Free Certification Courses to Learn Data Analytics in 2025:

1. Python
🔗 https://imp.i384100.net/5gmXXo

2. SQL
🔗 https://edx.org/learn/relational-databases/stanford-university-databases-relational-databases-and-sql

3. Statistics and R
🔗 https://edx.org/learn/r-programming/harvard-university-statistics-and-r

4. Data Science: R Basics
🔗https://edx.org/learn/r-programming/harvard-university-data-science-r-basics

5. Excel and PowerBI
🔗 https://learn.microsoft.com/en-gb/training/paths/modern-analytics/

6. Data Science: Visualization
🔗https://edx.org/learn/data-visualization/harvard-university-data-science-visualization

7. Data Science: Machine Learning
🔗https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning

8. R
🔗https://imp.i384100.net/rQqomy

9. Tableau
🔗https://imp.i384100.net/MmW9b3

10. PowerBI
🔗 https://lnkd.in/dpmnthEA

11. Data Science: Productivity Tools
🔗 https://lnkd.in/dGhPYg6N

12. Data Science: Probability
🔗https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science

13. Mathematics
🔗https://matlabacademy.mathworks.com

14. Statistics
🔗 https://lnkd.in/df6qksMB

15. Data Visualization
🔗https://imp.i384100.net/k0X6vx

16. Machine Learning
🔗 https://imp.i384100.net/nLbkN9

17. Deep Learning
🔗 https://imp.i384100.net/R5aPOR

18. Data Science: Linear Regression
🔗https://pll.harvard.edu/course/data-science-linear-regression/2023-10

19. Data Science: Wrangling
🔗https://edx.org/learn/data-science/harvard-university-data-science-wrangling

20. Linear Algebra
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra

21. Probability
🔗 https://pll.harvard.edu/course/data-science-probability

22. Introduction to Linear Models and Matrix Algebra
🔗https://edx.org/learn/linear-algebra/harvard-university-introduction-to-linear-models-and-matrix-algebra

23. Data Science: Capstone
🔗 https://edx.org/learn/data-science/harvard-university-data-science-capstone

24. Data Analysis
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis

25. IBM Data Science Professional Certificate
https://imp.i384100.net/9gxbbY

26. Neural Networks and Deep Learning
https://imp.i384100.net/DKrLn2

27. Supervised Machine Learning: Regression and Classification
https://imp.i384100.net/g1KJEA

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## 🔹 Advanced Widget Customization
### 1. Custom Delegate for Table Views

class ProgressBarDelegate(QStyledItemDelegate):
    def paint(self, painter, option, index):
        progress = index.data(Qt.DisplayRole)
        
        # Draw background
        painter.save()
        painter.setPen(Qt.NoPen)
        painter.setBrush(QColor("#e0e0e0"))
        painter.drawRect(option.rect)
        
        # Draw progress
        if progress > 0:
            width = option.rect.width() * progress / 100
            progress_rect = QRectF(option.rect)
            progress_rect.setWidth(width)
            
            gradient = QLinearGradient(progress_rect.topLeft(), progress_rect.topRight())
            gradient.setColorAt(0, QColor("#4CAF50"))
            gradient.setColorAt(1, QColor("#2E7D32"))
            
            painter.setBrush(QBrush(gradient))
            painter.drawRect(progress_rect)
        
        # Draw text
        painter.setPen(QColor("#333"))
        painter.drawText(option.rect, Qt.AlignCenter, f"{progress}%")
        painter.restore()

# Usage
table = QTableView()
table.setItemDelegateForColumn(2, ProgressBarDelegate())

### 2. Custom Widget with QPainter

class GaugeWidget(QWidget):
    def __init__(self, parent=None):
        super().__init__(parent)
        self.value = 0
        self.min_value = 0
        self.max_value = 100
    
    def set_value(self, value):
        self.value = max(self.min_value, min(value, self.max_value))
        self.update()
    
    def paintEvent(self, event):
        painter = QPainter(self)
        painter.setRenderHint(QPainter.Antialiasing)
        
        # Draw background
        rect = self.rect().adjusted(5, 5, -5, -5)
        painter.setPen(QPen(QColor("#333"), 2)
        painter.setBrush(QColor("#f5f5f5"))
        painter.drawEllipse(rect)
        
        # Draw gauge
        angle = 180 * (self.value - self.min_value) / (self.max_value - self.min_value)
        span_angle = -angle * 16  # 1/16th of a degree
        
        pen = QPen(QColor("#FF5722"), 10)
        pen.setCapStyle(Qt.RoundCap)
        painter.setPen(pen)
        
        painter.drawArc(rect, 180 * 16, span_angle)
        
        # Draw text
        font = painter.font()
        font.setPointSize(20)
        painter.setFont(font)
        painter.drawText(rect, Qt.AlignCenter, f"{self.value}%")

---

## 🔹 Best Practices
1. Separate database logic from UI code
2. Use transactions for batch database operations
3. Optimize chart performance with limited data points
4. Follow MVC pattern for complex applications
5. Document custom widgets thoroughly

---

### 📌 What's Next?
In Part 5, we'll cover:
➡️ Networking & Web APIs
➡️ Multimedia Applications
➡️ Internationalization
➡️ Deployment & Packaging

#PyQt5 #Database #DataVisualization 🚀

Practice Exercise:
1. Build a sales dashboard with database-backed charts
2. Create a custom weather widget with API data
3. Implement an MVC-based inventory management system