Mohini S.’s Post

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟰-𝟮𝟬𝟮𝟱 This roadmap provides a structured path to become a proficient data scientist. Each step builds on the previous one, covering fundamental to advanced topics in data science. Step 1: Introduction to Data Science 1. Title: What is Data Science? - Course: https://lnkd.in/d2WADtF9 2. Title: Python for Data Science and AI - Course: https://lnkd.in/d_X_7rTP Step 2: Data Wrangling and Exploration 1. Title: Data Wrangling and Exploration - Course: https://lnkd.in/d2K6PSsw 2. Title: Data Visualization with Python - Course: https://lnkd.in/dj7TU6nP Step 3: Machine Learning Fundamentals 1. Title: Machine Learning for Everyone - Course: https://lnkd.in/dFcSUdyR 2. Title: Applied Machine Learning - Course: https://lnkd.in/gdGWYu5Q Step 4: Advanced Machine Learning and AI 1. Title: Deep Learning Specialization - Course: https://lnkd.in/dfnDZ9Rz 2. Title: AI for Everyone - Course: https://lnkd.in/dzm_75d5 Step 5: Big Data and Data Engineering 1. Title: Big Data Specialization - Course: https://lnkd.in/db_xCa59 2. Title: Data Engineering on Google Cloud - Course: https://lnkd.in/d8gh5Sz9 Step 6: Topics and Advanced Tools 1. Title: Natural Language Processing Specialization - Course: https://lnkd.in/d2sHbPm9 2. Title: TensorFlow in Practice - Course: https://lnkd.in/d3Y_umHP Step 7: Business and Communication Skills 1. Title: Data Science and Business Analytics - Course: https://lnkd.in/dVkDQp-B 2. Title: Data Science Professional Certificate - Course: https://lnkd.in/enhr5QNt Step 8: Capstone Project and Real-World Experience 1. Title: Applied Data Science Capstone - Course: https://lnkd.in/dAsfkN3g 2. Title: Data Science at Scale Specialization - Course: https://lnkd.in/dNthYZ_q

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟰-𝟮𝟬𝟮𝟱 This roadmap provides a structured path to become a proficient data scientist. Each step builds on the previous one, covering fundamental to advanced topics in data science. Step 1: Introduction to Data Science 1. Title: What is Data Science? - Course: https://lnkd.in/d2WADtF9 2. Title: Python for Data Science and AI - Course: https://lnkd.in/d_X_7rTP Step 2: Data Wrangling and Exploration 1. Title: Data Wrangling and Exploration - Course: https://lnkd.in/d2K6PSsw 2. Title: Data Visualization with Python - Course: https://lnkd.in/dj7TU6nP Step 3: Machine Learning Fundamentals 1. Title: Machine Learning for Everyone - Course: https://lnkd.in/dFcSUdyR 2. Title: Applied Machine Learning - Course: https://lnkd.in/gdGWYu5Q Step 4: Advanced Machine Learning and AI 1. Title: Deep Learning Specialization - Course: https://lnkd.in/dfnDZ9Rz 2. Title: AI for Everyone - Course: https://lnkd.in/dzm_75d5 Step 5: Big Data and Data Engineering 1. Title: Big Data Specialization - Course: https://lnkd.in/db_xCa59 2. Title: Data Engineering on Google Cloud - Course: https://lnkd.in/d8gh5Sz9 Step 6: Topics and Advanced Tools 1. Title: Natural Language Processing Specialization - Course: https://lnkd.in/d2sHbPm9 2. Title: TensorFlow in Practice - Course: https://lnkd.in/d3Y_umHP Step 7: Business and Communication Skills 1. Title: Data Science and Business Analytics - Course: https://lnkd.in/dVkDQp-B 2. Title: Data Science Professional Certificate - Course: https://lnkd.in/enhr5QNt Step 8: Capstone Project and Real-World Experience 1. Title: Applied Data Science Capstone - Course: https://lnkd.in/dEejHZPf 2. Title: Data Science at Scale Specialization - Course: https://lnkd.in/d3fz6mQV

