if required etc,



This is the stage where you will partition the data as well. i.e create training data (to do modelling) and validation (to do validation).



&nbsp;Question 4: How will you treat missing values?



The first step is to identify variables with missing values. Assess the extent of missing values. Is there a pattern in missing values? If yes, try and identify the pattern. It may lead to interesting insights.



If no pattern, then we can either ignore missing values (SAS will not use any observation with missing data) or impute the missing values.



Simple imputation – substitute with mean or median values



OR



Case wise imputation –for example, if we have missing values in the income field.



Question 5: How will you treat outlier values?



You can identify outliers using graphical analysis and univariate analysis. If there are only a few outliers, you can assess them individually. If there are many, you may want to substitute the outlier values with the 1stpercentile or the 99th percentile values.



If there is a lot of data, you may decide to ignore records with outliers.



Not all extreme values are outliers. Not all outliers are extreme values.



Question 6: How do you assess the results of a logistic regression analysis?



You can use different methods to assess how good a logistic model is.



a. Concordance – This tells you about the ability of the model to discriminate between the event happening and not happening.



b. Lift – It helps you assess how much better the model is compared to random selection.



c. Classification matrix – helps you look at the false positives and true negatives.



Some other general questions you will most likely be asked:



<ul>
<li>What have you done to improve your data analytics knowledge in the past year?</li>



<li>What are your career goals?</li>



<li>Why do you want a career in data analytics?</li>
</ul>



The answers to these questions will have to be unique to the person answering it. The key is to show confidence and give well thought out answers that demonstrate you are knowledgeable about the industry and have the conviction to work hard and excel as a data analyst.



About Sarita Digumarti: Sarita has over 10 years of extensive analytics and consulting experience across diverse domains including retail, health-care and financial services. She has worked in both India and the US, helping clients tackle complex business problems by applying analytical techniques. She has a Master’s degree in Quantitative Economics, from Tufts University, Boston, and a PG Diploma in Management from T.A. Pai Management Institute, Manipal. Sarita’s <a href="http://in.linkedin.com/in/saritadigumarti">Linkedin profile</a>

Common Analytics Interview Questions

The demand for skilled data scientists and data engineers remains <a href="http://articles.economictimes.indiatimes.com/2015-01-22/news/58343917_1_big-data-analytics-professionals-data-science">strong in 2015</a>.&nbsp; Big data analysis is becoming a necessity for companies to prosper in all industries from <a href="http://blogs-images.forbes.com/louiscolumbus/files/2014/12/top-20-industries-hiring.jpg">agriculture to finance</a>. Large international companies to new startups have been reaching out to bootcamps, immersion, and fellowship programs for qualified professionals. The requirements for enrollment are more stringent than those of <a href="http://www.skilledup.com/articles/the-ultimate-guide-to-coding-bootcamps-the-exhaustive-list/">coding bootcamps</a> but, students who make it through will be positioned for one of the <a href="http://rht.mediaroom.com/index.php?s=533&amp;item=72">highest paying jobs</a> in the tech field. Take a look at what these data science and engineering schools have to offer.



<h2 class="wp-block-heading"><a href="http://insightdataengineering.com/">Insight Data Engineering</a> &amp; <a href="http://insightdatascience.com/">Data Science Fellows</a> Silicon Valley, CA, NYC<img loading="lazy" decoding="async" alt="Insight-logo" src="https://lh7-us.googleusercontent.com/FKUHAhr9snwDzrRhUDT3t3m2dXfS4HBpCxS4epaMImTGVQHIy5IbRrQ6TSrv4ILOD9CSwUA9FD3xcuk9uR5Xkj6VGhVBGWjhkvJBOg-EkPgwNN1Kp2ssj0XMqlPaInnfzJLPjgOpNBEnyLhhqQTSkGU" width="624" height="337"></h2>



Insight has two separate six-week programs designed to bridge the gap between traditional education and a lucrative career.&nbsp; Both are project-based learning programs with no traditional lecture classes. The data engineering program is for those interested in building infrastructures with large code bases. The data science program is for those who want to acquire experience in data analysis, experimental design, or machine learning. Upon graduation, graduates get positioned directly into partnered companies of Insight.



