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.
Interactive video lectures by industry experts
Instant e-certificate and hard copy dispatch by next working day
Fully online, interactive course with Professional voice-over
Developed by qualified first aid professionals
Self paced learning and laptop, tablet, smartphone friendly
24/7 Learning Assistance
Discounts on bulk purchases
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. Also, you can have your printed certificate delivered by post (shipping cost £3.99). 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.
Curriculum
Section 01: Let’s get started
Section 02: Descriptive statistics
Section 03: Distributions
Section 04: Probability theory
Section 05: Hypothesis testing
Section 06: Regressions
Section 07: Advanced regression & machine learning algorithms
Section 08: ANOVA (Analysis of Variance)
Section 09: Wrap up
