Personal Data Sheet Sample: Fill & Download for Free

When I was an engineer in the prehistoric, pre-internet days, I loved when sales reps would come to visit. They would bring data books with them which had their whole catalog of products. The salesperson would also bring samples and data sheets of their latest products too.It was really helpful and a great resource if you were a customer back in the day. In fact, it’s obvious the value that salespeople brought: They were an oracle of knowledge that you couldn’t find anywhere else.Then the internet happened and the bar for salespeople was raised significantly.Suddenly, you didn’t need a salesperson to come to your door to let you know about the latest and greatest product they were selling. All you had to do was go online, and all the information was there for you. Better yet, you could access it at your convenience.Sales reps would need to add lots more value.The basic, value-added, give data books to customer, role was gone forever. Now, for you to be a great sales person, you would have to act as a customer facilitator and advocate.The great thing for you, as a startup CEO, is the change in sales is a good thing.In our case, the majority of our sales, at least 90%, happened without any direct interaction with customers. Even $100,000 or $1 million orders just happened.Years ago, this never could have happened. However, the wealth of information on the internet makes it easy to sell even complex products like analog semiconductors.Every industry is being impacted by the change. We bought a new car last month, and the only interaction with the dealer was to pick up the car.You can deploy less sales people, but the sales people you deploy can have more impact.So, the question becomes, “What about the 10% of your business that requires sales interaction?”The great thing about focusing your sales force on the 10% is that these customers actually want you to help them. So give customers what they want and help them.Great sales people naturally will act as facilitators between customers and your team. A great salesperson will advocate on behalf of the customer AND understand the needs of your company simultaneously.Now, your sales team is adding value. They’re adding value from your customers perspective, and they’re adding value from your perspective.And, the 10% of the business your sales team is focused on is the most profitable and valuable business you have. That’s how you grow your business exponentially.For more, read: When Should You Hire Your First VP Sales? - Brett J. Fox

Front Cover is a Personal Data form containingProgramme Code, Course Code, Enrollment No. Name, Address, Mobile Number,Study Centre Code, Name and Address, Date, Signature.Each Assignment Contains - Two sets/pages of Mark Assessment Sheets, Question Paper along with the Assignments which has the relevant Question No. & Answer written.As I have already submitted my assignment, I don’t have a sample to show.

