You have reached the final lesson of this course, congratulations!

If you managed to solve most of the problems (especially if you've crashed the Extras as well), you are in a good spot. Even if you struggled with the previous lesson, but simply because you are in high-school or first year of university and that material is somewhat new, you still have decent chances of passing the interviews to high-paying companies. Most of them look for people with good problem solving abilities and basic foundational knowledge in Probability, Statistics, Programming and maybe Machine Learning and you have done a lot in both of this regards.

• Lesson 1: This is an introduction to the world of trading and interviews. In particular, its problem set contains a ton of problems on probability that have actually came up in the recent interviews. Even though you of course may not get the literally same problem in your interviews, it is highly recommended you visit this problem set.
• Lesson 2 & Lesson 3: These two revolve around the concepts of correlation, variance and around the simple linear regression model. Despite its simplicity, it happens to be insanely useful and questions about it do come up in the interviews. Luckily, even a high-schooler could understand this, but it does get quite theoretical at certain points. It is recommended you are 100% confident with all of the classworks as well the problem sets from these two lessons.
• Lesson 4: This lesson is mainly about the explore part, the course is called "Trader/Quant Job: Explore & Prepare" after all.. However, understanding the order book is a useful skill, it gives you some intuition about trading. But more importantly for passing some of the interviews is the Problem Set: this is where you can get to do some real data analysis and learn the basics of Python libraries like Pandas or Matplotlib. The Extra to Lesson 2 also has a problem to practise those skills.
• Lesson 5: This lesson can be viewed as a part of the trailer campaign for what should come next in your preparation path. Despite companies prioritising great problem solving skills over most other skills (like knowing what ), many companies have a strong preference towards people who know decent amount of continuous probabilistic theory, are on good terms with calculus and Linear Algebra. For more on this, please see (or wait to be launched) the SP-2 course, i.e the sequel to this one.

Given that you are now comfortable enough with the aforementioned things, let's briefly overview an application process as well as the crucial preliminary steps:

• CV: This is a summary of your skills, achievements and experience that you use to apply for jobs put on one A4 page. If you are not sure what this is, don't have one ready this very second or have not updated yours in like 6 months: change this! This is of outmost importance not only for getting into Finance, but anywhere...
• Quanta's SP-1 course or other preparation: Well, given that you are reading this is a part of SP-1 course and if you were actually solving the problems from the problem sets, then you are good here :)
• Wait for the right time to apply: If you are looking for an internship, then the good time to apply is generally September-October. If you are looking for a job (junior position somewhere), then it does not quite matter unless you know some inside information (like that some company just doubled it revenue so they are looking to hire 50 new people in the next few months).
• Apply to 10 companies that are not your favourite: There are two things to take into account, one is that interviews are time-consuming and another one is that you are likely to not do the best at first few interviews. Hence, do not apply everywhere right away, pick 10 companies that you think are fine-ish and view them as "practise ones".
• Scattergun: Once you do a few interviews with the companies that are not your favourite, start applying everywhere. Unfortunately, there is a big element of luck involved and you literally cannot predict where you will end up. Also, just a remark: applying via your friends (via referrals) does increase your chances. So if you have friends from the companies you want to apply to or if you have a large network, that helps.

This course is not sponsored by any of the trading companies, and I personally do not care which company you end up in. So, below is the list of companies that you can consider applying to (I plan to sort them a bit later on as well, but for now you will just have a huge list) and I do not aim to promote any particular one of them. Please note that it is not one or two or three companies that are good, and the rest are trash. No! The big difference between the company you have heard of and the company you have not heard of is in how the marketing team of the company operates. Most of them do not care as much and it is absolutely possible you will be earning more and/or learning more in a company that no one has heard of. Just like I have... :)

Jump Trading
Hudson River Trading
Qubist
QuantCo
G-research
Optiver
SIG (Susquehanna International Group)
Jane-streetGSA Capital
BMO capital markets
Tudor Investement Corporation
Quadrature
D E Shaw group
DRW
Five rings
Squarepoint
Akanu Capital
Two sigma
Alameda
XTX Markets
Point72
CitadelJP
Morgan Tower Research Capital
IMC
WorldQuant
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Morgan Stanley
Bank of America
Merrill Lynch
Capula Investment Management LLP
AQR Capital
PanAgora
Lazard
QuantRes
BlackRock
RG Niederhoffer
Winton
Quantile Technologies
XR Trading
SMB Capital
Tibra
Symbolic Partners
Chicago Trading Company
Flow Traders
Volant Trading
Allston Trading
Belvedere Trading
Dare

As pointed out above, many companies do prefer people with decent theoretical knowledge. Hence, the next step to increase your chances of getting and also explore the Finance even more, i.e dive in deeper into even cooler Nobel-Prize level stuff, is to do the sequel of this course, i.e SP-2. Besides that, you can make sure you are on good terms with the content from SP-1 (in particular you know how to solve most of the problems) as well as you can practise solving interview questions in the last Problem Set of this course: