DATA SCIENTIST
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DATA SCIENTIST
Data Scientist practice helps learners strengthen statistics, Python, SQL, machine learning, data visualization, and analytical thinking. Topic-wise MCQs and interview questions improve problem-solving skills and prepare candidates for data science interviews and career opportunities.
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Launch Practice QuestionsEligibility
Educational Qualification
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
Preferred Qualification
- Master's degree in Data Science, Artificial Intelligence, ML or Analytics is advantageous for advanced roles.
Technical Skills
- Knowledge of Python or R, SQL, statistics, ML and data visualization tools.
Mathematical Foundation
- Understanding of probability, linear algebra, calculus, and statistical analysis.
Experience
- Projects, certifications or relevant work experience strengthen job prospects.
Paper Pattern
Data Scientist Assessment Pattern
Online Computer-Based Test (CBT)
Mode
(MCQs), Coding Challenges and Case-Based Questions
Question Type
60 - 180 minutes
Duration
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Complete Syllabus Breakdown
Click on individual units below to reveal the specific topics and underlying concepts.
Data Scientist Syllabus
Mathematics & Statistics
- Probability , Descriptive & Inferential Statistics
- Linear Algebra , Calculus
- Hypothesis Testing , Regression Analysis
Programming
- Python , R (optional)
- Object-Oriented Programming
- File Handling , Exception Handling , APIs
SQL & Databases
- SQL Queries , Joins
- Subqueries , Views , Indexing
- Database Design , NoSQL Basics
Python
- NumPy , Pandas , Data Cleaning
- Data Wrangling , Exploratory Data Analysis (EDA),
- Feature Engineering
- Matplotlib , Seaborn , Plotly
Machine Learning
- Supervised Learning , Unsupervised Learning
- Decision Trees , Random Forest
- Support Vector Machines (SVM) , K-Means Clustering
Deep Learning
- Neural Networks , TensorFlow
- Keras , PyTorch , LSTM
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
Natural Language Processing (NLP)
- Text Preprocessing , Tokenization
- Sentiment Analysis , Word Embeddings
- Transformers , Large Language Models (LLMs)
Big Data Technologies
- Hadoop , Spark , Hive
- Kafka , Distributed Computing
Cloud & MLOps
- AWS , Microsoft Azure
- Google Cloud Platform (GCP) , Docker
- Kubernetes , MLflow , CI/CD for ML
Artificial Intelligence
- Search Algorithms , Knowledge Representation
- Reinforcement Learning , AI Ethics
Business & Domain Knowledge
- Business Analytics , KPI Analysis
- A/B Testing , Data Storytelling
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SHORT QUESTIONS
How deep learning differs from machine learning?
With machine learning, to perform any task, we need to design the right set of features and feed those features to the machine learning model. Feature engineering is a vital task for the success of any machine learning model. But it is hard to engineer the right set of features when dealing with unstructured data like text and images. In those cases, we can use deep learning.
With deep learning, we are not required to engineer the features since the deep neural network consists of several numbers of hidden layers. It implicitly learns and extracts the right set of features by itself. So, we don’t have to perform feature engineering by ourselves. Thus deep learning is widely used in the task where it is hard to perform feature engineering such as image recognition, text classification, and so on. Thus, in this way, deep learning differs from machine learning.
With deep learning, we are not required to engineer the features since the deep neural network consists of several numbers of hidden layers. It implicitly learns and extracts the right set of features by itself. So, we don’t have to perform feature engineering by ourselves. Thus deep learning is widely used in the task where it is hard to perform feature engineering such as image recognition, text classification, and so on. Thus, in this way, deep learning differs from machine learning.
What is a deep neural network?
The artificial neural network consists of one input, N number of hidden, and one output layer. When the artificial neural network consists of a large number of hidden layers then it is often called the deep neural network.
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