Data Science Jobs

𝗖𝗶𝘀𝗰𝗼 𝗙𝗥𝗘𝗘 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍  Stand out in the competitive job market.Cisco Networking Academy has you covered with free courses designed to enhance your professional skills. ✅ Learn the Most In-Demand Skills: ✅ Perfect for Everyone ✅ Earn Recognized Certificates 𝗟𝗶𝗻𝗸👇:-  https://pdlink.in/3PeiTOW Enroll for FREE & Get Certified 🎓

ZS hiring Data Scientist Apply link: https://www.linkedin.com/jobs/view/4226874462 👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J 👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5 All the best 👍👍

Check out this job at Corp Crunch: https://www.linkedin.com/jobs/view/4226409673

𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗙𝗿𝗼𝗺 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀😍 Top Companies Offering FREE Certification Courses To Upskill In 2025  Google:- https://pdlink.in/3YsujTV Microsoft :- https://pdlink.in/4jpmI0I Cisco :- https://pdlink.in/4fYr1xO HP :- https://pdlink.in/3DrNsxI IBM :- https://pdlink.in/44GsWoC Qualc :- https://pdlink.in/3YrFTyK TCS :- https://pdlink.in/4cHavCa Infosys :- https://pdlink.in/4jsHZXf Enroll For FREE & Get Certified 🎓

3 Data Science Free courses by Microsoft🔥🔥 1. AI For Beginners - https://microsoft.github.io/AI-For-Beginners/ 2. ML For Beginners - https://microsoft.github.io/ML-For-Beginners/#/ 3. Data Science For Beginners - https://github.com/microsoft/Data-Science-For-Beginners Join for more: https://t.me/udacityfreecourse

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 😍 Company Name: Khatabook  Role:- Analytics - Intern Location: Bangalore Experience: 0 to 1 Year  𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸👇:-  https://pdlink.in/43sdnQr Apply before the link expires 💫

American Express Global Business Travel hiring Data Scientist Apply link: https://travelhrportal.wd1.myworkdayjobs.com/Jobs/job/India/Associate-Data-Scientist_J-74018 👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J 👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5 All the best 👍👍

How much Statistics must I know to become a Data Scientist? This is one of the most common questions Here are the must-know Statistics concepts every Data Scientist should know: 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 ↗ Bayes' Theorem & conditional probability ↗ Permutations & combinations ↗ Card & die roll problem-solving 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 ↗ Mean, median, mode ↗ Standard deviation and variance ↗  Bernoulli's, Binomial, Normal, Uniform, Exponential distributions 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 ↗ A/B experimentation ↗ T-test, Z-test, Chi-squared tests ↗ Type 1 & 2 errors ↗ Sampling techniques & biases ↗ Confidence intervals & p-values ↗ Central Limit Theorem ↗ Causal inference techniques 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 ↗ Logistic & Linear regression ↗ Decision trees & random forests ↗ Clustering models ↗ Feature engineering ↗ Feature selection methods ↗ Model testing & validation ↗ Time series analysis I have curated the best interview resources to crack Data Science Interviews 👇👇 https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y Like if you need similar content 😄👍

𝟳 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗜𝗻 𝟮𝟬𝟮𝟱 😍 If you dream of a tech career but don’t want to break the bank, you’re in the right place. These 7 hand-picked resources are free and help you build real, job-ready skills—from web development to machine learning and AI. 𝐋𝐢𝐧𝐤 👇:- https://pdlink.in/4j1lqbJ Enroll for FREE & Get Certified 🎓

Ford Hiring Data Scientist Apply link: https://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/job/44845/?utm_medium=jobboard&utm_source=linkedin 👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J 👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5 All the best 👍👍

𝗧𝗼𝗽 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝟮𝟬𝟮𝟱 — 𝗥𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗔𝘀𝗸𝗲𝗱 𝗯𝘆 𝗠𝗡𝗖𝘀😍 📌 Preparing for Python Interviews in 2025?🗣 If you’re aiming for roles in data analysis, backend development, or automation, Python is your key weapon—and so is preparing with the right questions.💻✨️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3ZbAtrW Crack your next Python interview✅️

Uber is hiring! Position: Data Scientist, Analytics Qualification: Bachelor’s/ Master’s Degree Salary: 16 - 46 LPA (Expected) Experience: 1 - 2 (Years) Location: Hyderabad; Bangalore, India 📌Apply Now: https://www.uber.com/global/en/careers/list/138137/?uclick_id=210f8bf2-9303-4def-82f0-89fbaa85a039 👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J 👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5 All the best 👍👍

𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗘𝘃𝗲𝗿𝘆 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗦𝘁𝗮𝗿𝘁 𝗪𝗶𝘁𝗵😍 💻 Want to Learn Coding but Don’t Know Where to Start?🎯 Whether you’re a student, career switcher, or complete beginner, this curated list is your perfect launchpad into tech💻🚀 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/437ow7Y All The Best 🎊

