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🚀👉Data Analytics skills and projects to add in a resume to get shortlisted

1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.

2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.

3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.

4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.

5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.

6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.

7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.

8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.

9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.

10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.

11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.

12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.

💼Tailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.

Here are the answers for the above quizzes:

1️⃣ What is the primary use of OpenCV?
✅ B) Computer Vision & Image Processing

OpenCV is built for real-time computer vision tasks such as image processing, object detection, face recognition, and video analysis.

2️⃣ Which function is used to read an image in OpenCV?
✅ C) cv2.imread()

cv2.imread() loads an image from the specified file. It's the standard method for image reading in OpenCV.

3️⃣ What does cv2.cvtColor() do?

B) Converts image color spacece

This function converts images from one color space to another, like BGR to GRAY or BGR to HS

4️⃣ What key is commonly used to exit a video loop in OpenCV?

C) q

In many OpenCV examples, pressing the 'q' key breaks the loop and closes the video window using cv2.waitKey().

5️⃣ Which format does OpenCV use for image data internalB) NumPy arraysarrays

OpenCV stores images as NumPy arrays, allowing powerful array-based operations for fast image processing

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🔥 OpenCV – Computer Vision Library 👁️‍🗨️

OpenCV (Open Source Computer Vision Library) is an open-source library used for real-time image processing and computer vision applications.

🔹 Why Use OpenCV?
✔️ Fast image/video processing
✔️ Large collection of functions (face detection, object tracking, etc.)
✔️ Works with NumPy arrays
✔️ Cross-platform support
✔️ Integrates with deep learning models (e.g. with TensorFlow, PyTorch)

🔸 Installation
pip install opencv-python

🔸 Basic Example: Load & Display Image

import cv2

img = cv2.imread('image.jpg') # Load image
cv2.imshow('Image', img) # Display
cv2.waitKey(0) # Wait for key press
cv2.destroyAllWindows() # Close window

🔸 Read from Webcam & Convert to Grayscale

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    cv2.imshow('Grayscale Video', gray)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

🔹 Popular Features
✔️ Face Detection (Haar Cascades)
✔️ Edge Detection (Canny)
✔️ Object Tracking
✔️ Image Filtering (Blur, Sharpen)
✔️ Drawing shapes & text on images

🔹 Real-World Use Cases
✔️ Facial recognition systems
✔️ Surveillance cameras
✔️ Self-driving cars
✔️ Augmented reality

🔹 Summary

Ideal For: Developers working with images/videos or real-time vision apps
Strength: Fast processing, huge toolkit, active community

Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

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