Career in
Data Science
People call u
Data Scientist
How Kamal started his dream in Andriod:
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How to learn Data Science development?
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I have seen most of us asking how to learn Data Science and become a complete data scientist. Here, I have tried to cover most of the important things in the data science that I have learned the hard way. Learning data science is a step by step process, so must learn it step by step. So this guide will also show how to learn data science step by step. No one can become a complete data scientist in 30 days, so it’s a journey. This guide
will also show some best data science tutorials.
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Data Science with Python :
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To download and install Anaconda :
Anaconda : Anaconda is a free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment.
Download Anaconda : https://www.anaconda.com/distribution/
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Learn the basics of python and practice problems in python.
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understand the basics of data structures and algorithms.
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Learn the scientific libraries .
Numpy , Scipy , Pandas are the scientific libraries.These libraries can be imported to python using anaconda.
Numpy : NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
Scipy : SciPy is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT(Fast Fourier Transform), signal and image processing, ODE (ordinary differential equations ) solvers and other tasks common in science and engineering.
Pandas : In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free licensed software.
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Machine Learning with Scikit.
Scikit learn : Scikit-learn is a free software machine learning library for the Python programming language.It features various classification, regression and clustering algorithms including support vectormachines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
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practising machine learning skills.
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practise advanced libraries :
Pytorch , TensorFlow , Dlib. These libraries can be imported to python using anaconda.
Pytorch : It is a machine learning library based on the Torch library, used for applications such as deep learning and natural language processing. It is free and open-source software .
TensorFlow : It is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.
Dlib : Dlib is a general purpose cross-platform software library written in the programming language C++. Its design is heavily influenced by ideas from design by contract and component-based software engineering. Thus it is, first and foremost, a set of independent software components.
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latest updates, official information regarding Data Scientist
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What they are
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Responsible to identify valuable data sources and automate collection processes.
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Responsible to undertake preprocessing of structured and unstructured data.
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Analyze large amounts of information to discover trends and patterns.
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Build predictive models and machine-learning algorithms.
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Combine models through ensemble modeling.
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Present information using data visualization techniques.
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Propose solutions and strategies to business challenges.
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Collaborate with engineering and product development teams.
What they need
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Proven experience as a Data Scientist or Data Analyst.
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Experience in data mining.
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Understanding of machine-learning and operations research.
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Knowledge of R, SQL and Python; familiarity with Scala, Java or C++ is an asset.
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Experience using business intelligence tools (e.g. Tableau) and data frameworks (e.g. Hadoop).
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Analytical mind and business acumen.
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Strong math skills (e.g. statistics, algebra).
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Problem-solving aptitude.
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Excellent communication and presentation skills.
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BSc/BA in Computer Science, Engineering or relevant field; graduate degree in Data Science or other quantitative field is preferred.