DATA SCIENTIST
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SHORT QUESTIONS
With machine learning, to perform any task, we need to design the right set of features and feed those features to the machine learning model. Feature engineering is a vital task for the success of any machine learning model. But it is hard to engineer the right set of features when dealing with unstructured data like text and images. In those cases, we can use deep learning.
With deep learning, we are not required to engineer the features since the deep neural network consists of several numbers of hidden layers. It implicitly learns and extracts the right set of features by itself. So, we don’t have to perform feature engineering by ourselves. Thus deep learning is widely used in the task where it is hard to perform feature engineering such as image recognition, text classification, and so on. Thus, in this way, deep learning differs from machine learning.
With deep learning, we are not required to engineer the features since the deep neural network consists of several numbers of hidden layers. It implicitly learns and extracts the right set of features by itself. So, we don’t have to perform feature engineering by ourselves. Thus deep learning is widely used in the task where it is hard to perform feature engineering such as image recognition, text classification, and so on. Thus, in this way, deep learning differs from machine learning.
The artificial neural network consists of one input, N number of hidden, and one output layer. When the artificial neural network consists of a large number of hidden layers then it is often called the deep neural network.
Data Scientist Material
Data science uses algorithms and scientific methods to extract insights from many structural and unstructured data. Data science is related to deep learning, big data, and data mining. Today most of the info is unstructured or semi-structured, unlike data within the traditional systems which was mostly structured. Data science techniques and theories drawn from many fields within the context of computer science, mathematics, and statistics. Data scientists interpret and manage data to solve complex problems using expertise in data mining, deep learning, and big data fields. Five stages of the data science life cycle include Capture, Maintain, Process, Analyze, and Communicate.- Data Science Preparation material can be used by any candidate who is preparing for Data Scientist Interview
- All candidates who have to appear for the IT Officer can also refer to this short questions answers and multiple choice questions answers section.
- You can also get access to the Data Science Preparation material ebook.
- Data Science material can be used in the preparation of B.Sc (IT) , M. Sc (IT), BCA, MCA and various other exams.
- You can also download pdf for these Data Science preparation material.
- This section can also be used for the preparation of VIVA of various exams like B.Sc (computer science), M. Sc (computer science), BCA, MCA and many more.
- Data Science Preparation material can be used to gain a credit score in various undergraduate and postgraduate courses like B.com, M.Com, MBA, BBA and many more.
Data Science Preparation Material
Data Science Preparation Material for Bank Officer
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