8 courses for Data science

No prior coding experience? No problem! This beginner-friendly Python for Data Science course equips you with the fundamental programming skills to launch into data science. Learn core Python concepts, explore essential libraries like NumPy and Pandas, and master data manipulation and visualization using Jupyter Notebooks. By the end, you'll be writing Python scripts and transforming data into insights.

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Eligible : Open To all Freshers & College Students (UG,PG) What We Offer ? (Mode : Online Remote ) ✅ Internship + Training with Offer Letter & Course Completion Certificate ✅ ₹25,000 Per Month Internship For 6 Months After Course Completion . ✅PPO up to 4.5–8 LPA( Based on internship performance ) ✅ Duration: 1 to 6 Months . ✅ Weekend Live Session: Every Sunday at 6 PM ✅ Letter of Recommendation Included ✅ 100% Placement Support After Course + Projects ✅ 4 Minor & 2 Major Real-Time Projects ✅ Lab Sessions for Every Chapter ✅ Artificial intelligence: Scratch to Advanced Level ✅ Recorded Theory & Lab Sessions + Weekly Assignments 📘 Data Science – Course Index – Scratch to Advance 1.Introduction to Data Science 2. Mathematics for Data Science (Linear Algebra, Calculus, Probability, Statistics) 3. Python for Data Science (NumPy, Pandas, Matplotlib) 4. Data Collection & Data Sources 5. Data Cleaning & Preprocessing 6. Exploratory Data Analysis (EDA) 7. Data Visualization (Matplotlib, Seaborn, Plotly) 8. Feature Engineering 9. Machine Learning Basics 10. Supervised Learning Algorithms 11. Unsupervised Learning Algorithms 12. Model Evaluation & Metrics 13. Hyperparameter Tuning 14. Model Deployment Basics 15. Deep Learning Introduction 16. Neural Networks & Backpropagation 17. Convolutional Neural Networks (CNN) 18. Natural Language Processing (NLP) 19. Time Series Analysis 20. Recommender Systems 21. Big Data Basics (Hadoop, Spark – Intro) 22. Cloud & ML Deployment (AWS, GCP, Azure – Intro) 23. Data Pipelines & MLOps (Intro) 24. Data Science Tools (Jupyter, Colab, Git, APIs)

Unlock the power of Python programming with a structured, hands-on approach. This course will guide you through: The fundamentals of Python syntax and concepts Writing efficient Python code for real-world applications Working with data, loops, functions, and libraries Building projects that reinforce your learning Tips and best practices from industry experts to write clean, maintainable Python code Whether you’re starting from scratch or looking to sharpen your skills, this course provides a clear path to becoming a confident Python programmer.

🧠 WHO THIS IS FOR 🎓 Freshers struggling to get shortlisted for Data Science / ML roles 🔁 Software Engineers / QA / Analysts trying to transition into AI/ML 📉 Professionals stuck with courses but no real, deployable projects 👉 If you want job-ready credibility, this program is for you. 1️⃣ 1:1 AI/ML CAREER ROADMAP Role-specific path: Data Scientist / ML Engineer / GenAI / MLOps Skill gap → project → interview plan 2️⃣ RESUME & LINKEDIN - SHORTLISTED AI/ML-optimized resume & cover letter ATS keyword alignment for DS / ML roles 3️⃣ REAL INDUSTRY-GRADE AI PROJECTS You don’t just learn — you build real systems. Includes: End-to-end MLOps pipelines Full-stack ML systems Tableau & Data Visualization projects 4️⃣ HIGH-IMPACT PROJECT PORTFOLIO Hands-on experience with projects like: Movie Recommendation System Credit Card Fraud Detection System LLM-powered Instagram Search Engine... 5️⃣ AI SYSTEM DESIGN & ARCHITECTURE Research documentation HLD & LLD (High-Level & Low-Level Design) How to explain architecture clearly in interviews 6️⃣ INTERVIEW MASTERY DS / ML / GenAI interview frameworks Case-based problem solving Mock interviews with direct feedback 🎁 BONUSES AI/ML Interview Question Bank Project explanation & storytelling templates Lifetime updates Disclaimer: These resources are intended for learning and interview preparation support only; results may vary based on individual effort and experience.

This content is specially made for: • IGNOU self-learning students • Students who haven’t studied yet • Last-minute exam revision the whole book in short. • TEE 2025 MCA learners

Eligible : Open To all Freshers & College Students (UG,PG) What We Offer ? (Mode : Online Remote ) ✅ Internship + Training with Offer Letter & Course Completion Certificate ✅ ₹25,000 Per Month Internship For 6 Months After Course Completion ✅ PPO up to 4.5–8 LPA( Based on internship performance ) ✅ Duration: 1 to 6 Months ✅ Weekend Live Session: Every Sunday at 6 PM ✅ Letter of Recommendation Included ✅ 100% Placement Support After Course + Projects ✅ 4 Minor & 2 Major Real-Time Projects ✅ Lab Sessions for Every Chapter ✅ Data Analysis : Scratch to Advanced Level ✅ Recorded Theory & Lab Sessions + Weekly Assignments 📘 Data Analytics – Course Index – Scratch to Advance 1. Introduction to Data Analytics 2. Data Analytics vs Data Analysis vs Data Science 3. Types of Data & Data Sources 4. Statistics Essentials for Data Analytics 5. Probability & Distributions 6. Data Collection Techniques 7. Data Cleaning & Preprocessing 8. Data Wrangling Techniques 9. Exploratory Data Analysis (EDA) 10. Data Visualization Fundamentals 11. Tools for Data Analytics (Excel, SQL, Python) 12. Advanced Excel for Data Analytics 13. SQL for Data Analytics 14. Python for Data Analytics 15. Working with Libraries (NumPy, Pandas) 16. Data Visualization Tools (Matplotlib, Plotly, Power BI, Tableau) 17. Business Intelligence (BI) Concepts 18. Dashboard Creation (Power BI / Tableau) 19. Data Interpretation & Insights 20. Predictive Analytics (Basics) 21. Machine Learning for Data Analytics (Overview) 22. Regression & Classification Concepts 23. Time Series Analytics (Basic Concepts) 24. A/B Testing & Experimentation 25. Data Storytelling & Presentation Skills 26. Domain-Specific Analytics (Finance, Healthcare, Marketing, HR) 27. Working with Big Data Tools (Basics – Hadoop, Spark) 28. Cloud Platforms for Analytics (AWS / Azure Basics) 29. Automation in Data Analytics