Endi mavjud! Telegram Tadqiqoti 2025 โ yilning asosiy insaytlari 
CS CORNER Sunita Rai
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Kanal postlari
Discipline is often painful in the present, but regret is far more painful in the future. The effort we avoid today doesn't disappear.... it returns later as missed opportunities, unfulfilled dreams and the question of 'What if I had tried?'
Every small act of discipline is an investment in the life we want to build. Stay consistent, even when motivation fades, because temporary discomfort is a small price to pay compared to a lifetime of regret.
Choose discipline today, so you don't have to live with regret tomorrow.
| 2 |  Matn yo'q... | 104 |
| 3 |  Matn yo'q... | 101 |
| 4 | https://youtu.be/TB_BJ-hY9Z4 | 102 |
| 5 | https://youtu.be/ZjRN-yc0WMw | 93 |
| 6 | https://youtu.be/j_9FYVySPvA | 76 |
| 7 | https://youtu.be/IOy-V4rLBOk | 59 |
| 8 | *โ
Complete Tableau Roadmap in 2 Months ๐๐*
*Month 1: Strong Tableau Foundations*
*Week 1: Tableau Basics*
- What is Tableau and where it is used (BI, dashboards, storytelling)
- Understanding Tableau Desktop interface
- Connecting to data sources (Excel, CSV, SQL)
- Dimensions vs Measures
- Basic charts (bar, line, pie)
*Outcome:* You can create simple visualizations and understand Tableau workflow.
*Week 2: Data Visualization & Charts*
- Chart types: histogram, scatter plot, heat map
- Sorting and filtering data
- Groups and sets
- Show Me panel
- Formatting dashboards
*Outcome:* You can build clean and meaningful visualizations.
*Week 3: Calculations & Filters*
- Calculated fields (basic formulas)
- Aggregate functions (SUM, AVG, COUNT)
- Table calculations (running total, rank)
- Filters (dimension vs measure)
- Parameters
*Outcome:* You can perform data analysis inside Tableau.
*Week 4: Dashboards & Storytelling*
- Creating dashboards
- Adding actions (filter, highlight, URL)
- Using containers (horizontal, vertical)
- Story points
- Design best practices
*Outcome:* You can build interactive dashboards.
*Month 2: Intermediate to Advanced Tableau*
*Week 5: Advanced Calculations*
- LOD Expressions (FIXED, INCLUDE, EXCLUDE)
- Advanced table calculations
- Conditional logic (IF, CASE)
- Window functions
*Outcome:* You can solve complex business problems.
*Week 6: Data Preparation*
- Data blending vs joins vs relationships
- Using Tableau Prep
- Cleaning messy data
- Pivoting and reshaping data
*Outcome:* You can prepare real-world datasets for analysis.
*Week 7: Performance Optimization & Publishing*
- Performance optimization techniques
- Extract vs live connections
- Publishing dashboards to Tableau Server / Tableau Public
- User permissions and sharing
*Outcome:* You can deploy dashboards professionally.
*Week 8: Projects + Interview Prep*
Build 2โ3 real-world projects:
- Sales dashboard
- HR analytics dashboard
- Financial KPI dashboard
Also:
- Solve case studies
- Practice interview questions
- Create portfolio (GitHub + Tableau Public)
*Outcome:* You are job-ready for Tableau roles ๐
*Practice Platforms*
- Tableau Public (publish projects)
- Kaggle (datasets)
- Makeover Monday (weekly challenges)
*Pro Tip ๐ก*
Donโt just learn charts โ focus on storytelling with data.
Recruiters care more about insights than just dashboards.
