Tata Motors
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I applied via LinkedIn and was interviewed in Feb 2023. There were 2 interview rounds.
Yes, I can work under pressure.
I have experience working on tight deadlines and delivering high-quality results.
I am able to prioritize tasks and manage my time effectively.
I remain calm and focused in stressful situations.
I can adapt to changing priorities and handle multiple projects simultaneously.
In 2/3/4/5 years, I see myself as a senior data scientist leading a team, solving complex problems, and driving impactful insights.
Leading a team of data scientists
Solving complex problems using advanced analytics techniques
Driving impactful insights for business decision-making
Continuously learning and staying updated with the latest advancements in data science
Contributing to the growth and success of the organizatio
I have a strong background in data science and a passion for problem-solving, making me a valuable asset to your team.
I have a solid foundation in data science concepts and techniques.
I am proficient in programming languages such as Python and R.
I have experience working with various data analysis and visualization tools.
I am a quick learner and adapt easily to new technologies and methodologies.
I have excellent proble...
posted on 1 Sep 2024
I was interviewed in Mar 2024.
45 mins 15 min mcq data science and 30 mins 1 dsa problem
Generate all possible subsets of a given list in Python.
Use itertools.combinations to generate all possible combinations of the list elements.
Convert the combinations to lists and store them in a new list to get all subsets.
Use a SQL query to find customers who have ordered all products from all categories.
Join the Customers, Orders, and Products tables
Group by customer and count the distinct products ordered
Filter for customers who have ordered the total number of products available in each category
Feature engineering is crucial in data science as it involves selecting, transforming, and creating new features to improve model performance.
Feature engineering helps in improving model accuracy by providing relevant and meaningful input variables.
It involves techniques like one-hot encoding, scaling, normalization, and creating interaction terms.
Feature engineering can help in reducing overfitting and improving model...
GAN stands for Generative Adversarial Network, a type of neural network used for generating new data.
Consists of two neural networks - generator and discriminator
Generator creates new data samples while discriminator tries to distinguish between real and generated data
Used in image generation, text generation, and other creative applications
Find students who scored more than avg marks in both 11th and 12th grades.
Calculate the average marks for each student in 11th and 12th grades.
Compare each student's marks with the respective average marks to find those who scored higher in both grades.
Cost function is a mathematical function that measures the error between predicted values and actual values in a machine learning model.
Cost function helps in optimizing the parameters of a model to minimize the error.
Common cost functions include Mean Squared Error (MSE) and Cross Entropy Loss.
It is used in training machine learning models through techniques like gradient descent.
The goal is to find the parameters tha
Entropy is a measure of disorder or randomness in a system.
Entropy is used in information theory to quantify the amount of uncertainty involved in predicting the value of a random variable.
It is often used in machine learning to measure the impurity or disorder in a dataset.
In thermodynamics, entropy is a measure of the amount of energy in a physical system that is not available to do work.
Gini coefficient is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents.
Gini coefficient ranges from 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality.
A Gini coefficient of 0.4 is considered moderate inequality, while 0.6 or higher is considered high inequality.
It is commonly used in economics to measure income inequality with...
Using linear regression for classification can lead to inaccurate predictions and unreliable results.
Linear regression assumes a continuous output, making it unsuitable for discrete classification tasks.
It may not handle outliers well, leading to incorrect classification boundaries.
The predicted values may fall outside the 0-1 range for binary classification.
Logistic regression is a more appropriate choice for classifi
I applied via Campus Placement and was interviewed in Apr 2023. There was 1 interview round.
Data science is a field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Data science involves analyzing large amounts of data to uncover patterns, trends, and insights.
It combines statistics, machine learning, and domain knowledge to solve complex problems.
Data science is used in various industries such as healthcare, finance, marketing, and ...
I applied via LinkedIn and was interviewed in Apr 2024. There was 1 interview round.
Linear regression is used for predicting continuous values, while classification is used for predicting discrete values.
Linear regression is used when the output variable is continuous, such as predicting house prices based on features like size and location.
Classification is used when the output variable is categorical, such as predicting whether an email is spam or not based on its content.
Linear regression aims to f...
An outlier is a data point that differs significantly from other data points in a dataset.
Outliers can skew statistical analyses and machine learning models.
Examples of outliers include a person's weight being recorded as 1000 lbs, when the average weight is around 150 lbs.
Outliers can be detected using statistical methods like Z-score or IQR.
K-means algorithm is a clustering technique that partitions data into k clusters based on similarity.
Divides data points into k clusters based on centroids
Iteratively assigns data points to the nearest centroid and updates centroids
Continues until centroids no longer change significantly
Example: Grouping customers based on purchasing behavior
Classification metrics are used to evaluate the performance of a classification model.
Accuracy: measures the proportion of correctly classified instances out of total instances
Precision: measures the proportion of true positive predictions out of all positive predictions
Recall: measures the proportion of true positive predictions out of all actual positive instances
F1 Score: harmonic mean of precision and recall, balan...
I applied via Job Fair and was interviewed in May 2024. There was 1 interview round.
I have learned multiple programming languages including Python, R, SQL, and Java.
Python
R
SQL
Java
posted on 6 May 2024
PYTHON,SQL,STATS,ML,DL
I applied via Approached by Company and was interviewed in Jun 2022. There were 5 interview rounds.
posted on 13 Oct 2024
I applied via Referral and was interviewed in Apr 2024. There were 3 interview rounds.
Online assessment, coding mcq questions
I applied via Job Portal and was interviewed in Jul 2023. There was 1 interview round.
based on 1 review
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