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How To Start Working On Your First Data Science Project?

To secure a job in Data Science, having skills in Statistics, Visualization, Machine Learning, Programming, etc won’t be enough. You need to have a strong project portfolio that showcases your interest in Data Science. Your Data Science project portfolio will only be effective if you add a number of projects that are innovate & challenging.  Building a good portfolio that summarizes all the projects which you have worked on & by showcasing the skills that you have developed from them can do wonders for your job prospects.

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To begin with the process of building your Data Science project portfolio, you should fist understand that areas that interest you. Once you have got a clear idea, then you can analyze the following prospects.

  • Business Understanding

​You should be developing a clear idea about the business needs that are trying to address with your project. Then you can clearly define the objectives of your project. This can be illustrated with a simple example. Let’s us suppose that you are working for a product manufacturing company, that needs to retain its customers. So, in this case you can work on developing a model that helps in predicting churn?

  • Data Understanding

The ideal resource for any Data Science project would be data of good quality. So, you need to have a clear idea about the data on which you are going to work in your project. Data understanding simply means that knowing data is available to you, where it is stored? How can it be retrieved & processed without posing any issues.

  • Data Preparation & Data Modeling

In this stage you need to perform Exploratory Data Analysis & prepare your data for data modeling by make it free form any form of anomalies. Once the data is prepared you need to build your model and started training the data set. Choose the relevant algorithm to train the model.

  • Evaluation & Deployment

The model performance is then evaluated against a known data set & if the results are satisfactory then the model can be deployed in the real-time environment.

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