Truth be told first - even if it disappoints many people -the industry does not have an agreed upon definition of a data scientist!Jokes like 'a data scientist is a data analyst living in the Silicon Valley' are abundant. Below is one such cartoon, just for fun.Finding an 'effective' data scientist is hard and finding people who understand who a data scientist is equally hard. Note the use of 'effective' here, I use it to highlight that there could be people who might possess some of these skills yet may not be the best fit in a data science role. Irony is that even the people looking for hiring data scientists do not know who a data scientist is. Hiring managers post job descriptions for traditional data analyst and business analyst roles while the title calling it a 'Data Scientist' role.Everything that I say below is my experience working in a data scientist role with a major search engine and advertising platform. Instead of giving a bullet list of skills, I would first highlight the difference between some data related roles.Consider the following scenario. Shop-Mart and Bulk-Mart are two competitors in selling retail. Some higher up in the management chain asks this question: "How many Shop-Mart customers also go to Bulk-Mart?".[Please note that the question might be of interest to Bulk-Mart management or even a third party, possibly a market research or consumer behavior company, interested in shopping behavior of the population.]Here is how different data-related roles will approach the problem. ThisTraditional BI/Reporting Professional: Generate reports from structured data using SQL and some kind of reporting services (SSRS for instance) and send the data back to management. The management asks more questions based on the data that was sent and cycle continues. Insights about data are most likely not included in the reports. A person in this role will be experienced mostly in database related skills.Data Analyst: In addition to doing what the BI guy did, a data analyst will also keep other factors like seasonality, segmentation and visualization in mind. What about if certain trends in shopping behavior are tied to seasonality? What if the trends are different across gender, demographics, geography, product category? A data analyst will slice and dice the data to understand and annotate the report with it. Besides database skills, a data analyst will have a understanding of some of the common visualization tools.Business Analyst: A business analyst possess the skills that the BI and data analysts have plus domain knowledge and understanding of the business. A business analyst may also have some basic skills in forecasting etc.Data Mining or Big Data Engineer: Do what the data analyst did, possibly from unstructured data if needed. MapReduce and other big data skills may be needed. Understands the common issues in running jobs on large scale data and is able to debug the jobs.Statistician (A traditional one): Pull the data from a DB or obtain it from any of the roles mentioned above and run appropriate statistical tests. Ensure the quality of data and correctness of the conclusions by using standard practices like choosing the right sample size, confidence level, level of significance, type of test etc.The situation has changed a bit recently. Statistics departments at most schools have evolved in way that statisticians graduate with strong programming and decent foundation skills in CS enabling them to do the tasks that statisticians traditionally were not trained in.Program/Project Manager: Look at the data provided by the professionals mentioned so far, align business with the findings and influence the leadership to take appropriate action. Possesses communication skills, presentation skills and can influence without authority.Ironically the person a PM is influencing business decisions using the data and insights provided by others. If the person does not have a knack for understanding data, chances are that the person will not be able to influence others to take the correct decisions.Now putting it altogether.The rise of online services has brought a paradigm shift in software development life cycle and how business iterate over successive features and products. Having a different data puller, analyst, statistician and project manager is just now possible any more. The mantra now is ship, experiment and learn, adapt, ship, experiment and learn .... This situation has resulted in the birth of a new role - ' A Data Scientist'A data scientist should have the skills of all the individuals I have mentioned so far. In addition to the skills mentioned above, a data scientist should have rapid prototyping and programming, machine learning, visualization and hacking skills.Domain Knowledge and Soft Skills Are As Important as Technical Skills:The importance of domain knowledge and soft skills like communication and influencing without authority are severely under-estimated both by hiring managers and aspiring data scientists. Insights without domain knowledge can potentially mislead the consumers of these insights. Correct insights without the ability to influence the decision making is as bad as having no insights.All of what I have said above is based on my own tenure as a data scientist at a major search engine and later the advertising platform within the same company. I learnt that sometimes people asking the question may not know what they want to know - sounds preposterous - yet happens way too often. Very often a bozo will start rat holing into something that is not related to the issue at hand - just to prove that he/she is relevant. A data scientist encounters such HIPPOS (Highly Paid Person's Opinion) that are somewhat unrelated to the problem if not complete nonsense very often. A data scientist should posses the right soft skills to manage situations where people ask irrelevant, distracting or outside of scope questions. This is hard, especially in situations where the person asking the question is several levels up in the corporate ladder and is known to have an ego. It is a data scientist's responsibility to manage up and around while presenting and communicating insights.Below is a summary of necessary skills a data scientist should possess in my opinion.Curiosity About Data and Passion for Domain: If you are not passionate about the domain/business and curious about data then it is unlikely that you will succeed in a data scientist role. If you are working as a data scientist with an online retailer, you should be hungry all the time to crunch and munch from the Smörgåsbord (of data of course) to know more. If your curiosity does not keep you awake, no skill in the world can help you succeed.Soft Skills: Communication and influencing without authority. Understanding of what is the minimum that has the maximum impact. Too many findings are as bad as no findings at all. Ability to scoop information out of partners and customers, even from the unwilling ones is extremely important. The data you are looking for may not be sitting in one single place. You may have to beg, borrow, steal and do whatever it takes to get the data.Being a good story teller is also something that helps. Sometimes the insights obtained from data are counter-intuitive, if you are not a good story teller, it will be difficult to convince your audience.Math/Theory: Machine Learning. Stats and Probability 101. Optimization would be icing on the cake.CS/Programming: At least one scripting language (I prefer python). Decent algorithms and DS skills, to be able to write code that can analyze a lot of data efficiently. You may not be a production code developer but should be able write code that does not suck. Database management and SQL skills. Knowledge of a statistical computing package, most people including myself prefer R. A spread sheet software like excel.Big Data and Distributed Systems: Understanding of basic MapReduce concepts, Hadoop and Hadoop file system and least one language like Hive/Pig. Some companies have their own proprietary implementations of these languages. Knowledge of tools like Mahout and any of the xaaS like Azure and AWS would be helpful. Once again big companies have their own xaaS so you may be working on variants of any of these.Visualization: Ability to create simple yet elegant and meaningful visualization. In my case, R packages like ggplot, lattice and others have helped me in most cases but there are other packages that you can use. In some cases, you might want to use D3.Below is a visualization of high level description of skills needed to become a data scientist.Where is a data scientist in the big data pipeline?Here is a nice visualization of the big data pipeline, the associated technologies and the regions of operation. In general the depiction of where the data scientist belongs in this pipeline is largely correct, there is one caveat however. A data scientist should be comfortable to dive into the 'Collect' and 'Store' territories if needed. Usually data scientists would be working on transformed data and beyond but in scenarios where the business does cannot afford to wait for the transformation process to complete, a data scientist has to turn to raw data to gather insights.To be continued .....----------------------------------------------------------------------------------------------------------Note: I am publishing this without any edits/reviews. I will update with more thoughts as I get a chance. I am writing myself a note to finish this answer in the next one week. Pardon the typos and scattered thoughts at least for now.------------------------------------------------------------------------------------------------------------