Data Science Jobs

GE Aerospace hiring Data Scientist Apply link: https://careers.geaerospace.com/global/en/job/GAOGAYGLOBALR5011728EXTERNALENGLOBAL/Data-Scientist?utm_source=linkedin&utm_medium=phenom-feeds

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Data Science Learning Plan Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra) Step 2: Python for Data Science (Basics and Libraries) Step 3: Data Manipulation and Analysis (Pandas, NumPy) Step 4: Data Visualization (Matplotlib, Seaborn, Plotly) Step 5: Databases and SQL for Data Retrieval Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning) Step 7: Data Cleaning and Preprocessing Step 8: Feature Engineering and Selection Step 9: Model Evaluation and Tuning Step 10: Deep Learning (Neural Networks, TensorFlow, Keras) Step 11: Working with Big Data (Hadoop, Spark) Step 12: Building Data Science Projects and Portfolio Data Science Resources 👇👇 https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y Like for more 😄

𝟳 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗱 𝗢𝘂𝘁😍 🚀 Want to Make Your Resume Stand Out in 2025?✨️ If you’re aiming to boost your chances in job interviews or want to upgrade your resume with powerful, in-demand skills — start with these 7 free online courses👨‍💻📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3SJ91OV Empower yourself and take your career to the next level! ✅

Step-by-Step Roadmap to Learn Data Science in 2025: Step 1: Understand the Role A data scientist in 2025 is expected to: Analyze data to extract insights Build predictive models using ML Communicate findings to stakeholders Work with large datasets in cloud environments Step 2: Master the Prerequisite Skills A. Programming Learn Python (must-have): Focus on pandas, numpy, matplotlib, seaborn, scikit-learn R (optional but helpful for statistical analysis) SQL: Strong command over data extraction and transformation B. Math & Stats Probability, Descriptive & Inferential Statistics Linear Algebra & Calculus (only what's necessary for ML) Hypothesis testing Step 3: Learn Data Handling Data Cleaning, Preprocessing Exploratory Data Analysis (EDA) Feature Engineering Tools: Python (pandas), Excel, SQL Step 4: Master Machine Learning Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forests, XGBoost Unsupervised Learning: K-Means, Hierarchical Clustering, PCA Deep Learning (optional): Use TensorFlow or PyTorch Evaluation Metrics: Accuracy, AUC, Confusion Matrix, RMSE Step 5: Learn Data Visualization & Storytelling Python (matplotlib, seaborn, plotly) Power BI / Tableau Communicating insights clearly is as important as modeling Step 6: Use Real Datasets & Projects Work on projects using Kaggle, UCI, or public APIs Examples: Customer churn prediction Sales forecasting Sentiment analysis Fraud detection Step 7: Understand Cloud & MLOps (2025+ Skills) Cloud: AWS (S3, EC2, SageMaker), GCP, or Azure MLOps: Model deployment (Flask, FastAPI), CI/CD for ML, Docker basics Step 8: Build Portfolio & Resume Create GitHub repos with well-documented code Post projects and blogs on Medium or LinkedIn Prepare a data science-specific resume Step 9: Apply Smartly Focus on job roles like: Data Scientist, ML Engineer, Data Analyst → DS Use platforms like LinkedIn, Glassdoor, Hirect, AngelList, etc. Practice data science interviews: case studies, ML concepts, SQL + Python coding Step 10: Keep Learning & Updating Follow top newsletters: Data Elixir, Towards Data Science Read papers (arXiv, Google Scholar) on trending topics: LLMs, AutoML, Explainable AI Upskill with certifications (Google Data Cert, Coursera, DataCamp, Udemy) Free Resources to learn Data Science Kaggle Courses: https://www.kaggle.com/learn CS50 AI by Harvard: https://cs50.harvard.edu/ai/ Fast.ai: https://course.fast.ai/ Google ML Crash Course: https://developers.google.com/machine-learning/crash-course Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998 Data Science Books: https://t.me/datalemur React ❤️ for more

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LLM Project Ideas 👆

𝟲 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗖𝗵𝗮𝗻𝗴𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗜𝗻 𝟮𝟬𝟮𝟱 😍 🎯 Want to switch careers or upgrade your skills — without spending a single rupee? Check out 6 handpicked, beginner-friendly courses in high-demand fields like Data Science, Web Development, Digital Marketing, Project Management, and more. 🚀 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4e1I17a 💥 Start learning today and build the skills top companies want!✅️

