Discrete Math for Computer Science - Algorithms & Recursion

Colleagues, the “Discrete Math for Computer Science - Algorithms & Recursion” focuses on the mathematical foundations behind algorithms, efficiency, and recursive problem solving, building on the logic and counting techniques developed in earlier courses. It introduces key ideas from number theory and shows how they naturally lead to efficient algorithms used throughout computer science. The course begins with modular arithmetic, divisibility, and greatest common divisors, leading to classic algorithms such as the Euclidean algorithm and its extended form. These concepts are then applied to practical problems in cryptography, including modular exponentiation, key exchange, and public-key encryption, illustrating how abstract mathematics enables secure communication. You will learn to: Analyse algorithm efficiency using asymptotic growth and mathematical reasoning. Apply number theory concepts to algorithms and basic cryptographic systems. Design and reason about recursive algorithms using induction and recurrence relations. Gain high-demand skills: Computational Thinking, Key Management, Encryption, Applied Mathematics, Mathematical Theory and Analysis, Algorithms, Theoretical Computer Science, Combinatorics, Arithmetic, and Cryptography. Training lessons address: 1) Modular Arithmetic, 2) Greatest Common Divisor, 3) Cryptography, 4) Algorithms, 5) Induction, and 6) Recursion.

Enroll today!: Teams and executives are welcome: https://imp.i384100.net/k4PG0n 

Recommended Readings: The “Data-Driven Organizations” series.

1 - The Promise of Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much career success from the Data Science Academy (please subscribe and share with your colleagues)