The Data Science Course: Complete Data Science Bootcamp
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The Data Science Course: Complete Data Science Bootcamp at UDEMY Overview
Duration | 32 hours |
Total fee | ₹12,800 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Future job roles | Account Planning, .Net, Black Box Testing, Assistant Vice President - IT Knowledge Banking , E Commerce Analyst |
The Data Science Course: Complete Data Science Bootcamp at UDEMY Highlights
- Rich Learning Content
- 100% Online Course
- Course Instructor : 365 Careers
The Data Science Course: Complete Data Science Bootcamp at UDEMY Course details
- You should take this course if you want to become a Data Scientist or if you want to learn about the field
- This course is for you if you want a great career
- The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
- Start coding in Python and learn how to use it for statistical analysis
- Apply your skills to real-life business cases
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
- Understand the mathematics behind Machine Learning (an absolute must which other courses don�???�??¢??t teach!)
- Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
- Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.
The Data Science Course: Complete Data Science Bootcamp at UDEMY Curriculum
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
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