Overall Duration : 27:45 Hours DOWNLOAD Size : 15 GB (7 PART DOWNLOAD) Course content Part 1: Introduction The Field of Data Science - The Various Data Science Disciplines The Field of Data Science - Connecting the Data Science Disciplines The Field of Data Science - The Benefits of Each Discipline The Field of Data Science - Popular Data Science Techniques The Field of Data Science - Popular Data Science Tools The Field of Data Science - Careers in Data Science The Field of Data Science - Debunking Common Misconceptions Part 2: Probability Probability - Combinatorics Probability - Bayesian Inference Probability - Distributions Probability - Probability in Other Fields Part 3: Statistics Statistics - Descriptive Statistics Statistics - Practical Example: Descriptive Statistics Statistics - Inferential Statistics Fundamentals Statistics - Inferential Statistics: Confidence Intervals Statistics - Practical Example: Inferential Statistics Statistics - Hypothesis Testing Statistics - Practical Example: Hypothesis Testing Part 4: Introduction to Python Python - Variables and Data Types Python - Basic Python Syntax Python - Other Python Operators Python - Conditional Statements Python - Python Functions Python - Sequences Python - Iterations Python - Advanced Python Tools Part 5: Advanced Statistical Methods in Python Advanced Statistical Methods - Linear regression with StatsModels Advanced Statistical Methods - Multiple Linear Regression with StatsModels Advanced Statistical Methods - Linear Regression with sklearn Advanced Statistical Methods - Practical Example: Linear Regression Advanced Statistical Methods - Logistic Regression Advanced Statistical Methods - Cluster Analysis Advanced Statistical Methods - K-Means Clustering Advanced Statistical Methods - Other Types of Clustering Part 6: Mathematics Part 7: Deep Learning Deep Learning - Introduction to Neural Networks Deep Learning - How to Build a Neural Network from Scratch with NumPy Deep Learning - TensorFlow 2.0: Introduction Deep Learning - Digging Deeper into NNs: Introducing Deep Neural Networks Deep Learning - Overfitting Deep Learning - Initialization Deep Learning - Digging into Gradient Descent and Learning Rate Schedules Deep Learning - Preprocessing Deep Learning - Classifying on the MNIST Dataset Deep Learning - Business Case Example Deep Learning - Conclusion Appendix: Deep Learning - TensorFlow 1: Introduction Appendix: Deep Learning - TensorFlow 1: Classifying on the MNIST Dataset Appendix: Deep Learning - TensorFlow 1: Business Case Software Integration Case Study - What's Next in the Course? Case Study - Preprocessing the 'Absenteeism_data' Case Study - Applying Machine Learning to Create the 'absenteeism_module' Case Study - Loading the 'absenteeism_module' Case Study - Analyzing the Predicted Outputs in Tableau
