Wheels Eye is hiring Data Science Interns

Duration: 6 months

For 2023/2024 Grads

https://www.linkedin.com/posts/pulkit-bansal-29602025_google-forms-sign-in-activity-7142428855985745921-sPEs?utm_source=share&utm_medium=member_android

📣 Hiring Opportunity (Immediate)

💼 Data Analyst Intern
🤠 Data Engineer Job
📚 Frontend Developer Intern
💼 Backend Developer (Node.js)
💸 Competitive Salary
📍 Job Location - #workfromhome

✅ Apply now and begin your best career journey at SUESYS

✅Intreated share resume --> careers@suesys.com

Date: 19-12-2023
Company name: IKEA
Role: Data Analyst
Topic: Statistics, SQL

1. How can we deal with problems that arise when the data flows in from a variety of sources?

There are many ways to go about dealing with multi-source problems. However, these are done primarily to solve the problems of:

Identifying the presence of similar/same records and merging them into a single recordRe-structuring the schema to ensure there is good schema integration



2.  Where is Time Series Analysis used?

Since time series analysis (TSA) has a wide scope of usage, it can be used in multiple domains. Here are some of the places where TSA plays an important role:

Statistics
Signal processing
Econometrics
Weather forecasting
Earthquake prediction
Astronomy
Applied science


3. What are the ideal situations in which t-test or z-test can be used?

It is a standard practice that a t-test is used when there is a sample size less than 30 and the z-test is considered when the sample size exceeds 30 in most cases.


4. What is the usage of the NVL() function?

The NVL() function is used to convert the NULL value to the other value. The function returns the value of the second parameter if the first parameter is NULL. If the first parameter is anything other than NULL, it is left unchanged. This function is used in Oracle, not in SQL and MySQL. Instead of NVL() function, MySQL have IFNULL() and SQL Server have ISNULL() function.


5. What is the difference between DROP and TRUNCATE commands?

If a table is dropped, all things associated with that table are dropped as well. This includes the relationships defined on the table with other tables, access privileges, and grants that the table has, as well as the integrity checks and constraints. 

However, if a table is truncated, there are no such problems as mentioned above. The table retains its original structure and the data is dropped.

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GroupM is hiring!!

*GroupM Nexus | Data Tech Interns

Requirement: Engineering background preferred, Knowledge of SQL, Python, Java or any other similar languages, preferably who can join us by the First week of January.
Location: Gurgaon

Interested candidates can share their resume at prerna.bhatnagar@groupm.com

Walk-In Interview Dot Net  Gurugram || 23rd Dec 2023

Hiring Dot Net Developer with 0 Experience
https://www.linkedin.com/jobs/view/3787333550

Headout is hiring for Software Engineer role (1-2 years of experience) and Software Engineer in Test (0-3 years of experience)

Link to apply:
SDE: https://boards.greenhouse.io/headoutreferrals/jobs/4285760006?gh_src=d9f761f96us

SDET: https://boards.greenhouse.io/headoutcareers/jobs/4025089006?gh_jid=4025089006&gh_src=a1deb4976us

Date: 21-12-2023
Company name: Biocon
Role: ML Engineer
Topic: nodes, adaboost, ensemble, hierarchical

1. List down the different types of nodes in Decision Trees.

The Decision Tree consists of the following different types of nodes:
1. Root node: It is the top-most node of the Tree from where the Tree starts.
2. Decision nodes: One or more Decision nodes that result in the splitting of data into multiple data segments and our main goal is to have the children nodes with maximum homogeneity or purity.
3. Leaf nodes: These nodes represent the data section having the highest homogeneity.

2. What is the Alpha Term in AdaBoost? How does it work?

In AdaBoost, multiple weak learners are trained to get the strong learner. As a result, hence calculating the error term of every weak learner is essential to know which weak learner is performing best and which is not.
The term Alpha is a parameter that indicates the weight that should be given to a particular weak learner algorithm. If the value of the term Alpha for a particular algorithm is high, that indicates that the model is performing best and the error rate for the same is low.

3. Since Ensemble Learning provides better output most of the time, why do you not use it all the time? 

Although it provides a better outcome many times, it is not true that it will always perform better. There are several ensemble methods, each with its own advantages/disadvantages, and choosing one to use depends on the problem at hand. If there are models with high variance, then it will benefit from bagging. If the model is biased, it is better to use boosting. If the work is in probabilistic setting, the ensemble methods may not work because it is known that boosting delivers poor probability estimates.


4. What are the various types of Hierarchical Clustering?

The two different types of Hierarchical Clustering technique are as follows:
Agglomerative: It is a bottom-up approach, in which the algorithm starts with taking all data points as single clusters and merging them until one cluster is left.
Divisive: It is just the opposite of the agglomerative algorithm as it is a top-down approach.

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