Assessment Services Math Review Book: Fill & Download for Free

UPDATEI’ve created a concept map for all the material covered in this answer:Also one for all code present in today’s popular Python Cheat Sheets:A learning path is a sequence of courses, projects and reading that help people achieve a working knowledge of a topic. Each of these can be broken out as follows:Content: What should I learn?Approach: How should I learn it?Tracking: How do I know I’m making progress?Let’s begin by looking at what our options are regarding content. Then we’ll look at how to use that content inside an effective approach to learning. Finally, I’ll show how the student’s learning can be assessed by tracking how much content has been assimilated.CONTENTStudents cannot read and practice all possible resources. Think of this section as an array of options from which the student will pick from in the approach section that follows.For assimilating content I recommend the following levels of effort:This breakdown places most effort on projects. This is because there is no substitute for working on real projects when it comes to instilling a working knowledge. Reading helps you stay up-to-date and remain aware of emerging techniques. Courses provide a quick exposure to topics that will be relevant throughout your career.For courses, a recent listing shows the following top-rated options:1. Machine Learning A-Z™: Hands-On Python & R In Data Science2. Python for Data Science and Machine Learning Bootcamp3. Deep Learning A-Z™: Hands-On Artificial Neural Networks4. Complete Guide to TensorFlow for Deep Learning Tutorial with Python5. Deep Learning Certification by Andrew Ng from deeplearning.ai6. Data Science and Machine Learning Tutorial with Python – Hands On!7. Data Science and Machine Learning Bootcamp with R8. Scala and Spark for Big Data and Machine Learning Tutorial9. Machine Learning Certification by University of Washington10. Artificial Intelligence: Reinforcement Learning in Python11. Machine Learning Certification by Higher School of Economics12. Advanced AI: Deep Reinforcement Learning in PythonHere are their ratings (rated from 1–5)Also check out David Venturi’s article on machine learning courses ranked by reviews.Some will naturally be suited to the student’s learning style more than others. Look across their high-level topics and narrow the list of possibilities using the student’s interest, the course rating, and any technology requirements (in this case you mentioned Python specifically).For projects, the student should work through online tutorials and attempt to reproduce their objectives. Note that these are self-starter projects, not “projects” provided by courses. Self-starter projects do not come baked with a set timeline or supervision. They require the student pull in resources as needed to solve the problem. Some projects are small exercises to learn a single concept, others encompass a wider span of knowledge.Here are some beginner tutorials:Kaggle’s Machine Learning Tutorial for BeginnersGoogle TensorFlow’s Beginner TutorialMachineLearningMastery Beginner TutorialConor Dewey’s Online Guide to ML in PythonBoth aspiring and working data scientists often use Kaggle to hone their skills. This is an easier way to get data for a project, and even provides a computing environment to get started quickly.Projects center around the machine learning workflow. These are the core steps that occur on every machine learning project. It will be the same thinking your student takes into a real job. Here are resources to learn more about each step.DATA GATHERINGHow to scrape websites with Python and BeautifulSoupConnecting to MYSQL with Python in 3 stepsHow to connect to PostgreSQL using PythonPython Excel Tutorial: The Definitive GuideGetting Started with Python and MongoDBRedis with PythonConnecting to Microsoft SQL server using PythonA thorough guide to SQLite database operations in PythonA step-by-step SQLAlchemy tutorialDATA PREPARATIONHow to Handle Missing Data with PythonRemoving Outliers Using Standard Deviation in PythonHow to correctly use scipy's skew and kurtosis functions?Class Imbalance in Credit Card FraudHow to Clean Text for Machine Learning with PythonMODEL BUILDINGCommonly-Used Supervised Learning AlgorithmsOrdinary Least SquaresRidge RegressionLassoElastic NetBayesian RegressionLogistic regressionNearest NeighborsNaive BayesDecision TreesSupport Vector MachinesConvolutional Neural NetworksLSTMEnsemble methodsCommonly-Used Semi-Supervised Learning AlgorithmsLabel PropagationCommonly-Used Unsupervised Learning AlgorithmsK-MeansAffinity