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Data Science


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​Data Scientist

How Kamal started his dream in Andriod:

  • How to learn Data Science development?

  • 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.

  • Data Science with Python :

  • 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 :

  • Learn the basics of python and practice problems in python.


  • understand the basics of data structures and algorithms.

  • 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.

  • 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.

  • practising machine learning skills.

  • 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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What they are

  • Responsible to identify valuable data sources and automate collection processes.

  •  Responsible to undertake preprocessing of structured and unstructured data.

  • Analyze large amounts of information to discover trends and patterns.

  • Build predictive models and machine-learning algorithms.

  • Combine models through ensemble modeling.

  • Present information using data visualization techniques.

  • Propose solutions and strategies to business challenges.

  • Collaborate with engineering and product development teams.

What they need

  • Proven experience as a Data Scientist or Data Analyst.

  • Experience in data mining.

  • Understanding of machine-learning and operations research.

  • Knowledge of R, SQL and Python; familiarity with Scala, Java or C++ is an asset.

  • Experience using business intelligence tools (e.g. Tableau) and data frameworks (e.g. Hadoop).

  • Analytical mind and business acumen.

  • Strong math skills (e.g. statistics, algebra).

  • Problem-solving aptitude.

  • Excellent communication and presentation skills.

  • BSc/BA in Computer Science, Engineering or relevant field; graduate degree in Data Science or other quantitative field is preferred.

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