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A lot... If you really want to be outstanding in electrical and electronics engineering (EEE), otherwise simply known as electrical engineering (EE) in the U.S.. It can also be known as electrical and computer engineering (ECE).I strongly disagree with Razvan Baba. I may be wrong, but he does not seem to have a good grasp of ECE, in terms of breadth across the scope of ECE or depth in any area of ECE.Look at IEEE journal and conference papers. Talk to faculty and graduate (MS/Ph.D.) students in ECE, as well as senior R&D engineers in ECE. Ask them if they can get away with programming and computer modeling.Programming in MATLAB for mathematical modeling, system/filter design and analysis, and simulation is used in control engineering, signal processing, antenna design, and many areas of ECE. Just check out the MATLAB and Simulink toolboxes: Products and Services. Challenging and academically rigorous classes, especially at the graduate level, will require you to implement or even design algorithms for control and signal processing. So, yes, you will need to understand algorithm analysis and design. Think about circuit complexity in VLSI circuit and system design. Isn't that a lot like computational complexity? Does the algorithm run in O(n^3)? Is the complexity of the circuit O(n*log n)?Furthermore, modern/advanced control systems are multi-input, multi-output, stochastic, adaptive, digital, autonomic, and/or nonlinear. CS students will not want to learn about nonlinear dynamical systems. Trust me. That is why the application of control engineering in autonomic computing has very few takers. Why? You need to be good at ECE and CS; that is, EE + CS = EECS.Many, if not most, computer science (CS) programs do not train you to design analog/RF and mixed-signal integrated circuits (ICs), and VLSI circuits and systems. So, all the talk about using hardware description languages (HDLs) for RTL design of ICs that are implemented on FPGA boards or standard cell logic is nonsense. Look, if they do not even teach computer organization classes that require students to design a simple 32-bit processor, do you think they can teach VLSI design or computer architecture effectively? Hell no! Behavioral modeling of AMS/RF circuits and systems with SystemC-AMS, Verilog-AMS, Verilog-A, and VHDL-AMS? Forget about it! If you want to design ICs and VLSI systems, pursue a MS/Ph.D. in ECE at a good research university (preferably in the U.S.). And, yes, VLSI system design may involve modeling with UML, invoking design patterns, using Petri Nets, and using formal/mathematical logic for formal methods and formal verification. VLSI design also involves programming in Perl, Tcl, and Python (or UNIX shell scripts), in addition to C, C++, and SystemC. I consider behavioral modeling in SystemC, Verilog, and VHDL as VLSI design, rather than programming. You are designing electronic systems and ICs, not programming a processor (as in system software, or application software, development).Instead of using a word processor for documentation, you can do that with LaTeX and Doxygen (works with VHDL, too!). Want to draw something? Use Graphics Layout Engine (GLE), Asymptote (vector graphics language), MetaPost and TikZ!Use build automation to compile, synthesize, or run your scripts/tools. Or, typeset your LaTeX source files. E.g., put a UNIX shebang at the top line of your SPICE netlist and run it like a script from the command line.Use revision control to manage different versions of your source files: MATLAB (or GNU Octave), C++, SystemC, Verilog, Python, Tcl, Perl, UNIX shell scripts, LaTeX source files, and SPICE (yes, you can write your own SPICE netlists from scratch and simulate them with a SPICE tool). Markdown works for GitHub, if you use that to commit your source code for MATLAB, Verilog, VHDL, scripts, C++ code, and what not.To work effectively in a UNIX-like operating system, knowing how to write simple UNIX shell scripts quickly helps you work efficiently and effectively. Learn how to use regular expressions.Take an advanced graduate class in antenna design, and you will have the "luxury" of implementing complex numerical methods in C, C++, FORTRAN, or some other programming language to model your antenna, simulate electromagnetic wave propagation, and analyze the system for electromagnetic interference and capability (EMI/EMC).Nanoscale device engineering will inevitably demand computational modeling and numerical computation in C, C++, Verilog-A, or other languages used for device/compact modeling.ECE-based approaches to systems engineering and reliability engineering will involve a lot of computer modeling and programming.Information theory and communication theory basically is based on mathematics and theoretical computer science. Being able to implement your ideas/methods as a computer program allows you to test your ideas and verify/validate them. Think about methods for encoding/decoding, and error detection and correction.Optical engineering and telecommunications allow you to explore different techniques for transmitting information, which is usually modeled in computers (think programming, again!), and examine everything from the performance and energy consumption of routing data packets in a telecommunications/computer network or network-on-chip (NoC). Yes, optical NoCs can use similar packet routing concepts as packet routing in telecommunications and computer networks.Other aspects of telecommunications: multimedia compression? Yes, you can implement them as software in C, C++, or MATLAB, or as VLSI circuits in SystemC, Verilog, or