Do i really need a masters degree for data science / machine learning ? Applied Maths background I am a senior student in a 5-year integrated Masters degree in Applied Mathematics and i have selected a specialization in Mathematics for Computer Science(Data structures,Algorithms and Complexity,Graph Theory etc.). I have a good understanding of Statistics, Probabilites etc. and decent knowledge of Python,R,Java,SQL,PHP. Last year i had a class of "Data Analysis using R" and i loved it so i was thinking of following a career in data science and machine learning and therefore doing an Msc in this field. 

Unfortunately i don't have good grades and therefore it is pretty hard to make it for a Master's degree in a good university,also i don't think i could make it for this year applications so that means i will have to wait another year until i can apply again. 

So i was wondering if i realy need a masters degree for a junior level job in this field, since i think i am half way there with my background, or if i could make it by studying on my own for some months(or a mooc) about ML,Big Data etc ?
I work as a data scientist. We hire many people with a bachelor's degree in computer science, statistics, applied math, etc. Having a master's degree is better. If you have a master's degree in applied math, most data science jobs will be happy to look at you. 

Be warned of course that there are various levels of 'data science' positions out there. Some of our junior employees literally just sit and query data bases for us all day. Or they are responsible to create nice looking images in Tableau. (Which has very little to do with "real" data science). I personally would find it to be mind numbing work. But it pays the bills for them, so it is what it is. To get onto the teams that do real research, we often require a graduate degree and some formalized abilities in machine learning. 

Compile a portfolio of your strongest projects.

Being able to demonstrate some projects in ML you have worked on can go a long way. The more examples of independent projects in ML and data science that you have done, the better. Projects that demonstrate an understanding of what is going on beneath the surface are the best. (Anyone with basic R skills can run a RandomForest analysis. Being able to explain how you changed your parameters to optimize your model is another story). 

I will comment on your grades here. For junior positions, grades honestly do not matter for the most part. All we would care about is if you can do the work. For higher positions, someone with a mediocre GPA is not going to be looked at as strongly. Most of us excelled in school. Much of our work requires study and applied learning. Applicants with mediocre GPAs usually do not have the specific study aptitudes we want. If your GPA is lower than about a 3.7, and you want to work into senior positions, be prepared to really demonstrate your value with a strong project portfolio. 

One piece of advice on applying for junior level positions. Data science positions tend to fall into two categories: data analyst and data researcher. There is not a lot of fluidity between the two tracks. People who start out as analysts tend to get stuck there long term. If you want to get into machine learning and big data, avoid applying to data analyst jobs. 

One other thing I will say: Avoid paying for a data science "boot camp" or the like. At least at my place of employment, we tend to place very little weight onto such things. I see applicants who have a BA in Classical Greek (very interesting subject, but very little job market) who have gone to a coding boot camp and expect that we will bring them in as a ML researcher or something. It does not work like that.