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟰-𝟮𝟬𝟮𝟱 This roadmap provides a structured path to become a proficient data scientist. Each step builds on the previous one, covering fundamental to advanced topics in data science. Step 1: Introduction to Data Science 1. Title: What is Data Science? - Course: https://lnkd.in/d2WADtF9 2. Title: Python for Data Science and AI - Course: https://lnkd.in/d_X_7rTP Step 2: Data Wrangling and Exploration 1. Title: Data Wrangling and Exploration - Course: https://lnkd.in/d2K6PSsw 2. Title: Data Visualization with Python - Course: https://lnkd.in/dj7TU6nP Step 3: Machine Learning Fundamentals 1. Title: Machine Learning for Everyone - Course: https://lnkd.in/dFcSUdyR 2. Title: Applied Machine Learning - Course: https://lnkd.in/gdGWYu5Q Step 4: Advanced Machine Learning and AI 1. Title: Deep Learning Specialization - Course: https://lnkd.in/dfnDZ9Rz 2. Title: AI for Everyone - Course: https://lnkd.in/dzm_75d5 Step 5: Big Data and Data Engineering 1. Title: Big Data Specialization - Course: https://lnkd.in/db_xCa59 2. Title: Data Engineering on Google Cloud - Course: https://lnkd.in/d8gh5Sz9 Step 6: Topics and Advanced Tools 1. Title: Natural Language Processing Specialization - Course: https://lnkd.in/d2sHbPm9 2. Title: TensorFlow in Practice - Course: https://lnkd.in/d3Y_umHP Step 7: Business and Communication Skills 1. Title: Data Science and Business Analytics - Course: https://lnkd.in/dVkDQp-B 2. Title: Data Science Professional Certificate - Course: https://lnkd.in/d-MAh-pb Step 8: Capstone Project and Real-World Experience 1. Title: Applied Data Science Capstone - Course: https://lnkd.in/dAsfkN3g 2. Title: Data Science at Scale Specialization - Course: https://lnkd.in/dNthYZ_q Image source: Data Science Dojo

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟰-𝟮𝟬𝟮𝟱 This roadmap provides a structured path to become a proficient data scientist. Each step builds on the previous one, covering fundamental to advanced topics in data science. Step 1: Introduction to Data Science 1. Title: What is Data Science? - Course: https://lnkd.in/dDw7vz3E 2. Title: Python for Data Science and AI - Course: https://lnkd.in/diya8gaw Step 2: Data Wrangling and Exploration 1. Title: Data Wrangling and Exploration - Course: https://lnkd.in/dWdVfmkE 2. Title: Data Visualization with Python - Course: https://lnkd.in/d7ScjPws Step 3: Machine Learning Fundamentals 1. Title: Machine Learning for Everyone - Course: https://lnkd.in/dmas9aNm 2. Title: Applied Machine Learning - Course: https://lnkd.in/dDmnzhuA Step 4: Advanced Machine Learning and AI 1. Title: Deep Learning Specialization - Course: https://lnkd.in/dYgzAy_S 2. Title: AI for Everyone - Course: https://lnkd.in/dts6_yvz Step 5: Big Data and Data Engineering 1. Title: Big Data Specialization - Course: https://lnkd.in/dfTxd8fj 2. Title: Data Engineering on Google Cloud - Course: https://lnkd.in/d9V2rmHn Step 6: Topics and Advanced Tools 1. Title: Natural Language Processing Specialization - Course: https://lnkd.in/dH5jy93Z 2. Title: TensorFlow in Practice - Course: https://lnkd.in/dNWviSXv Step 7: Business and Communication Skills 1. Title: Data Science and Business Analytics - Course: https://lnkd.in/dxQYFfVg 2. Title: Data Science Professional Certificate - Course: https://lnkd.in/dT_SEJ-z Step 8: Capstone Project and Real-World Experience 1. Title: Applied Data Science Capstone - Course: https://lnkd.in/dUV2GR4x 2. Title: Data Science at Scale Specialization - Course: https://lnkd.in/djzN5JZd Join for free resources https://lnkd.in/gh25uigq Image source: @Data science Dojo

