Differentiate Between Data Analytics and Data Science
Data Analytics |
Data Science |
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Data Analytics use data to draw meaningful insights and solves problems. |
Data Science is used in asking questions, writing algorithms, coding and building statistical models. |
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Data analytics tools include data mining, data modelling, database management and data analysis. |
Machine Learning, Hadoop, Java, Python, software development etc., are the tools of Data Science. |
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Use the existing information to uncover the actionable data. |
As a result, data Science discovers new Questions to drive innovation. |
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Check data from the given information using a specialised system and software. |
This field uses scientific methods and algorithms to extract knowledge from unstructured data. |
Ola Cabs Interview Rounds and Process
What is variance in Data Science?
Variance is the value which depicts the individual figures in a set of data which distributes themselves about the mean and describes the difference of each value from the mean value. Data Scientists use variance to understand the distribution of a data set.
6 How do you work towards a random forest?
The underlying principle of this technique is that several weak learners combine to provide a strong learner. The steps involved are:
This exhaustive list is sure to strengthen your preparation for data science interview questions.
For the given points, how will you calculate the Euclidean distance in Python?
The Euclidean distance can be calculated as follows:
euclidean_distance = sqrt( (plot1[0]-plot2[0])**2 + (plot1[1]-plot2[1])**2 )
Check out the Simplilearns video on “Data Science Interview Question” curated by industry experts to help you prepare for an interview.
4 How can we select an appropriate value of k in k-means?
Selecting the correct value of k is an important aspect of k-means clustering. We can make use of the elbow method to pick the appropriate k value. To do this, we run the k-means algorithm on a range of values, e.g., 1 to 15. For each value of k, we compute an average score. This score is also called inertia or the inter-cluster variance.
This is calculated as the sum of squares of the distances of all values in a cluster. As k starts from a low value and goes up to a high value, we start seeing a sharp decrease in the inertia value. After a certain value of k, in the range, the drop in the inertia value becomes quite small. This is the value of k that we need to choose for the k-means clustering algorithm.