Complete Data Science Bootcamp

Course Code: IT87

Course Objective:

• Learn how to pre-process data
• Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
• Start coding in Python and learn how to use it for statistical analysis
• Perform linear and logistic regressions in Python
• Carry out cluster and factor analysis
• Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
• Apply your skills to real-life business cases
• Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlow Develop a business intuition while coding and solving tasks with big data
• Unfold the power of deep neural networks

Target Audience:

• System analyst
• System admin
• Developer
• Software designer

Course Outline

1. Introduction

• The Various Data Science Disciplines
• Connecting the Data Science Discipli
• The Benefits of Each Discipline
• Popular Data Science Techniques
• Popular Data Science Tools
• Careers in Data ScienceDebunking Common Misconceptions

1. Probability

• Probability – Combinatorics
• Bayesian Inference
• Distributions
• Probability in Other Fields

1.  Statistics

• Descriptive Statistics
• Practical Example: Descriptive Statistics
• Inferential Statistics Fundamentals
• Inferential Statistics: Confidence Intervals
• Practical Example: Inferential Statistics
• Hypothesis Testing
• Practical Example: Hypothesis Testing

1. Python

• Variables and Data Types
• Basic Python Syntax
• Other Python Operators
• Conditional Statements
• Python Functions
• Sequences
• Iterations
• Advanced Python Tools

1. Advanced Statistical Methods in Python

• Linear Regression with StatsModels
• Multiple Linear Regression with StatsModels
• Linear Regression with sklearn
•  Practical Example: Linear Regression
•  Logistic Regression
• Cluster Analysis
•  K-Means Clustering
• Other Types of Clustering

1. Mathematics
2. Deep Learning

• Neural Networks
• How to Build a Neural Network from Scratch with NumPy
• TensorFlow 2.0: Introduction
•  Digging Deeper into NNs: Introducing Deep Neural Networks
•  Overfitting
• Initialization
• Digging into Gradient Descent and Learning Rate Schedules
•  Preprocessing
• Classifying on the MNIST Dataset
• Business Cass Example
• Conclusion

1. Appendix: Deep Learning - TensorFlow 1

• 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
• Appendix - Additional Python Tools
• Appendix-pandas Fundamentals
• Appendix-Working with Text Files in Python

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Register for this course

16 October 2022  (10 days - Dubai) 30 October 2022  (10 days - Istanbul) 27 November 2022  (10 days - Kuala Lumpur) 04 December 2022  (10 days - Cairo)
Enroll