<figure class="wp-block-table"><table><tbody><tr><td>FORMAT</td><td>In-person</td></tr><tr><td>COST</td><td>Free</td></tr><tr><td>REQUIRED EXPERIENCE</td><td>Completed or are approaching completion of a Ph.D.</td></tr><tr><td>LANGUAGES</td><td>Python, R, Git, Flask, Javascript, MySQL, Hadoop, Hive</td></tr><tr><td>CLASS SIZE</td><td>n/a</td></tr><tr><td>CLASS LENGTH</td><td>Six weeks</td></tr><tr><td>HOURS/WEEK</td><td>Full-Time</td></tr><tr><td>SESSIONS/YEAR</td><td>Two</td></tr><tr><td>APPLICATION</td><td>Standard application</td></tr><tr><td>JOB ASSISTANCE</td><td>Yes</td></tr><tr><td>TUITION REIMBURSEMENT</td><td>n/a</td></tr><tr><td>FINANCIAL AID</td><td>Need-based travel and living expense scholarships available</td></tr><tr><td>PAYMENT PLAN</td><td>n/a</td></tr><tr><td>SELECTIVE</td><td>Yes</td></tr><tr><td>DESCRIPTION</td><td>Data Science Fellowship</td></tr><tr><td>CONTACT</td><td><a href="mailto:info@insightdatascience.com%20">info@insightdatascience.com&nbsp;</a></td></tr></tbody></table></figure>



<h2 class="wp-block-heading"><a href="http://www.thisismetis.com/data-science">Metis</a> NYC<img loading="lazy" decoding="async" alt="Metis-logo" src="https://lh7-us.googleusercontent.com/z6Cz7zFvTWZ-EvHFZFIwgVpmLxfSer-ffiZ52tOb44i_U2GT3S1HyE25U0uhq1-pi8F3suRmzHyp1tr7nDHP6IM9myMbnIXXa-xt4GACyoYeNlk-jmjNvbxVrvM3Ugy5fFUPgnokWWwr4Rs8EWXFn7U" width="624" height="253"></h2>



In 2015, Kaplan’s Metis bootcamp has decided to focus all its expertise into it’s data science bootcamp. Its course is designed to teach someone to think and act like a true data scientist. Students are taught to discover the questions that will deliver the greatest value from their data. The coursework provides a very strong foundation amongst many essential tools and algorithms. At the end of the 12-week course Metis holds a career day visited by over 30 companies. That is more companies than the average amount of students enrolled. Metis holds the only data science bootcamp accredited by the Accrediting Council for Continuing Education &amp; Training (ACCET), a U.S. Department of Education nationally recognized agency.



<figure class="wp-block-table"><table><tbody><tr><td>FORMAT</td><td>In-person</td></tr><tr><td>COST</td><td>$14,000</td></tr><tr><td>REQUIRED EXPERIENCE</td><td>Applicants must have some previous experience programming (writing code) and studying or using statistics.</td></tr><tr><td>LANGUAGES</td><td>Python (Scikit-learn, Statsmodels, Numpy, Pandas, Matplotlib, Flask), Javascript (d3.js), and other software (Git, Hadoop, Hive, SQL, Mongo)</td></tr><tr><td>CLASS SIZE</td><td>18-24</td></tr><tr><td>CLASS LENGTH</td><td>12 weeks</td></tr><tr><td>HOURS/WEEK</td><td>Full-time</td></tr><tr><td>SESSIONS/YEAR</td><td>Four</td></tr><tr><td>APPLICATION</td><td>Standard application with self assessment + coding challenge + interview</td></tr><tr><td>JOB ASSISTANCE</td><td>Provide coaching, technical interviewing workshops, salary negotiation support, and access to employers. Career day, students present 1 of 5 different projects to 25-30 different employers</td></tr><tr><td>TUITION REIMBURSEMENT</td><td>No</td></tr><tr><td>FINANCIAL AID</td><td>$2,000 scholarship for women, underrepresented minority groups, and veterans or members of the U.S. military.</td></tr><tr><td>PAYMENT PLAN</td><td>Available. Financing partners available.</td></tr><tr><td>SELECTIVE</td><td>Yes</td></tr><tr><td>DESCRIPTION</td><td>Data Science Bootcamp</td></tr><tr><td>CONTACT</td><td><a href="http://www.thisismetis.com/data-science/?utm_source=datasciencereport&amp;utm_medium=website_ad&amp;utm_campaign=ds">Contact Form</a></td></tr></tbody></table></figure>



<a href="http://www.skilledup.com/articles/list-data-science-bootcamps/">VIEW COMPLETE LIST HERE (SkilledUp.com)&nbsp;</a>



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List of Data Science Bootcamps & Fellowships

Michael Berry is taking a stand against the big data hype. More data, said the analytics director for travel website TripAdvisor, doesn’t always mean better business results. Case in point: big data and <a href="http://searchcrm.techtarget.com/definition/predictive-analytics">predictive analytics</a>.



“Many predictive analytics applications turn out not to need all of the data,” Berry said during his keynote talk at Predictive Analytics World. So the real task for <a href="http://searchcio.techtarget.com/video/HMS-CIO-Were-big-data-scientists-not-big-data-practitioners">data scientists</a> et al. isn’t figuring out how to analyze all the available data; instead, it’s figuring out how much data it takes to see something worth noting. The bad news?



“There’s not a simple answer to that question,” Berry said.



