Practical Data Science using Python

🚀 Unlock the Secrets of Data Science with Python! 📊

Course Title: Practical Data Science using Python

Course Headline: Apply Data Science using Python, Statistical Techniques, EDA, Numpy, Pandas, Scikit Learn, Statsmodel Libraries

🎓 Are you aspiring to become a Data Scientist or Machine Learning Engineer? If your answer is a resounding "Yes!" then this course is precisely tailored for you. Dive into the world of Data Science with Python and master the art of transforming raw data into actionable insights and predictive models.

What You Will Learn:

• Core Concepts of Data Science: Understand the fundamental principles that drive Data Science projects.
• Exploratory Data Analysis (EDA): Learn to navigate and analyze datasets using Python, Numpy, and Pandas libraries.
• Statistical Methods: Gain a solid grasp of statistical techniques that are crucial for any Data Science project.
• Python Language Mastery: Become proficient in Python as you learn its syntax, structure, and data manipulation capabilities.
• Bias, Variance, and Overfitting: Grasp the challenges and learn how to address them effectively.
• Performance Metrics: Choose the right metrics to evaluate your models accurately.
• Model Evaluation Techniques: Master model evaluation through cross-validation, performance metrics, and more.
• Hyperparameter Tuning & Grid Search Cross Validation: Optimize your models with advanced tuning techniques.

Hands-On Learning Experience:

This course is designed to be highly interactive, with a focus on practical, hands-on projects and examples that will take you through:

• Exploratory Data Analysis: Work on real-world datasets and learn how to extract meaningful patterns.
• Model Development: Build predictive models using a variety of algorithms and understand their use cases.
• Model Optimization: Enhance your models through hyperparameter tuning and grid search cross-validation.
• Visualization Techniques: Master data visualization with Marplotlib and Seaborn libraries to present your findings effectively.

Course Highlights:

• Comprehensive Python for Data Science & ML: A detailed exploration of Python as it relates to Data Science and Machine Learning.
• Deep Dive into Numpy and Pandas: Learn the ins and outs of these indispensable libraries for data manipulation and analysis.
• Data Visualization with Marplotlib and Seaborn: Create compelling visualizations that reveal the stories within your data.
• Introduction to Deep Neural Networks: Get an overview of deep learning with a hands-on example using TensorFlow and Keras.
• Case Studies: Real-world applications in House Price Prediction and Credit Card Fraud Detection to understand practical use cases.

Course Breakdown:

1. Introduction to Data Science
2. Use Cases and Methodologies
3. Role of Data in Data Science
4. Statistical Methods
5. Exploratory Data Analysis (EDA)
6. Understanding the Process of Training or Learning
7. Understanding Validation and Testing
8. Python Language in Detail
9. Setting up your DS/ML Development Environment
10. Python Internal Data Structures
11. Python Language Elements
12. Pandas Data Structure – Series and DataFrames
13. Exploratory Data Analysis (EDA) - A Deeper Dive
14. Learning Linear Regression Model using the House Price Prediction case study
15. Learning Logistic Model using the Credit Card Fraud Detection case study
16. Evaluating your model performance
17. Fine Tuning your model
18. Hyperparameter Tuning for Optimizing our Models
19. Cross-Validation Technique
20. Learning SVM through an Image Classification project
21. Understanding Decision Trees
22. Understanding Ensemble Techniques using Random Forest
23. Dimensionality Reduction using PCA
24. K-Means Clustering with Customer Segmentation
25. Introduction to Deep Learning
26. Bonus Module: Time Series Prediction using ARIMA

Embark on your journey to becoming a proficient Data Scientist or Machine Learning Engineer today! With this comprehensive course, you'll have the tools and knowledge to analyze data with confidence and build predictive models that can make a real-world impact. 🌟