VHDL.Power engineering? The design, modeling, and analysis of electrical machines would involve modeling with LabVIEW, a graphical computer language (or graphical "programming" language, if you like). Smart grid design? Definitely a lot of computer modeling (read: computer programming).Now, which part of ECE does not involve programming? See IEEE Society Memberships and IEEE Technical Councils for the scope of ECE.Bottom line: You can't run away from programming in ECE. If you hate it, remember that a lot of programming and computer modeling is used in financial engineering and computational finance. So, if you wanna make big bucks in investment banking that exploits your ECE skills, think again!Addendum:[Read the last portion to address the question of whether to accept the offer to study EEE, or try to switch to (or join) a CS program.]You can learn much more skills and languages in good internships, where you are thrown into real-world projects and are expected to perform to justify your US$20/hr - US$40/hr pay check. Unfortunately, good internships where you can actually develop/design stuff and learn aren't that easily available in many areas around the world. See Pasquale Ferrara's answer to Which is better to study for a short term winter course, with the hope of a future foreign internship: Embedded systems or VLSI design?.A lot of undergraduate classes in many ECE programs may not involve programming. It all depends on where you go to college. I had to implement some numerical methods for my applied/engineering math classes in vector calculus and differential equations. But, I had a choice of programming languages to choose from. Many math classes in other programs won't require you to. Ditto for many undergraduate ECE classes in electrical machines, analog circuit design, and what not. However, at good graduate programs in the U.S. for MS/Ph.D. students, you will have lots of opportunities to learn and program, since you will have lots of projects to complete (on top of any research for Ph.D. students).Generally, they don't make you learn something for no reason. If you have to learn a bunch of languages and software to use for your engineering projects, it may be because they want to expose you to different design steps for that particular area/subfield. So, you don't have to learn that many skills and computer languages, if you don't want to.For example, for digital VLSI design, I learned Verilog for RTL design, SPICE for cell characterization and circuit simulation, and Tcl for driving/customizing EDA tools. That said, I did not get to learn SystemVerilog nor hardware verification languages, such as e and Vera. In industry, for entry-level jobs and internships in VLSI design, you need to know how to work in a UNIX environment, program in Perl or Tcl (and hopefully SKILL from Cadence Design Systems), and use Verilog (or VHDL) for RTL design. Why? Because that is what you will be paid to do. So, the technical questions are meant to determine if you can work effectively in your job, or if they have to spend a ridiculous amount of time training you. Training employees may be common in some places, such as India. In the U.S., you need to have demonstrated use of your skills in class projects, open source hardware/software projects, and prior work experience. If your source code is on Open Cores and GitHub, evaluating your skill set is so much easier.I use MATLAB as much as possible, so that I don't have to use R and what not.Using LaTeX helps me write documentation much easier. As a freshman, I started writing papers and reports with >40 references, which can be hard to manage in number-based citation/referencing style in Microsoft Word (back in the day)... The notion that ECE documentation and research papers do not involve mathematics (discrete mathematics, numerical analysis, or even abstract algebra), stochastic modeling, and statistical analysis is nonsense. Writing mathematical equations in LaTeX is so much easier than with word processors, especially if your computing environment is well set up and you have a good process for technical writing; hint: macros and templates help a lot.I used to hate UNIX, but when I saw my classmates destroying me in my mandatory CS classes in computer systems (assembly programming projects), data structures and algorithms, and software engineering, I had to pick up more skills from them so that I can be effective in my class projects. Working in the computer lab instead of my dorm room helps me interact with others, and learn how to work more effectively and efficiently. Yes, it can be distracting when others bug you for help and try to chit chat with you. But, you get to learn how the top students (possible rockstar engineers/developers) do something in 3 hours that takes you days to do it. So, you learn tricks that help you work better. Basic stuff like unit testing, test automation, regression testing can be applied to EE projects, too. Ditto for fault isolation, decoupling of modules (to reduce dependencies), and what not. This set of skills can be summed up as computational thinking, which can be learned in any academic major and be applied to any profession. See Computational Thinking and Pasquale Ferrara's answer to What tangible non-platform-specific skills do computer scientists pick up through their undergraduate education?.To paraphrase Michael Jordan, remember that the fundamentals don't change, and all that changes is your attitude/approach towards them. Reference: M. Jordan. I Can’t Accept Not Trying: Michael Jordan on the Pursuit of Excellence. Harper San Francisco, San Francisco, CA, 1994.Basically, programming, like