I’m a Math teacher and I just wanna try to study programming to be able to apply as Data Scientist. Can you please give me more specific idea based on your experience as a Data Scientist? Where and how should I start when entering this new field or career?
What program do I need to focus on as a beginner?
I’m willing to accept any kind of help, advice, or guidance as a beginner. Thank you.
Hello. Happy to hear your motivation.
The Data Scientist is a complex path, and there are many fields like, mathematics, statistics, CC, programming and finance for give some examples.
Having said that. Assuming that you have some Quantitative background. You can begin by learning Python or R. As a programing language (however,in the middle term,I encourage you to learn both.) Regarding the theoretical knowledge, you will need an introduction to Machine Learning. For a fun introduction, I recommend Intro to ML lectures from Caltech, in YouTube, old but gold. Then you can move to many other material.
By the moment, get focused in learning python (pandas, numpy, matplotlib, scipy, seaborn) and then move into Scikit-learn and Tensorflow (at least.)
When you have the coding ability and the theoretical knowledge. You can implement some model, while at the same time, enhance yourself with more tools, like some Knowledge of cloud(AWS, Azure or Google cloud) and of Github.
Finally, focus your sector, eg. Medicine, Finance, NLP and so on.
Hope it helps.
Note, data science is not the same thing as machine learning.
I would start by learning how do statistics on data sets with Python. To do that, I would start by learning the basics of Python.
Thanks for the replies. My uncle told me something that it was usually related to AI. Do I need to attend extra classes to be informed in that kind of field or should I focus more on statistical research and analyzing different set of data with Python?
It depends of your level of proficiency in statistics and your aspiration.
For example, if you learn the basics of python(numpy, pandas, matplotlib, seaborn). And know Inferential Statistics. You can do Data Analysis. (not data science.)
If you want to do Data Science, you will need more statistical theory, and Linear Algebra, and Calculus. In addition to more knowledge of python (scikit learn and statsmodels). And even if ML is not Data Science, there are algorithms of ML that you must know, Logistic Regression, SVM, KNN, Decision Trees, Random Forest, and so on. Also, in the practice, if you don’t even know how to fit a model in scikit-learn, you cannot do too much. You can learn the coding, sure, but you must know what are you doing. When I must do Bagging, When Boosting, Does the assumption of Normal Distribution apply to this model?.. And so on… By this reason I recommend you to learn the at least the basics of ML if you want to enter into the Data Scientist path.
If you are undecided, you can give a try to the Kaggle platform and see if it appeals to you.
Hope it helps.
I see. I must do more research about the field that I’m planning to enter. Based on the info that you had given, it will require me a lot of effort to learn about the program needed for that.
At first, I thought that data science is somehow similar with data analysis. I don’t see any difference at all until I read this one. I really appreciate it.
You still can learn python and statistics while you decide the path. There are many jobs that only require Data Analysis.
Consider the freecodecamp course on data analysis.
In my case, all started with it:
But you can also watch:
If in the future you feel inspired to give one step more, then you can learn the data science content with a good background and you will already know the basics of python!
Best of wishes
Logistic Regression is a general statistical technique, not a ML specific technique.
No need to dive in the deep end to start and learn artificial intelligence or machine learning. Data science is, roughly speaking, stats + programming. Start simple, learn programming basics and refresh your stats, and explore what interests you as you learn more.
I’m grateful for your concerns and suggestions. I’ll consider your words as guidelines for this thing. Thank you.
Sounds great…many thanks for your advice.
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