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Societe Generale is hiring! Position: Business Analyst Qualifications: Bachelor’s/ Master's Degree Salary: 6.5 - 12 LPA (Expected) Experience: Freshers/ Experienced Location: Bangalore, India (Hybrid) 📌Apply Now: https://careers.societegenerale.com/en/job-offers/business-analyst-25000BM6-en 👉 WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226 👉 Telegram Channel: https://t.me/addlist/4q2PYC0pH_VjZDk5 All the best! 👍👍

EY is hiring! Position: Data and Analytics Qualifications: Bachelor's degree Salary: 6 - 13 LPA (Expected) Experience: Experienced Location: Bangalore, India 📌Apply Now: https://careers.ey.com/ey/job/Bengaluru-Data-and-Analytics-Pricing-and-Commercial-Senior-KA-560016/1200004101/ 👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J 👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5 All the best 👍👍

𝟱 𝗙𝗥𝗘𝗘 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗧𝗲𝗰𝗵, 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲😍 Dreaming of an MIT education without the tuition fees? 🎯 These 5 FREE courses from MIT will help you master the fundamentals of programming, AI, machine learning, and data science—all from the comfort of your home! 🌐✨ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/45cvR95 Your gateway to a smarter career✅️

𝗪𝗲’𝗿𝗲 𝗛𝗶𝗿𝗶𝗻𝗴: 𝗔𝗜/𝗠𝗟 𝗘𝗻𝘁𝗵𝘂𝘀𝗶𝗮𝘀𝘁𝘀! 🚀 Are you passionate about Gen-AI, LLMs, and cutting-edge ML tech? 𝗪𝗲'𝗿𝗲 𝗼𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗸𝗼𝘂𝘁 𝗳𝗼𝗿 𝗮 𝗰𝘂𝗿𝗶𝗼𝘂𝘀 𝗺𝗶𝗻𝗱 𝘄𝗶𝘁𝗵 𝟬–𝟭 𝘆𝗲𝗮𝗿𝘀 𝗼𝗳 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝗔𝗜/𝗠𝗟 (𝗣𝘆𝘁𝗵𝗼𝗻-𝗳𝗼𝗰𝘂𝘀𝗲𝗱) 𝘁𝗼 𝗷𝗼𝗶𝗻 𝗼𝘂𝗿 𝗴𝗿𝗼𝘄𝗶𝗻𝗴 𝘁𝗲𝗮𝗺 𝗶𝗻 𝗔𝗵𝗺𝗲𝗱𝗮𝗯𝗮𝗱 (𝗪𝗼𝗿𝗸 𝗳𝗿𝗼𝗺 𝗢𝗳𝗳𝗶𝗰𝗲). 🔍 What we’re looking for: ✨ Experience with Hugging Face, GPT-3.5/4, Claude, or similar tools 📚 Familiarity with NLP tasks (summarization, NER, sentiment analysis) 🧠 Bonus: Knowledge of RAG, vector DBs, prompt engineering & LLM orchestration If you're constantly exploring GitHub, reading research papers, and love building with AI—we want to hear from you! 📧 Share your resume at hr@nextgensoft.io

𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱!😍 Want to communicate with AI like a pro? 🤖 Whether you’re a data analyst, AI developer, content creator, or student, this is the must-have skill of 2025✨️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/456lMuf Save this now & unlock your AI potential!⚡

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Zelestra is hiring Junior Data Scientist 🚀 Qualification : Bachelor's degree Experience : 0-2 Years Location :; Gurugram Apply link : https://careers.solarpack.es/job/Haryana-Junior-Data-Scientist/1162773055/ 👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J 👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5 All the best 👍

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Basics of Machine Learning 👇👇 Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types: 1. Supervised Learning: The algorithm is trained on a labeled dataset, learning to map input to output. For example, it can predict housing prices based on features like size and location. 2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing. 3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications. Key concepts include: - Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training. - Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance. - Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns. - Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks. In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task. Free Resources to learn Machine Learning: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y ENJOY LEARNING 👍👍