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After learning Python, what I have to study to become a data scientist?
Do I have to study deep learning, then machine learning, and the data science or what?
Asked by Arikatla Gopi venkata sudheer Last Modified
Answer Python is the most common coding language and is widely used. It is a favourite tool along being a flexible and open-sourced language. Its massive libraries like- Numpy, Pandas, SciPy, Keras, Pytorch, Tensorflow, Scikit-Learn and e.t.c which are used for data manipulation and are very easy to learn for a beginner or for professionals who want to know.
Data Science is the broad field in which you can learn about many concepts like- Python and R programming, Statistics, Probability and other topics of Mathematics, Machine Learning Algorithms, Techniques, Data Visualization tools- Big Data, SQL, NoSQL, Apache-Spark, MongoDB, PowerBI and e.t.c.
To learn data science and their other concepts, I would suggest you CorpWin technologies Programs. Corwin is one of the best training platforms to learn data science in depth. They provide the best course curriculum in which you will get all essential concepts of data science like- Python and its massive libraries concepts, Mathematics concepts, Techniques of ML and e.t.c.
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Rithvik Madugula
RESEARCH SCHOLAR WITH 3 years Teaching Experience in CSE department
The correct path for shifting towards data science will the following from my experience:
1. Learn Python in-depth with lib Numpy, Pandas, Seaborn, Pytorch and many more to explore.
2. There is a difference between Data scientist and Machine learning.
3. How a data science project works?
- You collect a set of data or a data set is provided to you
- You study, clean , and order data as required with the method Exploratory data analyis.
- You then select the best suitable ML Algo ( Regression, Clusturing, etc)
- You feed the given data to your model and expect an output as predction
4. Deep learning and NLP are often used to slove complex problems required.
: Θ), Linear
Pre-requisite: Probability, Stats, Derivatives, Algorithms,Algebra(Matrices), Graph Theory.
Note:- Data science is a very diverse field, you have to choose the domain and work hard for that.
All the best!
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Aheli Ghosh
You have to study all the machine learning algorithms, and practice projects to get a command over numpy, pandas, scikit-learn etc. You can then start with OpenCV to learn about computer vision. After that, you can start with deep learning techniques and NLP. Deep learning includes CNN, RNN, auto-encoders, GANs, deep-GANs etc. You must continuously practise on Kaggle or do your projects for better understanding of the concepts. Then you can further expand your skill-set by learning extra tools like seaborn, Tableau etc. Also, be familiar with Pytorch.
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