We Did It #Chandrayan3 🥳🇮🇳

From Sales Executive @ Byju's to Data Analyst @ Mercer, Know about Sanjit and his motivational career transition journey in our latest podcast episode of "Career Ki Baat With CloudyML" on YouTube. His journey will inspire you as well.

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Company: Walmart Global Tech India
Role: Data Scientist IN3
Exp: 1+ Years
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Company: KONE
Role: Data Scientist
Exp: 0-2 Years
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Company: Kyndryl
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🚨JOB OPENING UPDATE🚨

Company: MiQ
Role: Data Scientist
Exp: 1+ Years
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Company: Infosys
Role: Data Scientist
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Company: Siemens Healthineers
Role: Data Scientist
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Company: Jash Data Sciences
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Company: GSK
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Company: DotPe
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🚨JOB OPENING UPDATE🚨

Company: Virtusa
Role: Data Analyst
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Company: Peroptyx
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Company: TalentMaze Recruitment Company
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Company: Genpact
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Company: Kavya Staffing Solutions
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Company: Thomson Reuters
Role: Data Scientist
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🚨JOB OPENING UPDATE🚨

Company: Schneider Electric
Role: Data Analyst
Exp: 1+ Years
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Company: TekWissen
Role: Data Scientist (Data Analyst)
Exp: 0-1 Years
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Company: INSIDEA
Role: Data Analyst Trainee
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Company: Meesho
Role: Data Scientist I
Exp: 1-2 years
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Company: Virtusa
Role: Data Analyst
Exp: 0-3 Years
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Company: Peroptyx
Role: Data Analyst {Part Time}
Exp: Fresher
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Company: Genpact
Role: Business Analyst
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Company: AVEVA
Role: Data Quality Analyst
Exp: 0-2 Years
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All the best💙

🚨JOB OPENING UPDATE🚨

Company: Accenture in India
Role: Data Analysis
Exp: 1+ Years
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Company: The University of British Columbia
Role: Data Analyst
Exp: 0-1 Years
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Company: PW Skills
Role: Data Analyst Mentor (Teaching)
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Company: Intervie Tech
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Company: Unified Mentor
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Company: Virtusa
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Company: Vaishnavi Services
Role: Machine Learning Engineer
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All the best💙

🚨JOB OPENING UPDATE🚨

Company: Gxo Logistics
Role: Data Analyst
Exp: 3+ Years
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Company: Genpact
Role: Data Analyst
Exp: 0-1 Years
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Company: Chegg India
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Company: Rockwell Automation
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Company: Big Cash
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Company: Bohni Tech Pvt Ltd
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Exp:
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All the best💙

🚨JOB OPENING UPDATE🚨

1) Company: Genpact
Role: Senior Associate - Analyst - Consumer Insights-CPG024553
Exp: 0-1 years
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2) Company: Kenvue · Bengaluru, Karnataka, India
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9) Company-
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🚨JOB OPENING UPDATE🚨

1) Company:Alp Consulting Ltd
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2) Company:KPMG India
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3)Company:Schneider Electric
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1) Company- Globant
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2) Company- United Airlines
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Date: 14-10-2023
Company name: Elica
Role: Data Scientist
Topic: kmeans, KNN, LSTM, powerbi, SQL

1. Explain some cases where k-Means clustering fails to give good results

k-means has trouble clustering data where clusters are of various sizes and densities.Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.

2. If your Time-Series Dataset is very long, what architecture would you use?

If the dataset for time-series is very long, LSTMs are ideal for it because it can not only process single data points, but also entire sequences of data. A time-series being a sequence of data makes LSTM ideal for it.For an even stronger representational capacity, making the LSTM's multi-layered is better.Another method for long time-series dataset is to use CNNs to extract information.

3. How would you define Power BI as an effective solution ?

Power BI is a strong business analytical tool that creates useful insights and reports by collating data from unrelated sources. This data can be extracted from any source like Microsoft Excel or hybrid data warehouses. Power BI drives an extreme level of utility and purpose using interactive graphical interface and visualizations.

4. Why is the KNN Algorithm known as Lazy Learner?

When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.

5. Explain the difference between drop and truncate.

In SQL, the DROP command is used to remove the whole database or table indexes, data, and more. Whereas the TRUNCATE command is used to remove all the rows from the table.

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Date - 15-10-2023
Company name: Capgemini
Role: Data Scientist
Topic: decorators, acid, one hot encoding, kpi

1. What are decorators in Python?

Ans: Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.

2. What is the ACID property in a database?

The full form of ACID is atomicity, consistency, isolation, and durability.
• Atomicity refers that if any aspect of a transaction fails, the whole transaction fails and the database state remains unchanged.
• Consistency means that the data meets all validity guidelines.
• Concurrency management is the primary objective of isolation.
• Durability ensures that once a transaction is committed, it will occur regardless of what happens in between such as a power outage, fire, or some other kind of disturbance.

3. What is the meaning of KPI in statistics?

KPI is an acronym for a key performance indicator. It can be defined as a quantifiable measure to understand whether the goal is being achieved or not. KPI is a reliable metric to measure the performance level of an organization or individual with respect to the objectives. An example of KPI in an organization is the expense ratio.

4. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?

One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.

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🚨JOB OPENING UPDATE🚨

1) Company:Vinfinity Immigration
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Date: 16-10-2023
Company name: Wipro
Role: Data Analyst
Topic: concurrency, checkpoint, groups, nested if

1. What is concurrency control in DBMS?
This is a process of managing simultaneous operations in a database so that database integrity is not compromised. The following are the two approaches involved in concurrency control:
Optimistic approach – Involves versioning
Pessimistic approach – Involves locking

2. What is a checkpoint in DBMS and when does it occur?
A checkpoint is a mechanism where all the previous logs are removed from the system and are permanently stored on the storage disk. So, basically, checkpoints are those points from where the transaction log record can be used to recover all the committed data up to the point of crash.

3. What are groups in Tableau?
A group is a combination of dimension members that make higher level categories. For example, if you are working with a view that shows average test scores by major, you may want to group certain majors together to create major categories.

4. How are nested IF statements used in Excel?
The function IF() can be nested when we have multiple conditions to meet. The FALSE value in the first IF function is replaced by another IF function to make a further test.

————————————————————-

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🚨JOB OPENING UPDATE🚨

1) Company- GoodSpace
Role- Data Analyst
Experience- 3years
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2) Company- Crossover
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