Computational Thinking for Data Science Course

Course Description

Are you ready to unlock the power of computational thinking in the world of data science? This exciting course, part of the prestigious Big Data MicroMasters program, offers a comprehensive journey into the realm of computational thinking and its applications in data science. You'll dive deep into core concepts such as decomposition, pattern recognition, abstraction, and algorithmic thinking, equipping yourself with invaluable skills that are in high demand across every industry.

What students will learn from the course

• Advanced core computational thinking concepts and their application to large-scale data sets
• Industry-level tools for data preparation and visualization, including R and Java
• Methods for data preparation and analysis of large data sets
• Mathematical and statistical techniques for extracting information from big data and identifying relationships between data sets
• Data representation, cleaning, presentation, and visualization techniques
• Development of data-driven problem design skills and algorithms for big data
• Mathematical representations, probabilistic and statistical models, dimension reduction, and Bayesian models

Pre-requisites or skills necessary to complete the course

This course is designed as an introductory level program, and there are no specific prerequisites listed. However, a basic understanding of mathematics and computer science concepts would be beneficial.

What the course will cover

• Core computational thinking concepts (decomposition, pattern recognition, abstraction, algorithmic thinking)
• Data representation and analysis
• Data cleaning, presentation, and visualization techniques
• Data-driven problem design and algorithms for big data
• Mathematical representations and statistical models
• Dimension reduction and Bayesian models
• R and Java data processing libraries
• Data manipulation and joining techniques
• Transformation of data for modeling purposes
• Probability and statistical concepts
• Graph theory and basic algorithms
• Hashing and hash functions

Who this course is for

This course is ideal for:

1. Aspiring data scientists and analysts
2. Computer science students looking to specialize in big data
3. Professionals seeking to enhance their computational thinking skills
4. Anyone interested in learning how to effectively process and analyze large datasets

How learners can use these skills in the real world

The skills acquired in this course have numerous real-world applications:

1. Developing efficient algorithms for processing large datasets in various industries
2. Creating data visualization tools for business intelligence
3. Implementing predictive models for financial forecasting or market analysis
4. Optimizing business processes through data-driven decision making
5. Conducting scientific research that involves complex data analysis
6. Designing and implementing big data solutions for companies across various sectors

Syllabus

1. Data in R
2. Visualizing relationships
3. Manipulating and joining data
4. Transforming data and dimension reduction
5. Summarizing data
6. Introduction to Java
7. Graphs
8. Probability
9. Hashing
10. Bringing it all together

This comprehensive course will transform you into a computational thinking expert, ready to tackle the challenges of big data in any field. Don't miss this opportunity to gain cutting-edge skills that will set you apart in the job market and empower you to solve complex problems across industries!