However, testing the predictive model’s performance by incrementally adding more data can shed light on when enough is enough. For example, when Berry wanted to know the standard bid by travel agency partners for a specific hotel and specific customer, he began computing averages: The first two bids compared to the first three bids compared to the first four bids and so on until he hit a steady plateau at 100,000. If he kept going to 200,000 bids, the average would change, sure, but not enough to matter.



“That’s the way data tends to be: When you have enough of it, having more doesn’t really make much difference,” he said.



So if more data doesn’t matter, what does? “So many things,” Berry said. Working with <a href="http://searchdatamanagement.techtarget.com/feature/Business-data-quality-measures-need-to-reach-a-higher-plane">clean data</a>, doing unbiased sampling, hiring staff dedicated to data quality and creative thinking.



That’s right, there’s a big place in predictive analyses for those <a href="http://searchbusinessanalytics.techtarget.com/news/2240181721/Data-science-team-building-101-Cross-functional-talent-key-to-success">soft data science skills</a>, such as figuring out what variables can make the model stronger or what new patterns might be discovered by combining different kinds of data together. Examples?



“Someone had to think of the idea of wind chill factor,” Berry said, before combining actual temperature and wind speed to reveal a new data point: What the weather will actually feel like.



<h3 class="wp-block-heading">More big data delusions</h3>



Berry wasn’t the only presenter who badmouthed the state of <a href="http://searchbusinessanalytics.techtarget.com/news/2240100743/Predictive-analytics-and-big-data-The-good-the-bad-and-the-ugly">big data and predictive analytics</a>. Karl Rexer, founder of the consulting firm Rexer Analytics, went so far as to suggest that the current crop of data scientists suffers from a bit of delusional thinking.



In his 2013 Data Miner Survey, respondents indicated that the size of data sets is getting bigger. But when Rexer asked them how many records are in a typical data set they use for analyses, “We get the same answer we got in 2007,” he said.



That’s not to say big data is a farce or to give short shrift to the <a href="http://searchcio.techtarget.com/news/2240178592/Hadoop-framework-breathes-new-life-into-Ancestrycom-legacy-tools">interesting work some are doing</a> in this space, he said. “But for the typical analytic predictive modeling/data mining/whatever-you-want-to-call-it project, I would say the overall sample size used for those data mining projects is not increasing.”



<h3 class="wp-block-heading">Name that acronym</h3>



<a href="http://searchcio.techtarget.com/news/2240186181/Learning-native-tongue-of-your-business-is-key-to-reviving-role-of-IT">Translating the language</a> of analytics into something the business can understand is challenging. One way Paychex, a payroll, human resources and benefits service provider, deals with the language barrier is by, well, using language the business suggests.



<h4 class="wp-block-heading">PREVIOUSLY ON THE DATA MILL</h4>



‘Cookie stuffing&#8217;: A <a href="http://searchcio.techtarget.com/opinion/Cookie-stuffing-A-data-scientist-takes-on-seamy-side-of-online-ads">data scientist</a> tackles sleazy side of online ads



Do businesses have the patience for good <a href="http://searchcio.techtarget.com/opinion/Does-business-have-the-patience-for-data-science">data science</a>?



How semi-structured data drives <a href="http://searchcio.techtarget.com/opinion/Semi-structured-data-is-king-of-LinkedIns-recommendation-engine">LinkedIn analytics</a>



“When we build a model, we’ll run a naming contest for the users,” Tom Kern, a risk-modeling analyst for Paychex, said at Predictive Analytics World. Kern’s department will send users an email with a short description about the model and suggest a couple of words to get them started. The users have to come up with “an acronymic name,” he said. So there’s SAM, the sales anticipation model, and TIM, the territory identification and mapping model. “Still working on a TOM,” he quipped.



If the business users’ suggestion is chosen, they get a gift card, and the company gets its users, such as the sales staff, to think about what the predictive model really does.



<h3 class="wp-block-heading">The Tide turns</h3>



The Procter &amp; Gamble Co., one of the biggest consumer goods retailers in the world, announced plans to release a lower-priced version of the laundry detergent Tide in an effort to attract mid-tier customers. Bold move or bad decision?



“One of the big concerns? If you launch products like these, not only are you going to attract folks you currently don’t have, but you’re going to encourage consumers to trade down,” said Shel Smith, partner and founder of <a href="http://searchcio.techtarget.com/news/2240204410/Zipcar-CMO-taps-data-driven-marketing-to-personalize-the-business">marketing analytics</a> firm Twenty-Ten Inc.



That’s especially true after a recession that forced so many consumers to be price-conscious. But Smith, for one, has faith in P&amp;G’s strategy. He believes the company will use predictive modeling, lots of data and highly targeted marketing to make new customers but keep the old.



“There’s something they know that we don’t in terms of their ability to maintain the existing franchise, but go after new consumers by being far more surgical,” he said.

BIGDATA