mathematical analysis, and knowledge of physics are basic skills (or "tools") that you employ to solve real-world engineering problems. Don't use more tools/skills than you need, since you want to save time and effort (and $$$). But, if you can't outcompete your competitors with your current skill set or tools, re-tool yourself and pick up more advanced skills. Programming is not the be all or end all of engineering.The technologies come and go. You need to pick up new tool-specific skills, platforms, computer languages, and what not over time. However, the fundamentals in engineering design, verification, validation, and testing do not change. Modularity is modularity, and it exists in software architectures, VLSI architectures, embedded systems, large engineering systems from cars and airplanes to telecommunication systems. Ditto for fault isolation, fundamental concepts in electromagnetic wave propagation, and what not.Yes, not going to a good engineering program to earn your BS ECE (or equivalent) and advanced degree (MS/Ph.D. ECE) affects your choices and opportunities. However, as you realized, there are a lot of learning resources online to help you grasp what you are missing out on. Joining IEEE and ACM helps expose you to what your peers are doing in their free time, for fun (e.g., IC design, and publishing their novel circuits in a research conference), and taking graduate ECE classes for MS/Ph.D. students while they were still undergrads. Studying in universities with good undergraduate programs, such as those that I mentioned in Pasquale Ferrara's answer to When recruiting Software Engineer/Computer Science majors for US companies, what international universities are on par with MIT/Stanford?, helps a lot, too. If you can't study in Politehnica University of Bucharest, fine, ask your friends who may be studying there. Ditto for the other top engineering programs in your region or the world, such as the Technion. It's okay if you can't have 100% of the opportunities that some students have. 70%, or even 30%, isn't that bad. It is better than 0%.Like I said, asking people in industry and monitoring job advertisements as you progress through college and graduate school will you figure out what skills employers (or rather, hiring managers) want.As for why good ECE programs encourage you to build up a broad base of skills spanning ECE (as aforementioned), and yet more skills in critical areas, such as technical writing, technical/engineering management, and intellectual property law, well, it is to enable ECE graduates to explore different career paths. You don't have to study in the U.S. to realize that a B.A./B.S. (or equivalent) is a basic degree in many professions (such as medicine, law, ECE, and CS). Some career paths may not require more than a B.A./B.S.. But, if you want to outcompete others, you need to have a unique skill set that may be obtained in MS/Ph.D. programs.WIth this in mind, since not everybody wants to be a computer architect, you will find ECE graduates (like many other graduates) venturing into different professions. Some NCAA Division I student-atheletes graduating with BS ECE may become professional athletes. Others may go into management consulting. Some may choose to teach science and math in high schools, while others may teach English to non-English speaking people in Europe or East Asia. Some go to law school and pick up their law degrees (e.g., J.D. or LL.B.), and become lawyers in intellectual property law for high-tech companies. Some become start-up entrepreneurs, and/or venture capitalists. This is where the breadth of skills come in handy. Ditto for having intercultural competence. Also, this helps you in interdisciplinary research, if research is your cup of tea.I may be terribly wrong, but I believe that there are much more low-skilled software engineers and web developers than VLSI designers (read: digital IC designers). There are much more VLSI designers than AMS/RF IC designers. The proportion of rockstar engineers/developers is very small. The number of highly-skilled software developers is also very small, especially in niche topics like embedded computer vision and electronic design automation; these people typically have MS/Ph.D.s in CS and/or ECE.The number of not-so-good BS/MS CS programs is huge. It is very hard to differentiate yourself in many traditional software development jobs, even for full-stack developers. Front-end developers? Forget about it. Pick something that you know many, if not most people, hate and are weak/poor at but that you love. You will be much better off working in that sweet spot, which will enable you to out-innovate the large amount of software developers in Latin America, Europe, South Asia (including India), and East Asia (e.g., China, South Korea, Japan, and Taiwan).Also, the hot emerging technological trends in big data, cloud computing, and cyber-physical systems (facilitating the Internet of Things) allow you to exploit ECE skills better than CS graduates. Most CS graduates are weak in numerical analysis, physics, and engineering. They cannot handle the engineering, statistical analysis, stochastic modeling (ever heard of CS students volunteering to take graduate ECE classes in random processes at MIT, Berkeley, Stanford, and USC? Probably not.), and continuous-time domain aspects of cyber-physical systems (CPS). Exploit this. Now, you are collecting so much information in your CPS devices anyway. What should you do with them? Big data analysis! Domain-specific analytics, whether it be for basketball, financial analysis, or medicine will be the sweet spot for you to tap into your wisdom and insight regarding your passion (whatever it is).Bottom line: An ECE degree may help you separate yourself from the pack more easily. However, it is not for everybody.P/S: Whatever you do, don't listen to bigots like Razvan. He is not just anti-American, anti-CS, but also foolish and not-so-smart.

