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.
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 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.Candidates who are looking for online Data scientist Interview Questions can check out this section. We have everything for the online preparation of the Data scientist Interview. Our online learning program is designed for Data scientist technical interviews.Technical Skills required in Data science Online Course:
General skills required for Data scientist Interview Preparation are given below-
Mathematical Analysis and Linear Algebra
Statistics and Probability
Data extraction and preparation
Data Presentation and visualization
Data Science Course Duration:
It depends on which course we are picking from the available. Data science online courses. We can choose a Data science program that covers some topics and get done with the course in a month or we can choose the online Data Science online classes which are taught by esteemed names in the industry. A master’s certification in Data Science or a diploma in Data Science. A PG Diploma can be completed within a course span of 6 months and a master’s certification requires 3–4 months for completion. Basic Data Science Interview Questions include an intermix of business analytics and visualization and the application of advanced analytics models for deep learning and artificial intelligence.
Following Topics may be required for the preparation of Data Scientist Technical Interview-
Python for Data Science
Machine Learning using Python
Machine Learning Algorithms & Applications
Basic Data Scientist Coding Interview Preparation
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data scientist interview questions