Description

Overview:

Welcome to “Statistics & Probability for Data Science & Machine Learning!” This course provides a comprehensive introduction to statistics and probability concepts essential for data science and machine learning. Understanding statistics and probability is crucial for analyzing data, making predictions, and building machine learning models. In this course, you’ll learn key statistical techniques, probability distributions, and their applications in data analysis, inference, and predictive modeling using real-world datasets.

Main Course Features:

• Thorough coverage of fundamental statistical concepts, including descriptive and inferential statistics
• Exploration of probability theory, including probability distributions and random variables
• Hands-on tutorials and coding exercises using Python for statistical analysis and modeling
• Practical examples and case studies from various domains, including finance, healthcare, and marketing
• Guidance on data preprocessing, feature engineering, and model evaluation techniques
• Access to datasets and resources for practicing statistical analysis and machine learning
• Supportive online community for collaboration and assistance throughout the course
• Regular assessments and quizzes to track progress and reinforce learning

Who Should Take This Course:

• Aspiring data scientists and machine learning engineers seeking a strong foundation in statistics and probability
• Students pursuing degrees in data science, computer science, or related fields
• Professionals in analytics, business intelligence, and data-driven decision-making roles
• Anyone interested in learning statistical concepts and their applications in data science and machine learning

Learning Outcomes:

• Understand fundamental statistical concepts and probability theory for data analysis and inference
• Gain proficiency in using Python libraries such as NumPy, Pandas, and Matplotlib for statistical analysis and visualization
• Apply statistical techniques for hypothesis testing, regression analysis, and predictive modeling
• Interpret and analyze data distributions, correlations, and relationships
• Build and evaluate machine learning models using statistical principles
• Develop critical thinking and problem-solving skills through hands-on coding exercises
• Create insightful data visualizations to communicate findings effectively
• Apply statistical and probabilistic concepts to real-world datasets and machine learning projects.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Assessment

At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.