propagationMean-shiftSpectral clusteringWard hierarchical clusteringAgglomerative clusteringDBSCANGaussian mixturesBirchCore ConceptsBias-Variance TradeoffCurse of DimensionalityRegularizationComplexityBackpropagationMODEL VALIDATIONComputing cross-validated metricsCross validation iteratorsShufflingCross validation and model selectionExhaustive Grid SearchRandomized Parameter OptimizationSpecifying an objective metricSpecifying multiple metricsParallelismRobustness to failureInformation CriterionOut of Bag EstimatesCommonly Used Validation MetricsAccuracy scoreCohen’s kappaConfusion matrixHamming lossPrecision, recall and F-measuresHinge lossLog lossMean absolute errorMean squared errorR² score, the coefficient of determinationAssessing Data VolumesValidation curveLearning curveDEPLOYMENTPutting Machine Learning in ProductionDeploying Machine Learning models in Production as APIs (using Flask)Docker for Data Science WorkflowsProjects are worked on using the tools of data science. These include languages, libraries and services that enable building and validating machine learning models, prototyping applications, and versioning/deploying code.LanguagesPython (A Complete Tutorial to Learn Data Science with Python from Scratch)Librariesnumpy (fundamental package for scientific computing with Python)scipy ( Python-based ecosystem of open-source software for mathematics, science, and engineering)pandas ( high-performance, easy-to-use data structures and data analysis tools)Matplotlib (2D plotting library)scikit-learn (machine learning framework)Statsmodels (functions for estimating statistical models)TensorFlow / Keras (deep learning framework)IDE / NotebookJupyter (Dataquest Tutorial)Version ControlGitHub (Hello World · GitHub Guides)ContainersDocker (Get Started, Part 1: Orientation and setup)Front-End PrototypingJavascript - JavaScript TutorialReact - Tutorial: Intro to React – ReactAzle - (https://azlejs.com)For reading, the student should use top-rated books from well-known sources:Packt Publishing Machine Learning BooksO’Reilly Machine Learning BooksAmazon Best Sellers in Machine LearningHere are some popular hands-on machine learning books from Packt Publishing:Students should also become accustomed to reading journals. The most common in data science circles is arxiv.Recent Arxiv Machine Learning articlesAPPROACHThe first section showed a lot of content. Rather than overwhelming the student, make sure they view the above list as a box from which they can draw from, as they use their approach to learning; one possibility outlined in this section.For courses, these should only be treated as a high-level overview, and viewed as an ongoing supplement to reading and projects. Beginner courses can be used at the early stages of a learning path, while more advanced courses can come later, and even leveraged periodically throughout the student’s career.For projects, a student’s motivation needs to be higher than in courses. This is where data scientists forge their skill set and gain confidence going forward. Struggling through real projects instills deep intuition about how to map machine learning to challenges. I recommend projects to both beginners all the way to senior managers. Projects keep you close to the latest technology and the problems we need to solve.For reading, students should begin by choosing books that are more practical. This will supplement their self-starter projects more effectively, and help them learn concepts in context. For journals, these can be skimmed at first, requiring deep reading only when an aspect of research is particularly interesting or bears relevance to their project.To outline a learning path I will use 6 months as an example. This can be reduced or expanded as needed. Obviously the timeframe will dictate the level of knowledge a student can bring to his or her first job. Notice how each month groups progressively more advanced options from the content listed above.Month 1CoursesData Science and Machine Learning Tutorial with Python – Hands On!ProjectsBeginner Tutorial, Python Excel Tutorial: The Definitive Guide, A thorough guide to SQLite database operations in Python, How to Handle Missing Data with Python, Ordinary Least Squares, Nearest Neighbors, K-Means, Accuracy score, R² score, the coefficient of determination, Bias-Variance Tradeoff, A Complete Tutorial to Learn Data Science with Python from Scratch, numpy, pandas, Jupyter (Dataquest Tutorial)ReadingPackt Publishing Machine Learning BooksMonth 2CoursesPython for Data Science and Machine Learning BootcampProjectsGoogle TensorFlow’s Beginner