LSE MicroBachelors Final Assessment

Course Description

This comprehensive final assessment is the culmination of the LSE MicroBachelors program in Mathematics and Statistics Fundamentals. It's a rigorous two-hour online proctored exam that evaluates your mastery of concepts, methods, and techniques covered in four essential courses: Mathematics 1: Differential calculus, Mathematics 1: Integral calculus, algebra, and applications, Statistics 1: Introductory statistics, probability and estimation, and Statistics 1: Statistical methods. This exam is your gateway to completing the program and earning your prestigious LSE MicroBachelors certificate, requiring a minimum passing score of 60%.

What Students Will Learn

Through this assessment, students will demonstrate their proficiency in a wide range of mathematical and statistical concepts. You'll showcase your ability to apply differential and integral calculus, utilize algebraic techniques, and implement various statistical methods. This exam will test your understanding of probability theory, data visualization, hypothesis testing, and regression analysis, among other crucial topics. By successfully completing this assessment, you'll prove your capability to tackle complex mathematical and statistical problems, setting yourself apart in the competitive fields of data analysis, finance, and research.

Pre-requisites

To excel in this final assessment, students should have successfully completed and passed the four courses within the LSE MicroBachelors program in Mathematics and Statistics Fundamentals. It's crucial to have thoroughly engaged with all learning materials and gained a deep understanding of the concepts, methods, and techniques introduced throughout the program. A strong foundation in basic mathematics, algebra, calculus, probability, and statistics is essential for success in this comprehensive exam.

Course Coverage

• Functions and graphs
• Derivatives and curve sketching
• Optimization techniques for single and multi-variable functions
• Integration and its applications
• Matrix algebra and linear equations
• Sequences, series, and financial modeling
• Probability theory and distributions
• Statistical estimation and hypothesis testing
• Data visualization and descriptive statistics
• Correlation and linear regression
• Sampling design and causation concepts
• Contingency tables and chi-squared tests

Who This Course Is For

This final assessment is designed for ambitious students who have completed the LSE MicroBachelors program in Mathematics and Statistics Fundamentals. It's ideal for those seeking to validate their skills in advanced mathematics and statistics, whether they're looking to enhance their current career prospects, transition into data-driven fields, or prepare for further academic pursuits. This exam is particularly suitable for individuals aiming to stand out in competitive industries such as finance, economics, data science, and research.

Real-World Applications

The skills assessed in this exam are highly valuable across numerous industries and academic fields. Graduates can apply their knowledge to:

1. Analyze complex financial models and make data-driven investment decisions
2. Conduct and interpret scientific research in various disciplines
3. Optimize business processes and improve operational efficiency
4. Develop predictive models for market trends and consumer behavior
5. Design and analyze clinical trials in the pharmaceutical industry
6. Contribute to policy-making decisions based on statistical evidence
7. Advance machine learning and artificial intelligence applications

Syllabus

1. Functions and graphs
2. The derivative
3. Curve sketching and optimization
4. Functions of two variables and partial derivatives
5. Critical points of two-variable functions
6. Integration
7. Profit maximization
8. Constrained optimization
9. Matrices, vectors, and linear equations
10. Sequences, series, and financial modeling
11. Point and interval estimation
12. Hypothesis testing I
13. Hypothesis testing II
14. Contingency tables and the chi-squared test
15. Sampling design and some ideas underlying causation
16. Correlation and linear regression
17. Mathematical revision and the nature of statistics
18. Data visualization and descriptive statistics
19. Probability theory
20. The normal distribution and ideas of sampling