Data Science and Machine Learning: Mathematical and Statistical Methods Course Overview
The 'Data Science and Machine Learning: Mathematical and Statistical Methods Course' from Open Source equips learners with essential quantitative skills for data analysis and model development. This course is crucial for data scientists, analysts, and professionals in technology and business sectors, enhancing their ability to harness data-driven insights effectively for informed decision-making and innovative solutions.
Course outline & what you'll learn
Overview of Data Science
- Importance of Mathematical and Statistical Methods
- Applications in Real-World Scenarios
- Descriptive Statistics
- Inferential Statistics
- Probability Distributions
- Data Cleaning and Transformation
- Handling Missing Values
- Feature Scaling and Normalization
- Visualization Techniques
- Summary Statistics
- Identifying Trends and Patterns
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement
- Model Evaluation Metrics
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines
- Clustering Methods: K-Means, Hierarchical Clustering
- Dimensionality Reduction: PCA, t-SNE
- Cross-Validation Techniques
- Hyperparameter Tuning
- Avoiding Overfitting and Underfitting
- Ensemble Methods
- Neural Networks and Deep Learning
- Natural Language Processing
- Bias and Fairness in Machine Learning
- Privacy and Data Protection
- Real-World Data Science Project
- Presentation of Findings and Insights
- Peer Review and Feedback
Why train with Traincrest
This Open Source course is delivered by Traincrest's certified instructors, live online or in the classroom, with hands-on labs and a 98% exam success rate. Trusted by 500+ companies and 50,000+ students worldwide.