Tutorial, Connecting to MYSQL with Python in 3 steps, How to connect to PostgreSQL using Python, Removing Outliers Using Standard Deviation in Python, How to correctly use scipy's skew and kurtosis functions?, Lasso, Elastic Net, Naive Bayes, Decision Trees, Curse of Dimensionality, Cross validation and model selection, Cohen’s kappa, Confusion matrix, Precision, recall and F-measures, Learning curve, scipyReadingMachine Learning with Python CookbookMonth 3CoursesDeep Learning A-Z™: Hands-On Artificial Neural NetworksDeep Learning Certification by Andrew Ng from deeplearning.aiProjectsHow to scrape websites with Python and BeautifulSoup, Class Imbalance in Credit Card Fraud, Support Vector Machines, Ensemble methods, Affinity propagation, Ward hierarchical clustering, Regularization, Complexity, Hinge loss, Log loss, Azle TutorialReadingHands-On Machine Learning with Scikit-Learn and TensorFlowMonth 4CoursesMachine Learning Certification by University of WashingtonProjectsGetting Started with Python and MongoDB, How to Clean Text for Machine Learning with Python, DBSCAN, Gaussian mixtures, Exhaustive Grid Search, Randomized Parameter Optimization, Putting Machine Learning in Production, Deploying Machine Learning models in Production as APIs (using Flask), Docker for Data Science Workflows, jQuery TutorialReadingThe Elements of Statistical LearningMonth 5CoursesArtificial Intelligence: Reinforcement Learning in PythonProjectsA step-by-step SQLAlchemy tutorial, Birch, Specifying multiple metrics, Parallelism, Statsmodels, GitHub (Hello World · GitHub Guides)ReadingLearning From DataMonth 6CoursesComplete Guide to TensorFlow for Deep Learning Tutorial with PythonAdvanced AI: Deep Reinforcement Learning in PythonProjectsRedis with Python, Convolutional Neural Networks, LSTM, Information Criterion, TensorFlowReadingDeep Learning (Goodfellow)Eventually the student’s projects take on more product-relevant concerns (attaching a front-end to their data pipeline, testing code, using version control, containerize their applications, and operating under continuous integration. These kinds of tasks ensure data scientists can work with developers and data engineers on scalable projects.TRACKINGFor a student to know they are making progress they need a way to measure their learning, and track progress throughout their learning path. A simple method is to retain the levels of focus between courses, projects and reading, outlined at the beginning, and count their completion at the end of each month.Here is an example:We see the relative focus between courses, projects and reading maintained throughout the learning path. The number is simply how many of each was finished by the end of the month. For example, month 1 has 2 courses, 7 projects and 5 readings. Keep in mind projects include smaller tasks, and readings include both books and journals. The journal count can be much higher, but students shouldn’t read them at the expense of working on projects.The reason total counts taper off towards the end is because the student is doing more advanced work; projects will involve additional engineering practices, and reading will involve deeper concepts. The focus also shits from consumption to creation. Students should approach later projects using their own original approach and ideas. Reading should be turned into blog posts, and courses can even be constructed for those less experienced. Learning truly happens when we create.Once their career in Data Science starts the student’s learning will occur on the job. I recommend always having a personal project on the go. I also recommend students expand their reading into areas less obvious to machine learning.Additional Areas:CommunicationIntroduction to Public SpeakingPresentation skills: Designing Presentation SlidesTell Stories With DataBuilding the MindsetWhat it Means to “Do” Math in Data Science – Towards Data ScienceMachine Learning and Statistics: Two Cultures, One Big OpportunityThe Interview ProcessThe Data Science InterviewOn a final note, there is no perfect plan, and the student shouldn’t be discouraged if their efforts don’t match the expectations set out by the learning path. Everyone learns best in their own way. A path like the one outlined here merely provides an anchor to learning. Not every Data Scientist’s profile is the same. What’s your profile?Ultimately, I stick by my assertion that there is only one skill you should be concerned with. If you are constantly trying to solve problems in your area of interest and create tangible solutions for peoole to see, you have all the learning you need.

