1 / 13

data science course visit nowhttpstechcadd.com

TechCADD offers the best Computer Training Institute in Jalandhar,Punjab.join us for expert-led courses and unleash your skills.<br>VISIT NOW:https://techcadd.com/best-data-science-course-in-jalandhar.php

Gagan29
Download Presentation

data science course visit nowhttpstechcadd.com

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. DATA SCIENCE COURSE VISIT NOW:HTTPS://TECHCADD.COM/ CONTACT NO:9888122255

  2. INTRODUCTION Data Science is a multidisciplinary field that combines statistics, computer science, and domain knowledge to extract meaningful insights from structured and unstructured data. This course provides a foundational understanding of data science principles, tools, and techniques.

  3. WHAT IS DATA SCIENCE? DEFINITION OF DATA SCIENCE IMPORTANCE IN TODAY'S WORLD FIELDS/DATA SCIENCE TOUCHES (FINANCE, HEALTHCARE, MARKETING, ETC.)

  4. DATA COLLECTION • DATA CLEANING • DATA EXPLORATION • MODELING • EVALUATION • DEPLOYMENT DATA SCIENCE LIFECYCLE: A

  5. DATA COLLECTION AND CLEANING: • DATA SOURCES • (APIS, DATABASES, FILES) • HANDLING MISSING DATA • DATA TYPES AND CONVERSIONS • IMPORTANCE OF CLEAN DATA

  6. CORE COMPONENTS OF DATA SCIENCE: • STATISTICS • PROGRAMMING (PYTHON/R) • MACHINE LEARNING • DATA VISUALIZATION • DOMAIN KNOWLEDGE

  7. TOOLS AND TECHNOLOGIES: • Programming Languages: Python, R • Libraries: Pandas, NumPy, Scikit-learn, TensorFlow • Tools: Jupyter, Tableau, Power BI • Databases: SQL, MongoDB

  8. INTRODUCTION TO MACHINE LEARNING: • WHAT IS ML? • TYPES: SUPERVISED, • UNSUPERVISED • REINFORCEMENT LEARNING • EXAMPLES: SPAM DETECTION, • RECOMMENDATION SYSTEMS

  9. DATA VISUALISATION: • IMPORTANCE OF DATA STORYTELLING • POPULAR TOOLS: MATPLOTLIB, SEABORN, PLOTLY • DASHBOARDS & REPORTS

  10. REAL WORLD APPLICATIONS • Predictive analytics in healthcare • Fraud detection in banking • Recommendation systems in e-commerce • AI in customer service

  11. CAREER PATHWAYS IN DATA SCIENCE: • DATA ANALYST • DATA SCIENTIST • MACHINE LEARNING • ENGINEER • DATA ENGINEER • BUSINESS INTELLIGENCE • ANALYST

  12. CONCLUSION: • Summary of key points • Recommended learning resources • Q&A or Feedback • Contact info or course registration link (if applicable)

  13. THANKYOU...... VISIT NOW:https://techcadd.com/

More Related