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟰-𝟮𝟬𝟮𝟱 IBM Data Science Professional Certificate https://lnkd.in/d-geZUzZ Step 1: Introduction to Data Science 1. Title: What is Data Science? - Course: https://lnkd.in/d2WADtF9 2. Title: Python for Data Science and AI - Course: https://lnkd.in/d_X_7rTP Step 2: Data Wrangling and Exploration 1. Title: Data Wrangling and Exploration - Course: https://lnkd.in/d2K6PSsw 2. Title: Data Visualization with Python - Course: https://lnkd.in/dj7TU6nP Step 3: Machine Learning Fundamentals 1. Title: Machine Learning for Everyone - Course: https://lnkd.in/dFcSUdyR 2. Title: Applied Machine Learning - Course: https://lnkd.in/gdGWYu5Q Step 4: Advanced Machine Learning and AI 1. Title: Deep Learning Specialization - Course: https://lnkd.in/dfnDZ9Rz 2. Title: AI for Everyone - Course: https://lnkd.in/dzm_75d5 Step 5: Big Data and Data Engineering 1. Title: Big Data Specialization - Course: https://lnkd.in/db_xCa59 2. Title: Data Engineering on Google Cloud - Course: https://lnkd.in/d8gh5Sz9 Step 6: Topics and Advanced Tools 1. Title: Natural Language Processing Specialization - Course: https://lnkd.in/d2sHbPm9 2. Title: TensorFlow in Practice - Course: https://lnkd.in/d3Y_umHP Step 7: Business and Communication Skills 1. Title: Data Science and Business Analytics - Course: https://lnkd.in/dVkDQp-B 2. Title: Data Science Professional Certificate - Course: https://lnkd.in/enhr5QNt Step 8: Capstone Project and Real-World Experience 1. Title: Applied Data Science Capstone - Course: https://lnkd.in/dy6GSB5G 2. Title: Data Science at Scale Specialization - Course: https://lnkd.in/dBmvmSRq #datascience #data

Data Science Roadmap - a comprehensive guide. 🚀 From foundational concepts to advanced techniques, discover the milestones that pave the way to becoming a proficient data scientist. 🌐 Stay ahead by embracing continuous learning, honing skills in data visualization, predictive analytics, and leveraging emerging technologies. 📈 Complete Roadmap to become a Data Scientist in 4 months: Week 1-2: Fundamentals - Day 1-3: Introduction to Data Science, its applications, and roles. - Day 4-7: Brush up on Python programming. - Day 8-10: Learn basic statistics and probability. Week 3-4: Data Manipulation and Visualization - Day 11-15: Pandas for data manipulation. - Day 16-20: Data visualization with Matplotlib and Seaborn. Week 5-6: Machine Learning Foundations - Day 21-25: Introduction to scikit-learn. - Day 26-30: Linear regression and logistic regression. Week 7-8: Advanced Machine Learning - Day 31-35: Decision trees and random forests. - Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction. Week 9-10: Deep Learning - Day 41-45: Basics of Neural Networks and TensorFlow/Keras. - Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Week 11-12: Data Engineering - Day 51-55: Learn about SQL and databases. - Day 56-60: Data preprocessing and cleaning. Week 13-14: Model Evaluation and Optimization - Day 61-65: Cross-validation, hyperparameter tuning. - Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score). Week 15-16: Big Data and Tools - Day 71-75: Introduction to big data technologies (Hadoop, Spark). - Day 76-80: Basics of cloud computing (AWS, GCP, Azure). Week 17-18: Deployment and Production - Day 81-85: Model deployment with Flask or FastAPI. - Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku). Week 19-20: Specialization - Day 91-95: NLP or Computer Vision, based on your interests. Week 21-22: Projects and Portfolios - Day 96-100: Work on personal data science projects. Week 23-24: Soft Skills and Networking - Day 101-105: Improve communication and presentation skills. - Day 106-110: Attend online data science meetups or forums. Week 25-26: Interview Preparation - Day 111-115: Practice coding interviews on platforms like LeetCode. - Day 116-120: Review your projects and be ready to discuss them. Week 27-28: Apply for Jobs - Day 121-125: Start applying for entry-level data scientist positions. Week 29-30: Interviews - Day 126-130: Attend interviews, practice whiteboard problems. Week 31-32: Continuous Learning - Day 131-135: Stay updated with the latest trends in data science. 📌Follow Ankit Kumar Singh for more such Content 🌟 _____________________________________________________ #datascience #roadmaptosuccess #analytics #continuouslearning #analyticsjourney

