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I was also looking for an answer to this and unfortunately there is no concrete answer to this in the trail answers.Let me give you the facts.If you are applying online, the balance will reflect in your new PF account’s passbook within 15 days post approval from past/ present employer (as you choose while applying). But please note that it will initially be a ledger balance (Remarks in passbook: Amt received. credit subject to verification).It will take another 15–30 days for various levels of approval at EPFO office before the entire amount is actually transferred to your new PF passbook.Only after that you should apply for online withdrawal.Hope this helps.

Prohibit the SEC from finalizing its proposed rules on Regulation D and Form D.Make it easier to qualify as an accredited investor. Dodd-Frank made it more difficult to qualify as an accredited investor, and set up a situation where the SEC is going to review the standards every 4 years and probably raise them, crimping a key source of capital for early stage companies. Let's repeal these provisions of Dodd-Frank. We ought not be making it more difficult for people to qualify as angel investors.Reduce the financial thresholds to qualify as an accredited investor. A million dollar net worth is too high. Consider lowering it for investments of less than a certain amount.Add to the definition of "accredited investor" "experienced" angels, even if they don't meet the financial thresholds.Pass a law making it clear that angel groups do not have to register with the SEC as broker-dealers. The JOBS Act provision is imperfect. Fix it.Repeal Section 413 of Dodd-Frank.Repeal Section 926 of Dodd-Frank.Repeal the new rules which are going to require venture funds to fit within certain narrowly defined categories in order to be exempt from investment adviser registration--these rules are going to hamper the ability of VC funds to be creative in their approach to their industry and hamper angel funds as well. They are unnecessary.Repeal the AMT as it applies to Incentive Stock Options.Repeal income and employment tax on the non-cash gain on the exercise of stock options.Repeal Section 409A as it applies to startups.Repeal the investor verification rules.Pass a simple, usable crowdfunding law that won't require SEC regulations to become effective.Disallow states from collecting any fees from any startups raising funding or requiring startups to file any forms with any state agencies as long as they are complying with the federal securities laws.Repeal the Form D filing requirements.Provide a new safe harbor for startups raising money, which essentially insulates startups from shareholder securities fraud lawsuits as long as the investors aver that they realize that they are very likely going to lose all of their money.Repeal Rule 701's mathematical limitations.Reverse the presumption on Section 83(b) elections (or, alternatively, allow longer than 30 days to file such an election).Extend the rollover period under IRC Section 1045. Right now the length of the rollover period makes Section 1045 largely unusable.Make the 100% 1202 qualified small business stock tax exclusion, including the AMT exclusion, permanent.Shorten the holding period under IRC Section 1202. Reduce it from 5 years to 2, for example.Allow investors to recover their invested capital in stock sooner than the disposition or complete worthlessness of the stock.Allow a tax credit on investment.

Check with the department administrators if you can mix and match classes from 2-3 "Specialty Areas." My guess is that you probably can. Explain why. IF you can't justify your reasons, use this answer as a guide.Based on information from a private conversation, use data scientist positions as one of your backup plans, if not your primary aim.From my EECS (ECE + CS) blog, you would notice that there is a lot of computer modeling and programming in electrical and computer engineering (ECE). Specifically, check out Pasquale Ferrara's answer to What amount of programming is there in electronics and communication engineering? You mentioned that programming, or rather software development (including debugging, software testing, and what not), is one of your weaknesses. So, yes, you need to work on that. However, if you take CS classes this Fall (or whenever you begin your graduate program) without knowing how to develop software with a decent amount of efficiency and effectiveness, you will become toast in your graduate CS classes. You need 90% to get an "A," and 80% to get a "B." Poorly functioning or partially functioning software won't make the cut. So, if you are unprepared, you may do badly in the CS classes and hurt your chances (because of the bad G.P.A.) of using your CPT to get internships next summer, or during the academic year after 1-2 semester(s).See