The school teaching profession in America draws the bulk of its practitioners from the most intellectually pedestrian minds in our civilization.It is a pitiful and even cruel irony but the fact is that if you are the parent of a child enrolled in our public school system, high chances are that your child is being instructed by someone who would once have been at near or the bottom of the class while you were a student at university.The “education” major at university consistently ranks at the bottom when it comes to university entrance test scores, both at the graduate and post-graduate levels.And if you doubt the validity of that as a reflection of the relative intellectual caliber of students in various majors, consider that the ones at the top are such subjects as physics, mathematics, philosophy and engineering. Then if you think that mathematicians or engineers on average are smart people (which they are), know that that very same ranking places future school teachers right at the bottom of that scale.Let me be clear here that this is not an expectation that school teachers be among THE sharpest people in the room.That is never going to happen. So let’s have some sense of realistic expectations here.There is no nation where school teachers are amongst the smartest people going around. The sharpest minds typically go into professions which either make far more money (e.g. medicine, law, banking) or which involve a lot of analytical thinking (e.g. research scientists) or both of those factors (e.g. engineers).But while the teaching profession cannot be expected to draw the sharpest tools in the shed, it shouldn’t be scraping the bottom of the barrel either.How did this come to happen?It’s hard to chalk that down to compensation levels, not if we’re comparing it to other developed nations at least.American public school teachers may not be paid like surgeons or bankers but they’re still the second-best paid among the OECD nations for elementary school and the fourth best paid for high schools.The best and worst countries to be a teacher, based on salary(I’m discounting Luxembourg here since that matchbox of a place is inconsequential to be worth considering in rankings of any kind. Sorry, it just is.)So if you complain that you cannot expect anything but the stupidest people to be teachers in America given the pay level, how much more monumentally stupider then would you then expect teachers in most other developed nations to be?No.What we now have is the dregs at the bottom of the class at university now opting to become school teachers.But that’s just half the sorry tale really.Because it gets even worse for a couple of reasons.The caliber of stuff they have to study in these education courses.A friend of mine was once enrolled at university as an education major because she genuinely was passionate about that profession. Halfway through she simply quit and when I asked her why, her reply was that she couldn’t put up with the utter drivel which apparently passed muster as university courses in this major. She said it with a deep sense of both sadness and resignation.The “last one in, first one out” policy.Some of the most talented, bright and motivated members of the profession are among the younger cohort. But the rules stipulate that any cutbacks on staff be based purely on seniority and not merit. It produces the most perverse effects. My sister dated a guy a few years ago whose own sister was a young school teacher in Utah. This woman was clearly bright and won several teaching awards. Soon after that she was fired because of a cutback in staffing requirements. It didn’t matter how clearly good she was at the job, it’s “the one who got hired last will be the one to get fired first”.Tell me, what other profession operates in this kind of spectacularly stupid fashion when deciding whom to keep and whom to let go?Can you imagine if Google or Intel decided to make their layoffs based on this idiotic principle?“Just fire the last group of engineers we hired - screw their performance, we must keep the older engineers even if their productivity is shit.”Place yourself for but once now in the shoes of a soul who is intellectually bright and desires to become a teacher because they genuinely like teaching children.Then imagine the prospect of these below, one after another.Step 1 - You find yourself at university in the company of classmates who are the most intellectually mediocre as compared to the kids in premed, engineering, philosophy or practically 90% of any majors.Step 2 - You then have to sit through mind-numbingly stupid study courses which have you feeling your brain cells are actively atrophying.Step 3 - You somehow managed to make it through that and get a job. And know that your head is among the first on the chopping block, never mind how well you did it.How many intelligent and motivated young persons would toughen it out through all three of the above?How many have the kind of stomach to digest three straight shit sandwiches in a row? Especially among those who know how talented they are.Highly talented intellect invariably comes with an equally proportional sense of pride and entitlement. One which invariably viscerally screams“Screw this thing, I don’t need to put up with this kind of nonsense when I can well be in another place where my talent will be