Hello LinkedIn Community, I hope this post finds you well. I'm thrilled to share with you some of my 𝗿𝗲𝗰𝗲𝗻𝘁 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗮 𝘀𝗲𝗿𝗶𝗲𝘀 𝗼𝗳 𝗠𝗲𝗱𝗶𝘂𝗺 𝗮𝗿𝘁𝗶𝗰𝗹𝗲𝘀. As a passionate data science enthusiast, I've embarked on a journey to explore and explain the concepts of Data science, and machine learning. These articles not only serve as a way for me to document my learning but also aim to provide insightful guidance to fellow learners and professionals in the field. 1. 𝗔𝗜, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: 𝗨𝗻𝗿𝗮𝘃𝗲𝗹𝗶𝗻𝗴 𝘁𝗵𝗲 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 (https://lnkd.in/gtJK9RuA) In this article, I delve into the interconnections between AI, data science, and machine learning. From understanding the fundamentals to grasping their practical applications, this article serves as a primer for those looking for clarification on these connections. 2. 𝗔 𝗦𝗶𝗺𝗽𝗹𝗲 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗦𝘁𝗲𝗽𝘀 𝗜𝗻𝘃𝗼𝗹𝘃𝗲𝗱 𝗶𝗻 𝗮 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 (https://lnkd.in/gPZ55Pdb) Curious about data science? This article serves as a guiding light. I simplify the concept of data science, explaining its core principles and methodologies in a straightforward manner. What's more, I take you through the step-by-step journey of a real-time data science project. 3. 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗦𝗲𝗹𝗳-𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: 𝗬𝗼𝘂𝗿 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗧𝗼𝗽𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗕𝗲𝘀𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 (https://lnkd.in/g99jFvah) Learning is a lifelong journey, especially in the rapidly evolving field of data science. In this article, I share a roadmap for self-learners to master essential data science topics. I highlight key areas of study, from statistics and programming to machine learning algorithms, and provide a curated list of top free resources to facilitate effective self-learning. As I continue to explore the concepts of data science which I am eager to break down to everyone in the simplest way possible, I invite you all to join me on this enlightening path of discovery. Whether you're a seasoned professional seeking to expand your horizons or a newcomer eager to grasp the fundamentals, I will make sure these articles aim to serve as beacons of knowledge. Feel free to check out the articles on 𝗠𝘆 𝗠𝗲𝗱𝗶𝘂𝗺 𝗣𝗿𝗼𝗳𝗶𝗹𝗲 (https://lnkd.in/gEY42-Ew) and engage with them through comments and feedback. Your insights are valuable and contribute to the collective growth of the Data Science community. Thank you for reading until the end, Have a good day :) #datascience #machinelearning #mediumarticle