Choosing a Graduate Program in VLSI Design & Related Areas: Things to Consider to look at the desired skill set for software developers (point #2 of the desired skill set of EDA engineers).However, remember that programming is only one of a set of tools that engineers use to solve real-world problems.You would need to learn advanced statistical analysis (e.g., design of experiments and multivariate statistics), stochastic modeling (e.g., that class in random processes), and numerical analysis (e.g., linear algebra, differential equations, and vector calculus) and relevant numerical methods. With your BS EE degree, you should already have the math background that I mentioned. If not, you need to work on them. If you have this skill set, in addition to decent writing and analytical thinking skills, you have the basic skill set for data scientist and other analytics positions. To distinguish yourself from the pack, instead of applying for any analytics (e.g., business analytics, social analytics, and web analytics) or data scientist position, consider domains where you have an expertise, such as health care analytics, sports analytics (e.g., specifically for basketball or even cricket), and the music/film industry. Be unique, and know your preferred domain. My suggestion is to find a domain in EE, and dominate it. E.g., Intel is known for automating its manufacturing process in the mid-/late-2000s; see Intel's Automated Manufacturing Technology (AMT) at Intel's AMT enables rapid processing and info-turn for Intel's DFM test chip vehicle and Automated statistical process matching across the virtual fab. So, you can be a data scientist for Intel's manufacturing process; they use a lot of data mining and machine learning. Also, they use computational geome try and a small amount of computer vision to examine the masks, layout (output from the physical design process), and die micrographs. But, you have to take classes in semiconductor manufacturing, which your privately mentioned university has a decent reputation in.You can couple computer vision and robotics as a two-pronged attack in your graduate education and career, and use machine learning and data mining as tools to help you solve problems.Once you realize that all these algorithms, techniques in mathematical analysis/optimization, and what not are merely tools employed by engineers to solve different problems, then focus on a market that you are passionate to solve problems in. The tools need for all fields are pretty much the same anyway, even if you go into social network analysis (requiring graph theory + nonlinear dynamical systems, which is a traditional approach to modeling social networks).How do you couple computer vision and robotics? Use cyber-physical systems (CPS).Assumption:You have a background in control systems, given that you have a BS EE. Control engineering is used in robotics and CPS (or embedded systems, if you like).Some of these seem like intermediate classes that upperclassmen (juniors and seniors, or 3rd and 4th years in BS EE programs) and junior grad students (MS and junior Ph.D. students) take. If you have taken them, you can proceed to the advanced classes. Your university may require a placement test to place out of the intermediate classes, which tend to be prerequisites for the advanced classes. Check with the department administrators.Suggestions are based on the graduate ECE coursework at North Carolina State University (NCSU), where the anonymous question asker is going to.Classes for cyber-physical systems (CPS):ECE 516 System Control Engineering (all CPS, or embedded systems, have to be stable)ECE 521 Computer Design and Technology (CPS contain embedded systems, which include hardware and software)ECE 535 Design of Electromechanical Systems (your CPS needs to interface with the real-world, which tends to be "analog" in nature; think continuous-time domain). Robotics involve mechanical systems, too.ECE 561 Embedded System DesignECE 570 Computer Networks (for networked embedded systems)ECE 574 Computer and Network Security (you wanna secure your CPS network, right?)ECE 575 Introduction to Wireless Networking (think wireless sensor networks, or multi-agent robotic systems)ECE 720 Electronic System Level and Physical Design... The Electronic System Level, ESL, component is a critical part of CPS design and verification. Just take this class and learn about physical design; it shouldn't kill you. You probably won't have to develop physical design tools, such as placement and clock network synthesis tools. Again, see Choosing a Graduate Program in VLSI Design & Related Areas: Things to Consider, and read the relevant sections about electronic design automation (EDA); I hope you realize that physical design is part of EDA, so are ESL tools.ECE 756 Advanced MechatronicsECE 776 Design and Performance Evaluation