Data Science Intern @ Webs IT Solution
placeHybrid
attach_money ₹15,000
businessHybrid
scheduleIntern
Posted 16 hours ago
Your Application Journey
Interview
Email Hiring Manager
******* @websitsolution.com
Recommended after applying
Job Details
About Webs IT Solution
Webs IT Solution provides practical training and internship programs that help students and professionals gain expertise in AI, ML, and data-driven technologies.
Role Overview
As a Data Science Intern, you’ll work on end-to-end projects involving data preprocessing, machine learning model development, and predictive analytics.
Key Responsibilities
- Perform data cleaning, feature engineering, and exploratory data analysis.
- Build and evaluate machine learning models using Scikit-learn.
- Visualize data insights using Matplotlib, Seaborn, or Power BI.
- Work on real datasets to generate predictive solutions.
- Collaborate with mentors to refine model performance.
Requirements
- Knowledge of Python, Pandas, NumPy, Scikit-learn, and Matplotlib.
- Understanding of ML algorithms, data preprocessing, and evaluation metrics.
- Familiarity with SQL and Jupyter Notebooks.
- Strong analytical thinking and curiosity for data-driven problem-solving.
Perks & Benefits
- Certificate of Internship from Webs IT Solution.
- Hands-on experience in ML and AI projects.
- Mentorship from data scientists.
- Networking and placement opportunities.
Stipend
₹7,500 – ₹15,000 (Performance-Based)
Key Skills/Competency
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Machine Learning
- Data Cleaning
- Feature Engineering
- Predictive Analytics
- Data Visualization
How to Get Hired at Webs IT Solution
🎯 Tips for Getting Hired
- Research Webs IT Solution's culture: Study their mission, values, and testimonials online.
- Customize your resume: Highlight Python and ML project experience.
- Prepare for technical questions: Review data preprocessing and algorithm fundamentals.
- Practice interview insights: Be ready to discuss real data projects.
📝 Interview Preparation Advice
Technical Preparation
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Practice Python coding challenges.
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Review Pandas and NumPy data manipulation.
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Study core ML algorithm concepts.
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Experiment with Jupyter Notebook projects.
Behavioral Questions
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Describe a team project experience.
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Explain your problem-solving steps.
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Discuss handling tight deadlines.
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Share a learning moment from feedback.
Frequently Asked Questions
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