more respected or at the very least, far better compensated.”Why be surprised then when you draw intellectual snails? When the system you have on offer is composed of intellectual mud?I read answers here all the time on Quora of downright pedestrian quality not in terms of morality but in terms of analytical thinking. Lacking not as much in character as utterly in caliber.Stuff which is so elaborately written but so weakly thought out that it takes a certain level of inverted ‘genius’ to fit such a pauper of an idea into such rich attire of verbosity.Then I often look up who that person is and their profile says that they either are a school teacher in the U.S. or once were one.And I can’t help wondering with dismay“Who knows how many young and impressionable minds did this person manage to infect through the years with such rubbish.”Because what results can you expect when you’re filling your cup from the bottom of the barrel?Would you be surprised if Silicon Valley went kaput if engineering as a major at university somehow suddenly started to attract the students with the lowest scores?Would it amaze you if you had a ton of botched up surgeries and mistreatments if pre-meds at university were among the stupidest in their cohort of classmates?Then ask why would you be surprised when it comes to this profession?No, never mind the endless popular parroting of how it needs to be among the most respected.Based on WHAT?The valiant attempt at apotheosis of what is in terms of hard facts an idiocracy?Because the way that things stand, it certainly does not warrant much respect based on intellectual caliber.Post Script.Because I know how some will draw up examples of them, please recognize the critical distinction here between school teachers and university professors. The first and foremost requirement if you want to be a professor at a major American university is that you need to be a kick-ass research scientist and publish great work. That certainly is the case in the hard sciences.You won’t get even a peep through the door-hole, let alone entry into that club unless you’ve proven your intellectual chops in spades. So yes, you say to me “Professor of biochemistry or civil engineering” and I know that we’re dealing with some serious brainpower here. You say “School teacher” and it’s anything but that.

Well, it strongly depends on:what is the area of your interest: epidemiology, ecology, widely understood medicine, pharmacy and drug development, bio-informatics (for instance merging machines with human body), clinical diagnostics and so onwhere you are going to play the role of biostatistician: public offices, pharmaceutical companies, laboratories. You can be also an independent researcher/investigator (a freelancer) supporting scientists in doing their scientific investigations and writing dissertations and so on.Let me describe briefly the challenges I had to play with, working as a freelancer, who supported scientists and as a biostatistician working in a Contract Research Organization, where I assess statistically efficacy, safety, toxicity and other measures related to new drugs and therapies.Freelancer, Evidence-Based Medicine:huge number of statistical methods and tests. I remember the first time I bought and opened my beloved handbook: Amazon.com: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Edition (9781439858011): David J. Sheskin: Books (Table of contents in PDF)The content just… got me off my feet and knocked out. So many tests, methods, assumptions, notes, corrections! How to embrace all of them? The key is to understand what they do and organize the knowledge.So I slowly started to organize my knowledge: a-ha! some tests verifies assumptions of other statistical tests. Some answer the questions about the effect (this is what you want to examine). Many questions can be answered in many ways - there is no just single way to get the answer. There are parametric, semi-parametric and non-parametric test. A-ha! But what are their relationships to each other?One of the most important moment for me was to realize that ANOVA, ANCOVA, t-test, linear regression are realizations of a linear model. Then I added a whole set of logistic regressions and labelled this GLM.This was the most important moment in my data-analysts life: I started to see relationships and analogies between statistical methods, to group them.Wiki software is very helpful for organizing the knowledge. WikiPad is a good example of knowledge-base tool, offering extraordinary capabilities of searching, navigating and graphing relationships between terms. (sorry for Polish names)Testing for normality. Gosh! I remember my confusion: why, the hell, are there so many (> 10) normality tests? Do they all test the same thing(s)? No! So why they are called “normality tests”? Do we need all of them? Does the best normality test even exist? In fact there are no universal normality test, so one should know what *exactly* he wants to check for. And WHY he really wants to test the normality…Well… yeah… why should we formally test for normality? This was the longest story and the most important question in my career. It took a long time for me to make my own deep investigation and answer this question for myself. It was complicated even more due to the fact, that the greatest authorities in statistics still discuss this very issue - and they cannot agree on…Medical terminology. Yes. You are going to be a BIOstatistician. The “bio” prefix means that sooner or later, but it will be impossible to avoid medical terminology around you. Well, remember, this is the choice of yours - you could have chosen