of Network Systems and ServicesAssuming that you have taken ECE 561, ECE 521, ECE 570, ECE 574, I suggest that you take the following classes in order of priority (not in temporal sequence): ECE 720, ECE 756, ECE 516, ECE 535, and ECE 575. You can skip remaining classes, like ECE 776.Classes targeting CPS domains in embedded computer vision (Pasquale Ferrara's answer to What is Embedded Computer Vision and how is it different from Embedded Systems in terms of application and careers?), as well as manufacturing, consumer, rehabilitation, and domestic robotics:ECE 522 Medical Instrumentation (for medical and health care robotics, including robotic surgery and rehabilitation robotics)ECE 555 Computer Control of RobotsECE 739 Integrated Circuits Technology and Fabrication Laboratory (for manufacturing robotics and data scientists/analytics positions in the semiconductor industry)ECE 763 Computer VisionSkip ECE 522 and ECE 739, if you are not interested in such domains.Classes to sharpen up your skills in mathematics, stochastic modeling, statistical analysis, software development, software development, and computational thinking:ECE 517 Object-Oriented Languages and SystemsECE 542 Neural NetworksECE 751 Detection and Estimation TheoryECE 752 Information Theory (not so helpful)CS classesCSC 503- Computational Applied Logic (for formal verification of embedded systems; but without a formal verification class, it is useless on its own)CSC 505- Design and Analysis Of AlgorithmsCSC 510- Software EngineeringCSC 517- Object-Oriented Languages and Systems (probably the same as ECE 517)CSC 520- Artificial Intelligence ICSC 521- Artificial Intelligence ProgrammingCSC 522- Automated Learning and Data Analysis (read: machine learning class)CSC 541- Advanced Data Structures (to prepare for technical interviews)CSC 546- Management Decision and Control Systems (for data scientist/analytics positions)CSC 548- Parallel SystemsCSC 554- Human-Computer Interaction (helpful for robotics; think man-machine interface)CSC 570- Computer Networks (for networked embedded systems)CSC 575- Introduction to Wireless Networking (see ECE 575)CSC 579- Introduction to Computer Performance ModelingCSC 580- Numerical Analysis ICSC 583- Introduction to Parallel ComputingCSC 712- Software Testing and ReliabilityCSC 714- Real Time Computer Systems (for CPS)CSC 720- Artificial Intelligence IICSC 722- Advanced Topics in Machine LearningCSC 724- Advanced Distributed Systems (for networked embedded systems)CSC 762- Computer Simulation TechniquesCSC 772- Survivable Networks (for networked embedded systems)CSC 775- Advanced Topics in Wireless Networking (for networked embedded systems)CSC 776- Design and Performance Evaluation of Network Systems and Services (probably the same as ECE 776)CSC 779- Advanced Computer Performance ModelingCSC 780- Numerical Analysis IICSC 783- Parallel Algorithms and Scientific ComputationAlso, check out the special topics classes when information about them becomes available to you, whenever you sign up for classes (do this for each semester). Some of these classes are incredibly awesome, challenging, and fun. Note that some special topics classes are not offered every semester/year, or ever again.If you have been lazy to pick up software development skills prior to taking machine learning and computer vision classes, take CSC 510, CSC 517, CSC 541, CSC 712, and CSC 505 to start with.For machine learning, take CSC 520, CSC 521, CSC 522, CSC 546, CSC 720, and CSC 722.Wrapping things togetherRobotics can be a growing area, depending on how cyber-physical systems (includes robotics) pan out. Historically, careers in robotics tends to be restricted to defense, and some manufacturing markets (e.g., in the automotive manufacturing industry). WIth consumer robotics for health care (e.g., rehabilitation robotics) and domestic robotics (including vacuum cleaners and toys -- think Lego robotics!) becoming more prevalent, yes, careers in robotics are looking hot. Ditto for embedded computer vision. Cameras for photo taking and video recording are in many embedded systems, such as smart phones and cars (rear-end video cameras for automating or facilitating reverse parking). When autonomous vehicles hits the market, more embedded computer vision systems will be needed. So, yes, CPS ties robotics and computer vision together, nicely. Ditto for machine learning, which can be used in the design of robots and employed in computer vision algorithms/techniques.The macro emerging/growing tech trend to pay attention to is cyber-physical systems (including networked embedded systems -- see Internet of Things).So, with a background in EE, you can work in domains of cyber-physical systems involving computer vision, robotics, and machine learning, such as embedded computer vision systems for robots (implemented with machine learning techniques).