*Double Tap โค๏ธ For Detailed Explanation of Each Topic* | 79 |
| 9 | https://youtu.be/j_9FYVySPvA | 73 |
| 10 |  Thank you for your love โค and support๐ฅ | 151 |
| 11 | https://youtu.be/3sHe2Qns_MU | 138 |
| 12 | Top 50 Python Interview Questions And Answers.pdf | 127 |
| 13 | โ
90 Data Science Interview Questions
Data Science Basics
1. What is data science and how is it different from data analytics?
2. What are the key steps in a data science lifecycle?
3. What types of problems does data science solve?
4. What skills does a data scientist need in real projects?
5. What is the difference between structured and unstructured data?
6. What is exploratory data analysis and why do you do it first?
7. What are common data sources in real companies?
8. What is feature engineering?
9. What is the difference between supervised and unsupervised learning?
10. What is bias in data and how does it affect models?
Statistics and Probability
11. What is the difference between mean, median, and mode?
12. What is standard deviation and variance?
13. What is probability distribution?
14. What is normal distribution and where is it used?
15. What is skewness and kurtosis?
16. What is correlation vs causation?
17. What is hypothesis testing?
18. What are Type I and Type II errors?
19. What is p-value?
20. What is confidence interval?
Data Cleaning and Preprocessing
21. How do you handle missing values?
22. How do you treat outliers?
23. What is data normalization and standardization?
24. When do you use Min-Max scaling vs Z-score?
25. How do you handle imbalanced datasets?
26. What is one-hot encoding?
27. What is label encoding?
28. How do you detect data leakage?
29. What is duplicate data and how do you handle it?
30. How do you validate data quality?
Python for Data Science
31. Why is Python popular in data science?
32. Difference between list, tuple, set, and dictionary?
33. What is NumPy and why is it fast?
34. What is Pandas and where do you use it?
35. Difference between loc and iloc?
36. What are vectorized operations?
37. What is lambda function?
38. What is list comprehension?
39. How do you handle large datasets in Python?
40. What are common Python libraries used in data science?
Data Visualization
41. Why is data visualization important?
42. Difference between bar chart and histogram?
43. When do you use box plots?
44. What does a scatter plot show?
45. What are common mistakes in data visualization?
46. Difference between Seaborn and Matplotlib?
47. What is a heatmap used for?
48. How do you visualize distributions?
49. What is dashboarding?
50. How do you choose the right chart?
Machine Learning Basics
51. What is machine learning?
52. Difference between regression and classification?
53. What is overfitting and underfitting?
54. What is train-test split?
55. What is cross-validation?
56. What is bias-variance tradeoff?
57. What is feature selection?
58. What is model evaluation?
59. What is baseline model?
60. How do you choose a model?
Supervised Learning
61. How does linear regression work?
62. Assumptions of linear regression?
63. What is logistic regression?
64. What is decision tree?
65. What is random forest?
66. What is KNN and when do you use it?
67. What is SVM?
68. How does Naive Bayes work?
69. What are ensemble methods?
70. How do you tune hyperparameters?
Unsupervised Learning
71. What is clustering?
72. Difference between K-means and hierarchical clustering?
73. How do you choose value of K?
74. What is PCA?
75. Why is dimensionality reduction needed?
76. What is anomaly detection?
77. What is association rule mining?
78. What is DBSCAN?
79. What is cosine similarity?
80. Where is unsupervised learning used?
Model Evaluation Metrics
81. What is accuracy and when is it misleading?
82. What is precision and recall?
83. What is F1 score?
84. What is ROC curve?
85. What is AUC?
86. Difference between confusion matrix metrics?
87. What is log loss?
88. What is RMSE?
89. What metric do you use for imbalanced data?
90. How do business metrics link to ML metrics? | 160 |
| 14 | https://www.instagram.com/reel/DZkc81RKRi1/?igsh=MTEyeHhpYngxaHJtaA==
Kya aapne bhi ye galti ji hai๐ | 108 |
| 15 |  https://youtu.be/c-OpmhNyKeo
Create website completely free๐ค | 156 |
| 16 |  Matn yo'q... | 173 |
| 17 | If youโre interested in building AI Agents, automating workflows, and understanding how AI can perform real-world tasks beyond simple chatbots, this video is worth watching.
https://youtu.be/rV3HJ4LEZ7k?si=bPsA9EIjuriSbe1W | 164 |
| 18 | ๐จ Job Alert
๐ Role: Java Developer (Fresher)
๐ข Company: Kiya.ai
๐ Location: Airoli / Mumbai
๐ Details:
Experience: 0-6 months
Education: BE / B.Tech (Mandatory)
Skills Required:
โข Core Java basics
โข OOP concepts
โข Claude.ai Certification (preferred)
โข Good communication skills
Interview: 2 rounds (Face-to-Face)
Great opportunity for freshers to work on real-time projects and grow their career in IT.
๐ฉ Apply: shweta.mokal@kiya.ai
Note:
I'm not a recruiter I have posted this job opportunity for jobseekers.
Please stay away from recruiters seeking for money | 150 |
| 19 | If You're Using the New Google AI Studio
1. You can publish to Google Cloud(shown in video)
2.
Export code to GitHub.
Sign in to Vercel with GitHub.
Click Import Project.
Select the repository.
Click Deploy.
You'll get a live URL in about 1โ2 minutes. | 142 |
| 20 | Prompt for Student Management Hub shown in video๐
Create a production-ready full-๐stack Student Management System.
Tech Stack:
* Frontend: React.js + Tailwind CSS
* Backend: Node.js + Express.js
* Database: MongoDB
* Authentication: JWT Authentication
* Password Hashing: bcrypt
* Charts: Chart.js
* Icons: Lucide React
* API Architecture: RESTful APIs
Build a complete working application with frontend, backend, and database integration.
Modules Required:
1. Authentication System
* Admin Login
* Admin Registration
* JWT Authentication
* Protected Routes
* Forgot Password
* Logout Functionality
2. Dashboard
* Total Students
* Total Teachers
* Total Courses
* Attendance Percentage
* Recent Activities
* Performance Analytics
* Interactive Charts
* Statistics Cards
3. Student Management Module
* Add Student
* Edit Student
* Delete Student
* Search Student
* Filter Student
* Student Profile Page
Student Fields:
* Student ID
* Full Name
* Email
* Phone Number
* Department
* Semester
* Address
* Gender
* Date of Birth
* Profile Image
4. Attendance Management
* Mark Attendance
* Daily Attendance
* Monthly Attendance
* Attendance Reports
* Attendance Percentage Calculation
5. Marks Management
* Add Marks
* Edit Marks
* Delete Marks
* Subject-wise Results
* Percentage Calculation
* Grade Calculation
* Student Result Dashboard
6. Faculty Management
* Add Faculty
* Edit Faculty
* Delete Faculty
* Department Allocation
7. Reports Module
* Student Reports
* Attendance Reports
* Result Reports
* Export PDF
* Export Excel
8. Settings Module
* Profile Settings
* Security Settings
* Notification Settings
Backend Requirements:
* MongoDB Database Models
* Authentication APIs
* Student CRUD APIs
* Faculty CRUD APIs
* Attendance APIs
* Marks APIs
* Dashboard Analytics APIs
* Validation Middleware
* Error Handling Middleware
Database Collections:
* Users
* Students
* Faculty
* Attendance
* Marks
* Departments
UI Requirements:
* Modern SaaS Dashboard
* Professional Admin Panel
* Glassmorphism Design
* Dark/Light Mode
* Fully Responsive
* Beautiful Charts
* Premium Cards
* Smooth Animations
Generate:
* Complete Folder Structure
* Frontend Code
* Backend Code
* Database Schemas
* API Endpoints
* Installation Instructions
* Sample Data
The final project should look like a real commercial Student Management SaaS product ready for deployment. | 134 |