The universities are not stupid, they know that it is difficult to justify admitting low IQ students, particularly when they have good test results from SAT or ACT. They want to admit on the basis of race, not aptitude, so the simplest way is to proceed without evidence of academic ability.I will provide you with a little bit of information illustrating the importance of these tests and a few items relating to their defense against ill informed objections to them.SAP Software Solutions | Business Applications and Technology/ SATs carry more weightCollege admission officials, after years of downplaying SAT's importance, say several recent high school trends are forcing them to lean more heavily on test scores as the only valid measuring rods to compare students.They still insist a student's high school record - courses taken and grades earned - carries most weight in college admission decisions. But when a high school record is unclear - as is increasingly the case - a standardized test score can be all that's left."In many cases, the high school transcript tells you zip," says James Walters, dean of admission at the University of North Carolina-Chapel Hill. "There is real resistance among admission people to putting more weight on the SAT, but we are being forced to do it."Among the trends responsible for SAT's new importance:Gradeless high schools. More schools each year, including some elite math and science academies, are dropping the traditional A-F grading scale. Instead students merely "pass" a course or earn verbal evaluations such as "satisfactory." Gone with the grades is the traditional grade-point-average that colleges use to evaluate a student's performance.P. 206 - Selligman; A Question of IntelligenceThe Educational Testing Service, which designs the SAT, today declines to cite an IQ correlation, but Henry Chancy, a former president of ETS, has been quoted (in Klitgaard, Choosing Elites, 92) as stating that the SAT is essentially an intelligence test. A correlation of .80 between IQ and the GATB was reported by John Hawk, a scholar based at the US Employment Service, in a telephone conversation. In 1998, I interviewed a testing specialist in the Department of Defense, who told me that DOD had made a point of not even trying to ascertain the correlation between IQ and the ASVAB. When I mentioned that I had heard it was around .80, he laughed and said -- for for attribution -- "Yea, that's about right."P. 167The SATs are in effect intelligence tests (they correlate about .80 with the Wechsler Adult Intelligence Scale)... Correlations between standard IQ tests are usually in the 0.7 to 0.8 range. Examples:WAIS to S-B = 0.77WAIS to Raven's = 0.72WAIS to Otis = 0.78WAIS to SAT = 0.80Validation of the Frey and Detterman (2004) IQ prediction equations using the Reynolds Intellectual Assessment Scales/ A. Alexander Beaujean, et al./ Personality and Individual Differences 41 (2006) 353–357This paper confirmed the Frey and Detterman 2004 equation to predict IQ from the SAT. Beaujean showed that there were other ways to approach the same relationship.Scholastic Assessment or g? The Relationship Between the Scholastic Assessment Test and General Cognitive Ability/ Meredith C. Frey and Douglas K. Detterman/ Volume 15—Number 2004 American Psychological SocietyThis was one of the first papers to address the SAT:IQ relationship. I heard the paper presented at ISIR by Frey, when she was still working on her doctorate. Since then, there have been various changes to the SAT, but the g loading of the test is beyond dispute; this is why it has subsequently been used as an IQ proxy in various cognitive science papers. It is helpful to look at the scatter plot, since that shows the range over which the relatiohship exists.The Wall Street Journal - Breaking News, Business, Financial & Economic News, World News and Video - The Truth About the SAT and ACT - Nathan Kuncel and Paul SackettMyth: Tests Only Predict First-Year GradesLongitudinal research demonstrates that standardized tests predict not just grades all the way through college but also the level of courses a student is likely to take. Our research shows that higher test scores are clearly related to choosing more difficult majors and to taking advanced coursework in all fields. At many schools, the same bachelor’s degree can be earned largely with introductory courses or with classes that approach the level of a master’s degree. Students with high test scores are more likely to take the challenging route through college.Tests also predict outcomes beyond college. A 2007 paper published in the journal Science presented a quantitative review across thousands of studies and hundreds of thousands of students, examining the predictive power of graduate-school admissions tests for law, business, medicine and academic fields. It showed that the tests predict not only grades but also several other important outcomes, including faculty evaluations, research accomplishments, degree attainment, performance on comprehensive exams and professional licensure.Myth: Tests Are Not Related to Success in the Real WorldFundamental skills in reading and math matter, and it has been demonstrated, across tens of thousands of studies, that they are related, ultimately, to job performance.A 2004 meta-analysis published in the Journal of Personality and Social Psychology looked at results from a test that was designed for admissions assessment but was also marketed as a tool for making hiring decisions. Though originally intended as a measure of “book smarts,” it also correlated with successful outcomes at both school and work.Longitudinal research has demonstrated that major life accomplishments, such as publishing a novel or patenting technology, are also associated with test scores, even after taking into account educational opportunities. There is even a sizable body of evidence that these skills are related to effective leadership and creative achievements at work. Being able to read texts and make sense of them and having strong quantitative reasoning are crucial in the modern information economy.Myth: Beyond a Certain Point, Higher Scores Don’t MatterOne of us examined four large national data sets and found no evidence, in either work or academic settings, of a plateau where all relatively high scorers were roughly equal. If anything, the relationship between scores and success increased as scores went up. One theory for why this occurs is that people who score higher are more likely to seek out highly complex academic and work settings, where their cognitive skills are especially important.Myth: Test Prep and Coaching Produce Large Score GainsResearchers have conducted a mix of experimental studies and controlled field studies to test this question. They have generally concluded that the gains due to test prep are more on the order of 5 to 20 points and not the 100 to 200 points claimed by some test prep companies.One review found a typical gain of 15 to 20 points on the math portion of the SAT and 8 to 10 points on the verbal portion. One of us conducted a more in-depth analysis of 4,248 high-school students and, after controlling for prior scores and the differing propensity of students to seek coaching, we estimated a gain of 14 points on the math test and 4 points on the verbal.