econometrics, sociology, psychology, physics, but you chose biostatistics.The better you understand medical processes and terminology, the better you communicate with doctors/investigators, the better you “feel” the subject, the easier you see patterns and relationships and the more “independent” you are. This is the key thing!Of course it doesn’t mean you should take a full course in medicine at the university and get PhD in cardiology :) But if you don’t LIKE the medicine (or more generally - biology), you will quickly find your every day work hard, dark and misty. Investigators will use specialized terminology and mental shortcuts all the time. Let me give you a piece of advice: If you find bio-sciences difficult to learn, don’t even start.Software. Which software is the best option for you? Should you learn and use only a single package, or perhaps use different programs? My advice is: choose one and become a specialist, then learn the basics of other packages and learn how to mix them.Seriously, you should recognize this area thoroughly, as this will be your future workshop. SAS? R? Stata? It’s all up to you. But when yours choice is done, take it seriously. Invest some time (and money) and become a specialist. Learn the basics right before you take your first tasks. You cannot waste your time on learning things when you’re supposed to do your job in timely manner!Differences between statistical packages in: types of sums of squares (SAS: III, R: I), contrasts, formulas (even for quantilles!) and corrections are a big source of headache. Not to mention different methods of handling dates, missing values and so on.Real data challenge. I don’t know how it looks like in other areas of science, but bio-processes usually produce very “unpleasant” data. Forget the nicely shaped (bell-shaped) distributions. Forget the “lack of outliers”. Forget about automated methods of removing them. Forget about clean situations: mixed distributions, skewed distributions, “suspicious observations”, “influential observations” and lots of confusion (especially in clinical diagnostics), missing observations and missing classes, unequal sample sizes are likely to become your every-day reality.And you will have to deal with them. Removing outliers is often really bad idea (they often bring important information about the process or form a separate sub-group), data transformation doesn’t “cure” the shape of a distribution (and removes the default, well-established meaning from data), normality tests fail (but is this bad?), the size of a sample is often low (this is not “Big Data”, where you have billions of records at your disposal), co-linearity, variable dependency and strange patterns in model residuals are waiting for you :)Violated assumptions of statistical methods. This is the direct consequence of the previous paragraph. You will discover bootstrap methods, robust methods (M-estimators, quantille regression and others) and the power of Central Limit Theorem. You will learn to love mixed models over repeated ANOVA (which has some strong assumptions).New statistical methods. Deming regression. Survival analysis. Meta-analysis. New tests, like Tajima-D, Tryon’s, Westlake-Schuirmann’s. TOST (for testing bio-equivalence). Complex mixed models. Non-linear models, including GAM.Biostatistician in CRO - clinical researchConfirmatory analysis. Mostly. Forget the exploratory analysis, where you could pick any method, depending on data, and freely experiment. In the world of clinical research everything is planned a’priori and written down in Statistical Analysis Plan. You didn’t anticipated issues? You have to switch to non-parametric methods (due to violated assumptions) but they’re not described in SAP? Sorry, you lose! Any change must result in amendments to SAP and Protocol, and you must have bloody good explanation and justification of the changes. You will quickly learn to predict “bad things”.SOPs. SOPs everywhere… Standard Operating Procedures regulate almost everything you (could) do and how you do. Calculation of the sample size, writing statistical programs, validating the programs, storing input data, writing the report, organizing and managing files, contacting Spor - everything is regulated in details. Even the process of writing SOPs is… covered by another SOP :)Regulated environment. Every possible aspect of the data analysis is going to be regulated. Data storage. Analytical software (including the process of updates and configuration; you will have to set up a library of packages/codes/scripts and tools you usually use; it should be tested and versions - frozen). Backup policy. The process of versioning documents and tracking changes. Controlling access to objects.You will quickly start to think in a context of “processes”: who owns and initiates the process, how it is requested, what is to be done, who performs this, who controls this, what is the product and how it is documented (logs and trackers).Validation. Full validation. Every critical part of any program must be validated by another statistician or statistical programmer. But that’s not all. Not only the programs are going to be validated, but also the whole computing environment. This is done in detailed audits.Responsibility. The game stops here. This is not a game. You play with humans life. OK, not directly, but the decisions will be taken upon the results of your analysis.Training. You will be trained constantly. Not only in statistical guidances (ICH, FDA) but also in GCP (Good Clinical Practice), prevention and detection of frauds and misconduct and, of course, SOPs.I think these are the most important things you should be aware of.