HDFC securities hiring Information Technology Analyst - Artificial Intelligence https://www.hirist.tech/j/hdfc-securities-information-technology-analyst-artificial-intelligence-1477405.html

If you want a data role THIS year, don't just create value, CAPTURE it. 🟠 Creating value - Build end-to-end data projects - Work with cloud providers (AWS, Azure, GCP) - Learn fundamentals (SQL, Excel, Power BI, Python) 🟢 Capture value - Show your projects online (GitHub, LinkedIn) - Network with data pros and hiring managers - Quantify your achievements on your resume + interviews

𝟱 𝗙𝗥𝗘𝗘 𝗖𝘆𝗯𝗲𝗿 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 Break into the world of Cybersecurity without spending a dime!🚀 These 5 beginner-friendly courses are your gateway to mastering essential skills and advancing your career👨‍💻📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4fA9JXx 🌟 Don’t Wait! Start now and unlock endless possibilities!✅️

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝟭𝟬𝟬% 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗔𝘇𝘂𝗿𝗲, 𝗔𝗜, 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗠𝗼𝗿𝗲😍 Want to upskill in Azure, AI, Cybersecurity, or App Development—without spending a single rupee?👨‍💻🎯 Enter Microsoft Learn — a 100% free platform that offers expert-led learning paths to help you grow📊📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4k6lA2b Enjoy Learning ✅️

Some essential concepts every data scientist should understand: ### 1. Statistics and Probability - Purpose: Understanding data distributions and making inferences. - Core Concepts: Descriptive statistics (mean, median, mode), inferential statistics, probability distributions (normal, binomial), hypothesis testing, p-values, confidence intervals. ### 2. Programming Languages - Purpose: Implementing data analysis and machine learning algorithms. - Popular Languages: Python, R. - Libraries: NumPy, Pandas, Scikit-learn (Python), dplyr, ggplot2 (R). ### 3. Data Wrangling - Purpose: Cleaning and transforming raw data into a usable format. - Techniques: Handling missing values, data normalization, feature engineering, data aggregation. ### 4. Exploratory Data Analysis (EDA) - Purpose: Summarizing the main characteristics of a dataset, often using visual methods. - Tools: Matplotlib, Seaborn (Python), ggplot2 (R). - Techniques: Histograms, scatter plots, box plots, correlation matrices. ### 5. Machine Learning - Purpose: Building models to make predictions or find patterns in data. - Core Concepts: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation (accuracy, precision, recall, F1 score). - Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-means clustering, principal component analysis (PCA). ### 6. Deep Learning - Purpose: Advanced machine learning techniques using neural networks. - Core Concepts: Neural networks, backpropagation, activation functions, overfitting, dropout. - Frameworks: TensorFlow, Keras, PyTorch. ### 7. Natural Language Processing (NLP) - Purpose: Analyzing and modeling textual data. - Core Concepts: Tokenization, stemming, lemmatization, TF-IDF, word embeddings. - Techniques: Sentiment analysis, topic modeling, named entity recognition (NER). ### 8. Data Visualization - Purpose: Communicating insights through graphical representations. - Tools: Matplotlib, Seaborn, Plotly (Python), ggplot2, Shiny (R), Tableau. - Techniques: Bar charts, line graphs, heatmaps, interactive dashboards. ### 9. Big Data Technologies - Purpose: Handling and analyzing large volumes of data. - Technologies: Hadoop, Spark. - Core Concepts: Distributed computing, MapReduce, parallel processing. ### 10. Databases - Purpose: Storing and retrieving data efficiently. - Types: SQL databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Cassandra). - Core Concepts: Querying, indexing, normalization, transactions. ### 11. Time Series Analysis - Purpose: Analyzing data points collected or recorded at specific time intervals. - Core Concepts: Trend analysis, seasonal decomposition, ARIMA models, exponential smoothing. ### 12. Model Deployment and Productionization - Purpose: Integrating machine learning models into production environments. - Techniques: API development, containerization (Docker), model serving (Flask, FastAPI). - Tools: MLflow, TensorFlow Serving, Kubernetes. ### 13. Data Ethics and Privacy - Purpose: Ensuring ethical use and privacy of data. - Core Concepts: Bias in data, ethical considerations, data anonymization, GDPR compliance. ### 14. Business Acumen - Purpose: Aligning data science projects with business goals. - Core Concepts: Understanding key performance indicators (KPIs), domain knowledge, stakeholder communication. ### 15. Collaboration and Version Control - Purpose: Managing code changes and collaborative work. - Tools: Git, GitHub, GitLab. - Practices: Version control, code reviews, collaborative development. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 ENJOY LEARNING 👍👍