Let's be straightforward - MBB-level consulting is hyper competitive and not having the best grades possible, all things being equal, is going to hurt your chances. However, if your education is done and dusted and it's too late to knuckle down and improve, then don't panic - you can still be a strong candidate for an MBB-level job if you approach things intelligently!Crucially, there are no strict formal requirements for consulting in terms of qualifications. Good grades certainly don't guarantee a job either, and many academically-outstanding candidates are rejected (a common complaint would be that many very bright candidates are lacking in the kind of soft skills are crucial in consulting).Whilst top results from Ivy/Oxbridge/equivalent level universities in immediately-relevant subjects are certainly a big plus, what recruiters are really looking for is candidates with a particular skillset - and they will look wherever they need to to find them. This means that there is always room (within the bounds of reason) for a candidate with an excellent skillset and a compelling narrative to be hired in spite of a sub-optimal educational history.The question, then, is how to convince your recruiters you do indeed have those skills. This is far from impossible and, if your educational history is not the typical one for the office you are applying to, all is not lost. However, you will have to pay more attention to what you are doing to "make up" for places where you are not so strong.Now, pragmatically speaking, the average MBB-level consulting application tends to be skimmed for approximately 30 seconds before being rejected. And the average application is rejected, as over 50% of candidates are cut at this stage. Obviously, then, the application phase is crucial for everyone, but even moreso for you, as this is going to be the stage where you would be most likely to be arbitrarily filtered based on something like education.As such, your application needs to be the very best it can be, with all your relevant achievements properly presented and your relevant skills clearly flagged to quickly inform the recruiter that you are indeed the sort of person they are looking for.We have free comprehensive resume and cover letter guides to help you do this and optimise your application. Our resume guide also includes a free template, to make sure you get the formatting etc absolutely correct (resume formatting is not the time to stand out from the crowd - it will irritate those trying to read it quickly and make it look like you don't know what you're doing).Consulting applications have significantly higher demands to meet than those for just about any other industry, and you are going to have to invest some real time to get them right. As our guides explain, an ideal cover letter in particular is going to be the result from things like targeted networking with current/recent staff at the office (or at least firm) you are applying to and/or significant research into some of their recent projects. You should start with these activities immediately (if possible) and ideally some time before you need to submit the application.Given how crucial the application is, I would strongly suggest you consider having it reviewed by a professional to make sure it does everything it needs to. Our package includes three rounds of review by an experienced MBB consultant. Nobody else will be give you such genuinely useful feedback.InterviewOnce you make it to interview, you are going to be on a more even footing with the competition, as consulting firms will always value performance which they have directly assessed themselves over what they read on your resume. If you ace your interviews (not just the case - the fit components too!) you should get an offer - regardless of your grades!This is easier said than done, though! If you thought doing a proper application was a lot of work, buckle up, as it's only the tip of the iceberg. If you want to land a job you are going to have to put in a lot of prep, regardless of your background.The very first thing you are going to need to do is have a clear plan for your prep. Consulting candidates tend to be hard workers and many make the mistake of just jumping in and "getting on with it" without a plan of attack. However, this leads to them wasting time on areas they are already strong on and ending up with blind spots in certain other essential skills which they wholly neglect.The specifics of what you need to cover and in how much detail will vary depending on your specific background. However, to get you started, let's run through a general outline of what you will need to get through.1. General SkillsStraight off, candidates almost invariably neglect their mental math. This is a crying shame, as it is something you can be practicing right now for free without leaving your desk. We have a free mental math tool on our website to make sure your skills are sharp. You will also find a lot of useful material - including lots of invaluable "hacks" to make your calculations much more time efficient - in our consulting math article. The video lesson in MCC Academy then expands this material significantly as the gold-standard source on this most critical of consulting interview skills.Note that, even if you have a strong foundation in academic math, do not assume that this will carry you through interview. As we explain in our math article, consulting math is a very different beast to the academic variety.Beyond math, depending on your background, you will need