You can start learning data analysis by understanding the basics of statistical concepts, data types, and structures. Then learn a programming language like Python or R, master data manipulation and visualization, and delve into specific data analysis techniques.

SQL-Interview-Book.pdf

The amount of preparation needed for a data analysis interview can vary depending on your current knowledge and experience. It's important to have a solid understanding of key concepts in statistics, programming (e.g., Python or R), data manipulation, visualization, and potentially machine learning. Practice with real-world datasets and mock interviews can help you build confidence and proficiency. Aim to be comfortable explaining your thought process and problem-solving skills.

To be a successful business analyst, you need a combination of technical skills, analytical abilities, and interpersonal qualities. Here are some essential skills and pointers to excel in the field of business analysis:

1. Analytical Skills
2. Problem-Solving Skills
3. Domain Knowledge
4. Data Management:
5. Business Intelligence Tools:
6. Requirement Elicitation:
7. Documentation and Reporting:
8. Technical Knowledge
9. Critical Thinking
10. Interpersonal Skills
11. Project Management
12. Adaptability
13. Presentation Skills

Different Types of Data Analyst Interview Questions
👇👇

Technical Skills: These questions assess your proficiency with data analysis tools, programming languages (e.g., SQL, Python, R), and statistical methods.

Case Studies: You might be presented with real-world scenarios and asked how you would approach and solve them using data analysis.

Behavioral Questions: These questions aim to understand your problem-solving abilities, teamwork, communication skills, and how you handle challenges.

Statistical Questions: Expect questions related to descriptive and inferential statistics, hypothesis testing, regression analysis, and other quantitative techniques.

Domain Knowledge: Some interviews might delve into your understanding of the specific industry or domain the company operates in.

Machine Learning Concepts: Depending on the role, you might be asked about your understanding of machine learning algorithms and their applications.

Coding Challenges: These can assess your programming skills and your ability to translate algorithms into code.

Communication: You might need to explain technical concepts to non-technical stakeholders or present your findings effectively.

Problem-Solving: Expect questions that test your ability to approach complex problems logically and analytically.

Remember, the exact questions can vary widely based on the company and the role you're applying for. It's a good idea to review the job description and the company's background to tailor your preparation.

📚 Title: Machine Learning for Business Analytics (2023)

1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.

2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.

3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.

4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY

Here's a snippet of code written in C:

for(int i=0;i<n;i++)
{
 for(int j=0;j<n;j++)
{
//some operation
}
}

How can I write something equivalent in SQL?

Solution:Alias

say, you are asked to count the frequency of each color occurring in the same the table , so you can write something like this:


select distinct color ,(select count(*) from colors where c.color=color) from colors c;

Q.Autoencoder methods

A. Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. Various techniques exist to prevent autoencoders from learning the identity function and to improve their ability to capture important ' information and learn richer representations. 1.Sparse autoencoder (SAE) 2. Denoising autoencoder (DAE) 3. Contractive autoencoder (CAE) 4. Principal component analysis.


Q. L1 and L2 regularization?


A. L1 regularization gives output in binary weights from 0 to 1 for the model's features and is adopted for decreasing the number of features in a huge dimensional dataset. L2 regularization disperse the error terms in all the weights that leads to more accurate customized final models.


Q. How to measure the Euclidean distance betweeen the two arrays in numpy?

A. Euclidean distance is defined in mathematics as the magnitude or length of the line segment between two points. There are multiple methods for measuring the euclidean methods.

Method 1. In this method, we first initialize two numpy arrays. Then, we use linalg.norm() of numpy basically to compute the euclidean distance directly.

Method 2. In this method, we first initialize two numpy arrays. Then, we take the difference of the two arrays, compute the dot product of the result, and transpose of the result. Then we take the square root of the answer. This is another way to implement Euclidean distance.

Method 3. In this method, we first initialize two numpy arrays. Then, we compute the difference of these arrays and take their square. We take the sum of the squared elements, and after that, we take the square root in the end. This is another way to implement Euclidean distance.


Q.What are the support vectors in SVM?

A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.


Q. How do you handle categorical data?

A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.


Q. What is coerrelation?

A.Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It's a common tool for describing simple relationships without making a statement about cause and effects


Q. What is covariance?

A. Covariance is nothing but a measure of correlation. Covariance is a measure of how much two random variables vary together. It’s similar to variance, but where variance tells you how a single variable varies, co variance tells you how two variables vary together