## Differentiate Between Data Analytics and Data Science

## Data Analytics |
## Data Science |

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. |

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. |

Use the existing information to uncover the actionable data. |
As a result, data Science discovers new Questions to drive innovation. |

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.