to make sure you are up to speed on fundamental business concepts. You can try to do this by yourself by getting a few textbooks out of the local library. Good luck with this unstructured approach, though, as you are likely to waste time ploughing through a lot of irrelevant material and then miss crucial elements which you need.By contrast, our MCC Academy course has a set of video lessons which give you all the foundational knowledge you need to perform in interview and do so as efficiently as possible - the lessons are fully comprehensive without wasting your time on irrelevant material.2. Case CrackingYou are going to need to make it through case interviews to land any consulting job. As such, you will need to both learn how to tackle case studies and then practice as much as possible to make sure that the method is second nature to you by interview day.You will very probably have encountered the old fashioned "framework-based" case cracking systems, such as Case in Point. However, these will not get you very far and I cannot caution strongly enough against basing your prep around them.Framework-based approaches rely on a fundamental assumption that all business problems fit into one of a small, finite number (usually 12 or so) of schemes. In reality, you already know that this cannot be the case, as, if all business problems could really be solved by applying the contents of one book which is already in the public domain, then there would be no need for consulting firms to exist at all!Given that your interviewer will typically base your case study on a recent project they were involved with - and that this was therefore a difficult enough problem to merit bringing in consultants - you can expect that your case will not be one that can be solved with simple frameworks.MyConsultingCoach's founding mission was to offer a better alternative to dysfunctional framework-based approaches. By contrast, we teach you how to flexibly approach every case on its own merits, employing the same techniques as a working consultant. Thus, with our Problem Driven Structure method, not only will you be able to tackle whatever your interviewer throws at you, but you will be doing so in exactly the way they want to see.Our MCC Academy course teaches you the fundamental consulting skillset and before explaining how to employ those skills within our Problem Driven Structure method.3. Fit InterviewA lot of candidates spend all of their time prepping for case studies, thinking that they are "the important bit". This is an enormous and very damaging misconception. In reality, firms absolutely take fit interviews juts as seriously as case interviews.Fundamentally, the fit interview is a test of more "soft" skills, which cannot be easily assessed in a case study, but are still absolutely essential to the day-to-day work of a real consultant. What is more, no "average" is taken between case and fit interviews. A consultant will need the entire skillset they assess so - if you want to get the job - there is no alternative to performing well in both case and fit interviews.The fit interview is also a test of your motivation to work in consulting. Consulting firms have big problems with losing talent, and many new recruits drop out because they can't handle the demands of the job or because they always intended to parachute into another industry after gaining the minimum useful amount of consulting experience. Your interviewer is looking for someone whose backstory makes consulting the natural next step and who has the determination to stick with the job for the long haul.You can read more about the details of fit interviews and how to prepare in our article here. We created the first fit interview course, which is now also contained within MCC Academy (indeed, this is the component that completes the MCC Academy as a true end-to-end course, giving you everything you need for interview success). Given that so many candidates fail to prepare for fit interview, this is a place where you can really steal the march on the competition, so it makes sense to actually put the effort in for fit prep!4. Practice makes PerfectAs you are learning how to approach cases, you will want to first run through worked cases (available at our free case bank) and then practice solving case studies by yourself. As you become more confident with the relevant techniques, you will want to start practicing with peers to better simulate the real interview. Similarly, you will want to practice fit interview questions with real people. MyConsultingCoach has you covered here, with a free meeting board which allows you to find and quickly schedule meetings with fellow applicants partners from all around the world.Whilst it will never be a service that is for everyone, you should also seriously consider case practice with an experienced consultant. Realistically, there is only so much decent feedback non-consultants can give one another. After this point, it is simply a case of the blind leading the blind.By contrast, nobody will be able to more quickly identify your points for improvement and generally provide as useful feedback as a real consultant. Whilst these individual's time will never be cheap, when costs are balanced against even just the increased chances of your receiving an MBB salary, you can quickly calculate that it is a very worthwhile investment (we have actually done the math on this here). If you are interested, we offer coaching with experienced MBB consultants.Anyway - I hope that all helps! Good luck!