Guidelines For Making A Data Table: Fill & Download for Free

I get lot of queries and questions on being a CLINICAL data analyst, hence today I will explain my job profile in detail. This will be a long answer as I will try to cover various aspects of the work I do.Data analyst is of various kinds-Business Data Analyst, Clinical Data Analyst, Market Analyst, etc. Depending on the field of data, the name differs. Also, the methods, tools, software used vary so it is a vast field.I am an experienced Clinical data analyst. I have 5 years plus experience in Clinical Data Management. I have switched two companies till date. My employers, feature in the world top 10 best companies. I also have 3 Barnett certifications.I had joined my previous company as a fresher and was trained for 2 months by the client, post that I had On Job Training and that is how I became a CDA. I did not study Clinical Research, I was trained by the company.Clinical Research is a vast domain where research for new drugs is carried out by the big pharma companies. It is outsourced to Clinical Research Organizations mostly. So, our clients or sponsors would be any company whose medicines are sold in the chemist shop.I have worked for several clients, been a part of successful drugs that have released in the market. So, yes we help bring out cure to diseases and that is based on the quality we deliver. We handle patient data, we are into human data science.There are many departments in Clinical Research- Medical writing, Pharmacovigilance, Data Management, Regulatory affairs, SAS programmers, Data base programmers, Data scientists, etc. I will only focus on Data Management as it is very vast.Data analysis involves the following:1. Data collection2. Data cleaning3. Data representation4. Data analysisSo, we get projects from sponsor for a particular therapeutic area- Cardiovascular, pediatrics, oncology, auto immune etc.Based on the protocol, the data base is designed by data base programmers. We have SAS programmers to help us catch discrepancies in data based on Data Management Plan which has all details of data restrictions to be applied. Data after cleaning is represented as required by the Biostasticians, mainly as TFLs (Tables, Flow chart, Listing) and is submitted to FDA for analysis. Finally, the drug gets rejected or is approved.Data collection is carried out by medical centers worldwide. Data cleaning is done by us, Data Analysts, represented by the Bios team and analysed finally by Scientists ,etc for quality and reliability.There are many other teams working closely in this big process.As a data analyst, one should be through with the ICH guidelines, GCP and SOPs. Data is collected via EDC (Electronic Data Capture) so we need to be familiar with databases. There are many- Oracle Clinical, Inform , Rave, in-house data base etc which one must be through with.Firstly, during the set up the database, testing and QC is performed by the CDA, it is the setup phase. Below will be performed by the CDA:1. Test case writing2. TestingOnce, set up phase is over, the trial starts and data is collected which requires daily and monthly cleaning via various activities, namely:1. Query management(system and manual checks firing in data base)2. Vendor data Reconciliation3. SAE Reconciliation4. SAS Listing Output5. PD Reconciliation6. Trend analysis, if any7. Data Set review, etcEach activity is different and cleans data from various aspects.Lastly, when a projects ends, a CDA is suppose to perform data base lock activities (review activities). Finally, data base is manually locked each form wise by CDA or script run locked by database programmers.A CDA is expected to be highly skilled in Excel as data analysis can be faster. Understanding of protocol, its deviations and study design, understanding the restriction criteria is also very important. Softwares are used, for certain output. Applications are used for report pulling etc.Also, a CDA should know metrics and reporting for knowing the status of the study. Interim analysis, futility analysis, data base lock are milestones which demands clean data so quality should be always maintained.Quality cannot be compromised as its live data so expectations are quite high. With proper training and mentoring one can be a good data analyst. As time passes, speed of analysing data also increases so it gets better with time.It’s a very comfortable job, can be done from home too so may become home-based later and shift to any desired place where net connectivity is good.P.S-I hope the information is helpful. Please do not ask me about Market and Business Data Analysis. I also cannot suggest you any institutions for Clinical Research, as my Master is on Microbiology.

It's a reference textbook and not meant for reading cover to cover. That said, it's quite good for the foundation of medicine and understanding the concepts behind the disease.If you already have some idea of the topic you are going to read, I suggest reading the chapter from the end forwards. That helps you go through the management aspects first and when you finally reach the etiopathology, you will grasp better what points to stress on. The etiopathogenesis is generally vast in Harrison's, therefore starting from the beginning makes it a tedious task.Whether or not you choose to make this your go to book for medicine, do go through the first volume. Often neglected, this volume teaches you the concept behind the questions we ask while taking history and the theory behind clinical examination. This is very important especially for a medical student.The newer editions now have made the first volume smaller and focussed on symptom analysis as if to stress on this very point.The data and guidelines are often 3 to 4 years old as that's the time it takes for making an edition of this tome. Hence, for rapidly changing areas like sepsis, diabetes,hypertension, oncology and others you should also consult standard guidelines for latest treatment recommendations. This is especially applicable for medicine residents.The baby Harrison's that you have is a good supplement and will carry points of the chapter you read. It can be a handy revision tool.This book in its own self is adequate for the basics of microbiology, pathology, pharmacology and of course, medicine. A major advantage are the details of the side effects of drugs and their mechanisms described.For Indian grads, the tables of this book are high yield for MCQs. More than most books you consult in Undergraduation.Even if you don't use it after buying, it looks good in wooden bookcases :) One of the more aesthetically pleasing books in medicine there is!Hope your journey with ‘Harry’, as we call it, turns out as good and useful as mine.Good luck!

Software engineering interviews are intended to allow the hiring company to determine whether you have the skills to succeed at their job. Many would-be candidates try to game the system: How do I get an interview at company X if they aren’t actively recruiting me? What five solutions do I need to know to get past the interview? How do I make the company want me? Most of the time these are bad objectives, especially those that try to take a rote approach to mis-representing knowledge.Don’t try to game the system. The worst that could happen is that you could succeed. If you get hired into the company without having the required aptitude and skill, or if you’re mis-hired at a higher level than you belong, it will work against you. Why? Because you’ll have a miserable time working there, they’ll have a miserable time employing you, you’ll leave, and it will take a while for you to recover from the experience.Having said all that, interviewing is a skill. It’s good to make sure your interview skills are up to snuff, so that you can clearly communicate your skills and knowledge, and engage the interviewerTo prepare for data structure and algorithms questions:Review data structures and algorithms you already know. If it’s been a while since you’ve used heaps, binary trees, hash tables, stacks and queues, well, read up on them. Write some sample code with existing libraries for them. Write your own libraries implementing them.Review big points on your resume. If you have experience in a particular domain such as web design, it’s fair game for your interviewer to ask questions specific to that domain. Be able to to talk through the projects you did, especially any tough algorithms or data design aspects.Review computational complexity and storage requirements. Brush up on your knowledge of what algorithms are O(N), O(N^2), and so on. Be able to use these as guidelines for how to solve a given problem.In addition to these things, practice coding and problem solving both in a plain text editor (to emulate phone interviews), and standing at a white board. Talk through what you’re doing and why as you solve the problem. If you can, have a friend there who you can pretend is the interviewer. This will help with your purely interview skills, and reduce the chance of the interview environment getting in the way of you demonstrating your knowledge.