🚀 Exciting News for Data Science & AI Enthusiasts! 🚀 GATE 2024 has introduced a brand-new test paper on Data Science & Artificial Intelligence (DA). This is a fantastic opportunity for all tech aficionados to showcase their prowess in this rapidly evolving domain. 📅 Mark Your Calendars! Here are the key dates for GATE 2024: 📝 Start of Online Application: Last week of August 2023 🚫 Regular Registration Deadline (Without Late Fee): 5th October 2023 ⏰ Extended Registration Deadline (With Late Fee): 13th October 2023 ✏️ Application Modifications: 07th to 11th November 2023 🎟️ Admit Card Release: 3rd January 2024 📚 GATE 2024 Exams: 03, 04, 10, & 11 February 2024 📄 Candidate's Response Release: 16th February 2024 🔑 Answer Key Release: 21st February 2024 🤔 Challenge Submission on Answer Keys: 22nd to 25th February 2024 📢 Result Announcement: 16th March 2024 🏆 Score Card Availability: 23rd March 2024 🔗 Ready to Register? Dive in here: https://lnkd.in/gCV92xBA 📘 Sneak Peek into the Syllabus: Probability and Statistics: From the basics of counting to advanced topics like the chi-squared test. Linear Algebra: Dive deep into vector spaces, matrices, and more. Calculus and Optimization: Master functions, limits, and optimization techniques. Programming, Data Structures, and Algorithms: Get hands-on with Python, data structures, and essential algorithms. Database Management and Warehousing: Understand ER-models, SQL, data transformation, and warehousing concepts. Machine Learning: Explore both supervised and unsupervised learning techniques. AI: Delve into search techniques, logic, and reasoning under uncertainty. 📚Study Resources: DeepwizAI has created a free ebook for preparing for the GATE DA examination. Visit the link for more information: https://lnkd.in/gUTPH-8D Admission Opportunities with GATE: 📚 Master’s programs and direct Doctoral programs in Engineering, Technology, Science, Architecture, and Humanities. 🎓 Doctoral programs in relevant branches of Engineering, Technology, Science, Architecture, and Humanities at institutions backed by the MoE and other Government agencies. 💼 Recruitment through GATE: Did you know? Many top-tier Public Sector Undertakings (PSUs) use the GATE score for their recruitment. Here's a glimpse of some renowned PSUs: BHEL, BSNL, CIL, CRIS, CVPPL, DVC, ECIL, EIL, GAIL, IOCL, MDSL, NALCO, NHAI, NLCIL, NMDC, NPCIL, NTPC, ONGC, PGCIL, POSOCO, RINL, and more. This is your chance to shine in the world of Data Science and AI. Best of luck to all aspirants! 🌟👩💻👨💻🌟

Good resources for those who want to start their career in data science

🌸 **Excited to Share My Machine Learning Project & Data Science: Iris Flower Classification! 🌸** Hello, LinkedIn community! I am thrilled to present my latest machine learning project where I delve into the classic Iris Flower Classification problem. In this project, I utilize the power of data science to classify iris flowers into their respective species: Setosa, Versicolor, and Virginica, based on their distinct measurements. 🔍 **Project Overview:** The Iris dataset, a staple in the machine learning community, provides measurements such as sepal length, sepal width, petal length, and petal width for 150 iris flowers. The goal is to develop a model that can accurately predict the species of an iris flower given these measurements. 📊 **Steps Involved:** 1. **Data Exploration & Visualization:** Conducting an exploratory data analysis to understand the data distribution and relationships between features using Seaborn and Matplotlib. 2. **Data Preprocessing:** Handling missing values, standardizing features, and encoding categorical labels to prepare the data for modeling. 3. **Model Training:** Leveraging the K-Nearest Neighbors (KNN) algorithm to train the model, with a focus on parameter tuning to optimize performance. 4. **Model Evaluation:** Evaluating the model using metrics such as accuracy, confusion matrix, and classification report to ensure robust performance. 5. **Prediction:** Demonstrating the model's prediction capability with real-world examples. 🔧 **Technologies Used:** - **Programming Language:** Python - **Libraries:** Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn - **Tools:** Jupyter Notebook for an interactive and iterative coding environment 📈 **Results:** The model achieved impressive accuracy, effectively distinguishing between the three iris species. This project not only showcases the fundamental steps in building a classification model but also highlights the importance of data preprocessing and visualization in the data science pipeline. 🌟 **Takeaways:** This project reaffirmed my passion for data science and machine learning. It provided a hands-on experience with the complete machine learning workflow, from data exploration to model evaluation. I am excited to apply these skills to more complex datasets and real-world problems. 💬 **Feedback & Collaboration:** I would love to hear your thoughts on this project! Feel free to check out the detailed walkthrough in my Jupyter Notebook video. If you have any questions or are interested in collaborating on similar projects, please reach out. 🚀 Let's continue exploring the fascinating world of machine learning together! #MachineLearning #DataScience #IrisFlowerClassification #Python #ScikitLearn #DataVisualization #KNN #ArtificialIntelligence #DataScienceProjects #LinkedInLearning #oasisinfobyte