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I want to be a Data Scientist

I have Maths and French literature double degree. Learning some Python and can do it just for Pandas. Looking for an entry-level job but hard to figure out where to start because there is very few senior data scientist in the field. Almost every company want data people but they don't even know what they exactly want for them. Which kind of company could I helm me excel myself in this field? data datascience

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Nitasha’s Answer

Hi Claire, you have the right degree in Math and Statistics to get your hands dirty with Data Science, I would also recommend taking some online courses and doing some projects on your own with tons of "test datasets" which are available online. Do some predictive analysis of this data and see how you can apply your math and statistical skillsets to derive some meaningful insights from this test data.

Create blogs or project work around it, so that you could share it during interviews. There is a need for lots of data scientists in any industry especially corporate as every company wants to learn patterns about their products, see insights on how their product is going to perform in future etc. Just having Data engineers in their company is not sufficient, a lot of demand for Data Scientists. You seem to have the right skill sets including Python too, so doing some home projects on your own with creative test datasets would be the first step for you to understand what you could do with it. Add this to your resume, and apply to Data Scientists open positions to companies like Atlassian, Facebook, Google, Amazon, or any company of your choice.

Good Luck with your little home project and start applying with confidence that you have all potential to do it. Keep pushing till you find the right job!
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Zhebei’s Answer

Hi Claire, data science is broad and can be approached from three angles.

-What business problems do you want to solve? This is the area of study. Different industries have different problem domains. However, some problems like custom retention, chatbot, fraud detection are common across industries.
-What statistical models and machine learning models can be used to solve the problems? Nowadays you have suggested models for many problem domains.
-Tools you can use to develop the models. Python and R are the two programming language data scientists often use. There are user-friendly tools (Alteryx, Dataiku, DataRobot, etc) you can leverage. Most data scientists are proficient with at least one visualization tool (Tableau, PowerBI) or library (Seaborn, R-Shiny) .

As a beginner, I would suggest that you choose a field / problem domain that you like to study. You will then be inspired to learn ML models and tools.
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Charley’s Answer

I agree with everyone who said that going the route of the data analyst and then working your way into a data science role is ideal. Sometimes the hardest part of the role is getting to understand the data and how it's arranged - this is something you'll do a lot of as a data analyst. Working as a data analyst also allows you to build on your technical skills so that once you get to the data scientist role, your skills are sharp.

With regard to the degree question - I will tell you that I was able to get into an entry-level Decision Science Analyst role without a degree in a quantitative study (I have a Bachelors in Business Administration and an MBA). HOWEVER, I was turned down for a recent opportunity at a higher level because I lacked the degree. This very well could just be how things are at my particular company, but I will tell you that I am currently enrolled in a Master of Science in Data Science program so that I can check that box. (But that's just me.)

In my experience, the degree teaches you more of the theoretical nature of the job, while boot camps and on-the-job training get you the actual technical skills.

Wishing you the best of luck!
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Sudhansu’s Answer

Hi Claire,

There are two aspects to be successful in data science.

First one is technology - good programming skill with strong foundation in mathematics, data structure, algorithm is essential

Second one is domain expertise - in my observation this is a key skill to develop. It can be business or technology domain (or both). The more someone has understanding in the domain, it will be easier to conceptualize and build new analytics rich products.

Wish you good luck!
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Firoz’s Answer

Areas of understanding or skills that can be very helpful include:
-- Data Visualization
-- Tableau
-- SAS
-- SQL
-- Foundational understanding of data modeling
-- Understanding of how data flows from systems to landing areas to data use layers
-- Hadoop
-- Impala
-- Python
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C’s Answer

For what it's worth, degrees are not very indicative of ability (for example, my undergrad was in Philosophy, focusing on Language and Logic). What matters more is your capability to produce. Go to competition websites (like Kaggle) and join some of the offerings there. You'll learn a lot, make some connections with others in the field, and start to establish a track record of what you can and cannot do. You might even make a little money while you're at it.
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John’s Answer

Agree with Navya. Find a job related to data science that will allow you to explore the field and grow your skills while also contributing to the company in some meaningful way. Sometimes these are not the top paying jobs in the short term, but will pay off for you in long term as you become more valuable. Data Analyst is a common role where you can do this.

My primary role was as a network architect, but I had interest in data science and automation, so I chose roles and projects that allowed me to explore data related to my area of expertise, where I could relate data science to something that I already knew. I could compare how I looked at data before using data science, and find new ways to look at the same data to find new value in it using algorithms I learned, read about, or wanted to explore.

Next steps are always - go try it! Consider data from your current role and think about what you COULD do with it if you were an expert data scientist. Get hands on and try some things out with open source software. I prefer python and Jupyter notebooks.


Thanks John :) Claire Y.

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Navya’s Answer

As you already said, this is a new field and has very few experienced seniors in there. However, look at it this way. If you get into this career now, 5 years down the line, you will be one of the very few people with good experience. I know its tough to find an entry level position. But if you do not have the experience, nobody will hire you for a senior position. Most companies take the effort to train its junior employees through learning and development and through support system from the team. Try to find a job and put in effort at least for an year to get comfortable with the data science job and see what else you can do from there.


Thanks Navya :) Claire Y.

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karthik’s Answer

Earn a bachelor's degree in IT, computer science, math, physics, or another related field;
Earn a master's degree in data or related field;
Gain experience in the field you intend to work in (ex: healthcare, physics, business).
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Hang’s Answer

Hi, there're some of my recommendations:
-Learn SQL, NoSQL, Pytorch, and some Python's Libraries and ETL tools
-Get to know Machine Learning (Machine Learning by Andrew Ng on Coursera is such a good fit)
-Learning by doing with some datasets from Kaggle.com
-Advance your Python with Hackerrank or LeetCode

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karthik’s Answer

To become a data scientist, you could earn a Bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%).
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