𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝘃𝘀. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘃𝘀. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝘃𝘀. 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁
Think of them as data detectives.
→ 𝐅𝐨𝐜𝐮𝐬: Identifying patterns and building predictive models.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Machine learning, statistics, Python/R.
→ 𝐓𝐨𝐨𝐥𝐬: Jupyter Notebooks, TensorFlow, PyTorch.
→ 𝐆𝐨𝐚𝐥: Extract actionable insights from raw data.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Creating a recommendation system like Netflix.
𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿
The architects of data infrastructure.
→ 𝐅𝐨𝐜𝐮𝐬: Developing data pipelines, storage systems, and infrastructure. → 𝐒𝐤𝐢𝐥𝐥𝐬: SQL, Big Data technologies (Hadoop, Spark), cloud platforms.
→ 𝐓𝐨𝐨𝐥𝐬: Airflow, Kafka, Snowflake.
→ 𝐆𝐨𝐚𝐥: Ensure seamless data flow across the organization.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Designing a pipeline to handle millions of transactions in real-time.
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
Data storytellers.
→ 𝐅𝐨𝐜𝐮𝐬: Creating visualizations, dashboards, and reports.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Excel, Tableau, SQL.
→ 𝐓𝐨𝐨𝐥𝐬: Power BI, Looker, Google Sheets.
→ 𝐆𝐨𝐚𝐥: Help businesses make data-driven decisions.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Analyzing campaign data to optimize marketing strategies.
𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿
The connectors between data science and software engineering.
→ 𝐅𝐨𝐜𝐮𝐬: Deploying machine learning models into production.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Python, APIs, cloud services (AWS, Azure).
→ 𝐓𝐨𝐨𝐥𝐬: Kubernetes, Docker, FastAPI.
→ 𝐆𝐨𝐚𝐥: Make models scalable and ready for real-world applications. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Deploying a fraud detection model for a bank.
𝗪𝗵𝗮𝘁 𝗣𝗮𝘁𝗵 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗖𝗵𝗼𝗼𝘀𝗲?
☑ Love solving complex problems?
→ Data Scientist
☑ Enjoy working with systems and Big Data?
→ Data Engineer
☑ Passionate about visual storytelling?
→ Data Analyst
☑ Excited to scale AI systems?
→ ML Engineer
Each role is crucial and in demand—choose based on your strengths and career aspirations.
What’s your ideal role?
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁
Think of them as data detectives.
→ 𝐅𝐨𝐜𝐮𝐬: Identifying patterns and building predictive models.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Machine learning, statistics, Python/R.
→ 𝐓𝐨𝐨𝐥𝐬: Jupyter Notebooks, TensorFlow, PyTorch.
→ 𝐆𝐨𝐚𝐥: Extract actionable insights from raw data.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Creating a recommendation system like Netflix.
𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿
The architects of data infrastructure.
→ 𝐅𝐨𝐜𝐮𝐬: Developing data pipelines, storage systems, and infrastructure. → 𝐒𝐤𝐢𝐥𝐥𝐬: SQL, Big Data technologies (Hadoop, Spark), cloud platforms.
→ 𝐓𝐨𝐨𝐥𝐬: Airflow, Kafka, Snowflake.
→ 𝐆𝐨𝐚𝐥: Ensure seamless data flow across the organization.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Designing a pipeline to handle millions of transactions in real-time.
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
Data storytellers.
→ 𝐅𝐨𝐜𝐮𝐬: Creating visualizations, dashboards, and reports.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Excel, Tableau, SQL.
→ 𝐓𝐨𝐨𝐥𝐬: Power BI, Looker, Google Sheets.
→ 𝐆𝐨𝐚𝐥: Help businesses make data-driven decisions.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Analyzing campaign data to optimize marketing strategies.
𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿
The connectors between data science and software engineering.
→ 𝐅𝐨𝐜𝐮𝐬: Deploying machine learning models into production.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Python, APIs, cloud services (AWS, Azure).
→ 𝐓𝐨𝐨𝐥𝐬: Kubernetes, Docker, FastAPI.
→ 𝐆𝐨𝐚𝐥: Make models scalable and ready for real-world applications. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Deploying a fraud detection model for a bank.
𝗪𝗵𝗮𝘁 𝗣𝗮𝘁𝗵 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗖𝗵𝗼𝗼𝘀𝗲?
☑ Love solving complex problems?
→ Data Scientist
☑ Enjoy working with systems and Big Data?
→ Data Engineer
☑ Passionate about visual storytelling?
→ Data Analyst
☑ Excited to scale AI systems?
→ ML Engineer
Each role is crucial and in demand—choose based on your strengths and career aspirations.
What’s your ideal role?
❤8👍1
Join our WhatsApp channel
There are dedicated resources only for WhatsApp users
👇👇
https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
There are dedicated resources only for WhatsApp users
👇👇
https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
❤2
Why do we apply feature scaling in machine learning?
Anonymous Quiz
24%
a) To improve the accuracy of decision trees
13%
b) To reduce model complexity
53%
c) To ensure all features contribute equally to distance-based algorithms
9%
d) To remove irrelevant features
❤3👍2
Which of the following methods is least affected by outliers?
Anonymous Quiz
22%
a) Min-Max Scaling
43%
b) Standardization (Z-score)
25%
c) Robust Scaler
10%
d) MaxAbs Scaler
❤3👍1
After applying StandardScaler, the mean of each feature becomes:
Anonymous Quiz
33%
a) 0
22%
b) 1
19%
c) The same as original
25%
d) Dependent on feature distribution
❤4👍1
Which scaling technique would be most suitable for K-Nearest Neighbors (KNN)?
Anonymous Quiz
13%
a) No scaling needed
51%
b) Min-Max Scaling or Standardization
25%
c) PCA
10%
d) Label Encoding
❤4👍1
Which scaler transforms features by removing the median and scaling by the interquartile range?
Anonymous Quiz
35%
a) StandardScaler
29%
b) MinMaxScaler
24%
c) RobustScaler
12%
d) Normalizer
❤3👍2
🚀👉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 denoscription, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
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 denoscription, emphasizing the skills and experiences that align with the requirements of the position you're applying for.
❤8🔥1
1️⃣ What is the primary use of OpenCV?
Anonymous Quiz
4%
A) Web development
78%
B) Computer Vision & Image Processing
16%
C) Data analysis
1%
D) Game development
❤2
2️⃣ Which function is used to read an image in OpenCV?
Anonymous Quiz
15%
A) cv2.show()
18%
B) cv2.display()
48%
C) cv2.imread()
20%
D) cv2.readimg()
❤4
3️⃣ What does cv2.cvtColor() do?
Anonymous Quiz
3%
A) Captures video
87%
B) Converts image color space
9%
C) Applies filters
2%
D) Detects faces
❤4
4️⃣ What key is commonly used to exit a video loop in OpenCV?
Anonymous Quiz
48%
A) ESC
13%
B) Enter
29%
C) q
10%
D) Spacebar
❤4
5️⃣ Which format does OpenCV use for image data internally?
Anonymous Quiz
8%
A) Lists
47%
B) NumPy arrays
15%
C) Dictionaries
30%
D) Pandas DataFrame
❤4
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()
3️⃣ What does
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
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
React ❤️ for more**
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
React ❤️ for more**
❤6