Data Science & Analytics Intern

Full-time Unpaid Collaboration Internship

01

Available Positions

Fresher's having BE (IT/CS/CE), BSc.(IT/CS,CE), BCA, MCA, MBA (IT/CS/CE)

Experience

Bachelor's degree in Computer Science, AI/ML, Data Science, or related field (final year students or recent graduates)

Qualification

AI R&D

Department

Role Overview

Join our data science team to explore cutting-edge analytics technologies, develop predictive models, and drive data-driven decision making. This collaborative learning opportunity focuses on end-to-end data science workflows, advanced statistical analysis, and real-world AI applications in business contexts.

Key Responsibilities

  • Conduct comprehensive data collection, cleaning, preprocessing, and exploratory data analysis using Python, R, and SQL frameworks
  • Develop and implement machine learning models for classification, regression, clustering, and time-series forecasting applications
  • Create advanced data visualizations and interactive dashboards using Tableau, Power BI, matplotlib, and seaborn libraries
  • Perform statistical analysis, hypothesis testing, and A/B testing to derive actionable business insights
  • Build and deploy predictive analytics models to support business strategy and operational optimization
  • Collaborate with cross-functional teams to translate business requirements into analytical solutions and recommendations
  • Document analytical processes, create technical reports, and present findings to stakeholders through compelling data storytelling

Required Qualifications

Must-Haves

  • Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Mathematics, or related field (final year students or recent graduates)
  • Strong programming proficiency in Python with experience in NumPy, Pandas, Scikit-learn, and Jupyter Notebooks
  • Solid understanding of statistical concepts including descriptive statistics, probability distributions, and inferential statistics
  • Knowledge of SQL for database querying and data manipulation from various data sources
  • Experience with data visualization tools and techniques for creating meaningful insights from complex datasets

Nice-to-Haves

  • Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, or advanced scikit-learn applications
  • Familiarity with cloud platforms (AWS, GCP, Azure) for data processing, model deployment, and distributed computing
  • Knowledge of big data technologies including Spark, Hadoop, or NoSQL databases for large-scale data processing
  • Experience with advanced analytics techniques including deep learning, NLP, or computer vision applications
  • Previous internship experience or portfolio projects demonstrating real-world data science problem-solving capabilities

Learning Outcomes and Development

  • Gain hands-on experience with industry-standard data science methodologies and end-to-end analytics project lifecycle management
  • Master cutting-edge analytical tools, frameworks, and cloud-based platforms through mentorship from senior data scientists
  • Develop expertise in translating complex business problems into data-driven solutions with measurable impact
  • Build comprehensive understanding of AI ethics, bias evaluation, and responsible analytics practices in enterprise environments
  • Access to real-world datasets and high-visibility projects with potential for significant business impact and career advancement
  • Enhanced presentation and stakeholder communication skills through regular project demos and cross-functional collaboration

This unpaid internship represents a collaborative knowledge-sharing opportunity in the high-growth field of data science and analytics. Outstanding performers will be considered for full-time positions based on analytical excellence, innovative problem-solving, and demonstrated ability to drive business value through data insights.

Duration: 6 months
Hours: 09:00 AM to 06:00 PM (Monday to Friday)
Location: Ganesh Glory 11, Jagatpur, Ahmedabad, 382470 (Offline Internship)

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