Find Interview Questions for Coriolis Technologies

Right Answer:
Classification analysis is a data analysis technique used to categorize data into predefined classes or groups. It works by using algorithms to learn from a training dataset, where the outcomes are known, and then applying this learned model to classify new, unseen data based on its features. Common algorithms include decision trees, logistic regression, and support vector machines.

Right Answer:
A hypothesis is a specific, testable prediction about the relationship between two or more variables. To test a hypothesis, you can use the following steps:

1. **Formulate the Hypothesis**: Clearly define the null hypothesis (no effect or relationship) and the alternative hypothesis (there is an effect or relationship).
2. **Collect Data**: Gather relevant data through experiments, surveys, or observational studies.
3. **Analyze Data**: Use statistical methods to analyze the data and determine if there is enough evidence to reject the null hypothesis.
4. **Draw Conclusions**: Based on the analysis, conclude whether the hypothesis is supported or not, and report the findings.

Right Answer:
The different types of data distributions include:

1. Normal Distribution
2. Binomial Distribution
3. Poisson Distribution
4. Uniform Distribution
5. Exponential Distribution
6. Log-Normal Distribution
7. Geometric Distribution
8. Beta Distribution
9. Chi-Squared Distribution
10. Student's t-Distribution

Right Answer:
Some common data analysis tools and software include:

1. Microsoft Excel
2. R
3. Python (with libraries like Pandas and NumPy)
4. SQL
5. Tableau
6. Power BI
7. SAS
8. SPSS
9. Google Analytics
10. Apache Spark

Right Answer:
To handle missing data in a dataset, you can use the following methods:

1. **Remove Rows/Columns**: Delete rows or columns with missing values if they are not significant.
2. **Imputation**: Fill in missing values using techniques like mean, median, mode, or more advanced methods like KNN or regression.
3. **Flagging**: Create a new column to indicate missing values for analysis.
4. **Predictive Modeling**: Use algorithms to predict and fill in missing values based on other data.
5. **Leave as Is**: In some cases, you may choose to leave missing values if they are meaningful for analysis.

Right Answer:
You can check if all characters in a string are decimal using the `str.isdecimal()` method in Python. For example:

```python
my_string = "12345"
is_decimal = my_string.isdecimal()
```

This will return `True` if all characters are decimal, otherwise `False`.

Right Answer:
Django is a high-level Python web framework that simplifies the development of web applications by providing built-in features such as an ORM (Object-Relational Mapping), an admin interface, and tools for handling user authentication, URL routing, and form processing. It follows the MVC (Model-View-Controller) architectural pattern and promotes rapid development and clean, pragmatic design.

Right Answer:
To set up static files in Django, follow these steps:

1. Add `'django.contrib.staticfiles'` to your `INSTALLED_APPS` in `settings.py`.
2. Define the `STATIC_URL` in `settings.py`, e.g., `STATIC_URL = '/static/'`.
3. Optionally, set `STATICFILES_DIRS` to specify additional directories for static files, e.g., `STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static')]`.
4. Collect static files for production using the command `python manage.py collectstatic`.
5. In your templates, use the `{% load static %}` tag and refer to static files with `{% static 'path/to/file' %}`.

Right Answer:
Django is a high-level framework.

Right Answer:
Normalization is the process of organizing a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller, related tables and defining relationships between them. For example, instead of having a single table for customers and their orders, you might have one table for customers and another for orders, linked by a customer ID.

Denormalization is the process of combining tables to improve read performance at the cost of increased redundancy. For example, you might combine the customers and orders tables into one table that includes customer details with each order, making it faster to retrieve all information in a single query, but increasing the risk of data inconsistency.