Applied Data Finance Chennai Interview Questions

1 What are the popular libraries used in Data Science?

Below are the popular libraries used for data extraction, cleaning, visualization, and deploying DS models:

  • TensorFlow: Supports parallel computing with impeccable library management backed by Google.
  • SciPy: Mainly used for solving differential equations, multidimensional programming, data manipulation, and visualization through graphs and charts.
  • Pandas: Used to implement the ETL(Extracting, Transforming, and Loading the datasets) capabilities in business applications.
  • Matplotlib: Being free and open-source, it can be used as a replacement for MATLAB, which results in better performance and low memory consumption.
  • PyTorch: Best for projects which involve Machine Learning algorithms and Deep Neural Networks.
  • Interested to learn more about Data Science, check out our Data Science Course in New York!

    4 Explain collaborative filtering in recommender systems.

    Collaborative filtering is a technique used to build recommender systems. In this technique, to generate recommendations, we make use of data about the likes and dislikes of users similar to other users. This similarity is estimated based on several varying factors, such as age, gender, locality, etc.

    If User A, similar to User B, watched and liked a movie, then that movie will be recommended to User B, and similarly, if User B watched and liked a movie, then that would be recommended to User A.

    In other words, the content of the movie does not matter much. When recommending it to a user what matters is if other users similar to that particular user liked the content of the movie or not.

    5 Explain bagging in Data Science.

    Bagging is an ensemble learning method. It stands for bootstrap aggregating. In this technique, we generate some data using the bootstrap method, in which we use an already existing dataset and generate multiple samples of the N size. This bootstrapped data is then used to train multiple models in parallel, which makes the bagging model more robust than a simple model.

    Once all the models are trained, when it’s time to make a prediction, we make predictions using all the trained models and then average the result in the case of regression, and for classification, we choose the result, generated by models, that have the highest frequency.

    3 Why do we use the summary function?

    The summary function in R gives us the statistics of the implemented algorithm on a particular dataset. It consists of various objects, variables, data attributes, etc. It provides summary statistics for individual objects when fed into the function. We use a summary function when we want information about the values present in the dataset. It gives us the summary statistics in the following form: Summary Function

    applied data finance chennai interview questions

    How is Data Science different from traditional application programming?

    Data Science takes a fundamentally different approach in building systems that provide value than traditional application development.

    In traditional programming paradigms, we used to analyze the input, figure out the expected output, and write code, which contains rules and statements needed to transform the provided input into the expected output. As we can imagine, these rules were not easy to write, especially, for data that even computers had a hard time understanding, e.g., s, videos, etc.

    Data Science shifts this process a little bit. In it, we need access to large volumes of data that contain the necessary inputs and their mappings to the expected outputs. Then, we use Data Science algorithms, which use mathematical analysis to generate rules to map the given inputs to outputs.

    This process of rule generation is called training. After training, we use some data that was set aside before the training phase to test and check the system’s accuracy. The generated rules are a kind of a black box, and we cannot understand how the inputs are being transformed into outputs.

    However, If the accuracy is good enough, then we can use the system (also called a model).

    As described above, in traditional programming, we had to write the rules to map the input to the output, but in Data Science, the rules are automatically generated or learned from the given data. This helped solve some really difficult challenges that were being faced by several companies.


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