GE vernova - Internship

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  Job Description E ssential   Responsibilities: Interns will use their skills of programming, innovation and engineering and are encouraged to implement new ideas, develop tools and applications or improvise existing methods, tools , processes and features in the projects. The role requires the interns to get in contact with stakeholders, customers and colleagues in their day-to-day job which would require interpersonal, soft and presentation skills. Responsible to complete Assignments & Projects over the course of their project duration. It is an intern led, intern run project which can be seen as a self-contained project aimed at improving and refining aspects of their skill set. Qualifications:  B.Tech, M.Tech/M.E Final year student from Computer Science /Power Systems /Instrumentation /  Electronics/Communications  and other related  streams from reputed institutes (IIT/NIT/BITS/Other reputed institutes) Demonstrated academic excellence - cons...
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Data Science Roadmap: A Step-by-Step Guide to Success (Month-wise Plan)

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Are you looking to become a Data Scientist but not sure where to start or how to plan your journey? This 11-month roadmap is your structured path from beginner to job-ready! Whether you're a student, working professional, or career switcher, follow these steps to build a strong foundation and land your dream job in data science.

🚀 Month 1: Basic Python

Start by learning the fundamentals of Python — the most widely used language in data science.

  • Variables, data types, and operators

  • Control structures: if-else, loops

  • Functions and modules

  • Data structures: lists, dictionaries, tuples, sets

  • Practice on platforms like HackerRank, LeetCode, and Kaggle

Goal: Be comfortable writing basic scripts and solving logical problems.

📊 Month 2: Statistics & Probability

A strong grip on stats is essential to understand data and build models.

  • Descriptive statistics (mean, median, variance)

  • Probability theory (Bayes Theorem, conditional probability)

  • Distributions (normal, binomial, Poisson)

  • Hypothesis testing, p-values, confidence intervals

Goal: Understand data behavior and draw valid conclusions.

🐍 Month 3: Advanced Python

Time to dive deeper and write more efficient and modular code.

  • Object-Oriented Programming (OOP)

  • File handling, exceptions

  • Lambda, map, filter, reduce

  • Regular expressions

  • Libraries: NumPy, Pandas

Goal: Build scripts and data manipulation pipelines using advanced features.

📈 Month 4: Data Visualization

Data isn’t valuable unless it's understood. Visualization is key.

  • Matplotlib & Seaborn for charts

  • Plotly for interactive dashboards

  • Understanding data patterns

  • Creating reports and dashboards

Goal: Learn to communicate insights visually.

🤖 Month 5: Machine Learning

The core of Data Science — building models and making predictions.

  • Supervised & Unsupervised learning

  • Algorithms: Linear regression, decision trees, KNN, clustering

  • Scikit-learn for model training

  • Model evaluation metrics (accuracy, precision, recall)

Goal: Train basic models and evaluate their performance.

🔄 Month 6: Data Manipulation

Handling and preparing data is 80% of the job.

  • Pandas for dataframes

  • Handling missing values, outliers

  • Feature engineering

  • Data pipelines and preprocessing

Goal: Clean and prepare datasets for modeling.

🚀 Month 7: Deployment

Now that your models work — how do you make them usable?

  • Flask or FastAPI for API creation

  • Docker basics

  • Model serialization (pickle, joblib)

  • Cloud deployment (Heroku, AWS, Azure basics)

Goal: Deploy ML models to the web/app environment.

🧠 Month 8: Deep Learning

Dive into neural networks and more advanced AI.

  • Basics of Neural Networks

  • Keras & TensorFlow/PyTorch

  • CNNs for image data

  • RNNs for sequences/time series

Goal: Train deep learning models and understand backpropagation.

💬 Month 9: NLP / Computer Vision

Work with unstructured data: text and images.

  • Natural Language Processing: tokenization, stemming, TF-IDF

  • Sentiment analysis, topic modeling

  • Introduction to transformers (BERT)

  • Image classification with CNNs

Goal: Build basic NLP and vision-based projects.

🎯 Month 10: Interview Preparation

Get job-ready by refining your skills and boosting confidence.

  • Practice coding (DSA)

  • System design for ML

  • Real-world case studies

  • Mock interviews and resume reviews

Goal: Be fully prepared for technical interviews.

💼 Month 11: Projects & Resume Prep

Now it’s time to showcase your knowledge.

  • Create 2–3 end-to-end projects:

    • Predictive modeling

    • Image classification

    • NLP-based chatbot or analysis

  • Host code on GitHub

  • Create a portfolio website

  • Write blog posts on Medium/Kaggle

Goal: Build a portfolio that attracts recruiters and showcases your expertise.

🏆 Month 12: SUCCESS!

By now, you’re equipped with all necessary skills. Continue building, contributing to open source, networking on LinkedIn, and applying for jobs!

Final Tips:

✅ Stay consistent
✅ Join a learning community
✅ Keep practicing on real-world datasets
✅ Read research papers and industry blogs
✅ Network with professionals in the field

💡 Follow @Software Stack for daily data science tips, memes, and tutorials.
🎯 Ready to start your journey? Save this roadmap and crush your Data Science goals!

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