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How to connect computer science with data science?
I'm studying computer science as my major, but I have been interested in data science and data analytics. I was wondering what career paths or integrations I can do with programming and other skills related to computer science.
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7 answers
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Mahmoud’s Answer
Software engineer with data focus: You can work as a software engineer with a focus on data-related projects. For example, you can work on developing applications that process large datasets, build data visualization tools, or develop data-driven features for existing products.
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Kamlesh’s Answer
As previously discussed, Data Science is a specific area within the field of Computer Science. As a Data Scientist, your main goal is to concentrate on analyzing data, effectively utilizing data in reports, conducting statistical analysis, and working with Artificial Intelligence (such as machine learning). Additionally, you may be involved in refining data for improved presentation, handling data transformations, and other related tasks.
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Dan’s Answer
My response aligns closely with the previous ones - you're definitely on the right track! Just make sure to concentrate on data science-related courses towards the end of your degree. If you're truly passionate about this field, think about pursuing a graduate degree in data science. I've heard that Berkeley offers an online master's program in data science, and I'm confident there are plenty of other similar opportunities out there. Keep up the great work!
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Juan Sebastian’s Answer
What are some career options or ways to combine programming with other computer science skills?
A: As mentioned in previous answers, Computer Science and Data Analytics naturally overlap at some point. Adding Marketing Skills to the mix is like icing on the cake, as it helps you translate technical aspects into business terms.
In high school, I was really into computer science and the IT world, so I learned C++, Visual Basic, HTML, VBA, and more. To make a long story short, I eventually chose to study Business Analytics. Knowing the capabilities of each programming language has helped me work better with technology developers to create tech solutions that improve the data and reporting aspects of my job.
If you can include basic coursework in data analysis (and maybe even earn a certificate) and marketing, your communication and interactions will be much smoother and ultimately more beneficial for the business.
A: As mentioned in previous answers, Computer Science and Data Analytics naturally overlap at some point. Adding Marketing Skills to the mix is like icing on the cake, as it helps you translate technical aspects into business terms.
In high school, I was really into computer science and the IT world, so I learned C++, Visual Basic, HTML, VBA, and more. To make a long story short, I eventually chose to study Business Analytics. Knowing the capabilities of each programming language has helped me work better with technology developers to create tech solutions that improve the data and reporting aspects of my job.
If you can include basic coursework in data analysis (and maybe even earn a certificate) and marketing, your communication and interactions will be much smoother and ultimately more beneficial for the business.
Updated
Sanaz’s Answer
If you're studying computer science but interested in data science and analytics, there are several ways to combine the two fields:
1. Data Analysis and Visualization: Use programming skills to analyze large datasets, apply algorithms and machine learning models, and create visualizations to communicate insights.
2. Machine Learning and AI: Apply programming knowledge to build machine learning models for tasks like natural language processing, computer vision, and recommendation systems.
3. Big Data Processing: Learn tools like Apache Hadoop and Spark to handle and process massive amounts of data efficiently.
4. Data Engineering: Combine computer science and data management techniques to design and build robust data pipelines.
5. Data Science in Software Development: Incorporate data science techniques into software development to improve user experiences and system performance. We use this today to run large scale a/b tests & quantify the impact of changes to the user experience across our software & websites.
6. Research and Development: Contribute to advancing the field by researching new algorithms and models.
7. Data-Driven Product Development: Join product teams to develop data-driven products or services.
8. Learning Python or R and gaining knowledge in statistics and machine learning concepts will be helpful. Pursue relevant courses, certifications, and projects to enhance your skills. This combination of expertise will open up opportunities like data scientist, machine learning engineer, or data analyst in various industries.
1. Data Analysis and Visualization: Use programming skills to analyze large datasets, apply algorithms and machine learning models, and create visualizations to communicate insights.
2. Machine Learning and AI: Apply programming knowledge to build machine learning models for tasks like natural language processing, computer vision, and recommendation systems.
3. Big Data Processing: Learn tools like Apache Hadoop and Spark to handle and process massive amounts of data efficiently.
4. Data Engineering: Combine computer science and data management techniques to design and build robust data pipelines.
5. Data Science in Software Development: Incorporate data science techniques into software development to improve user experiences and system performance. We use this today to run large scale a/b tests & quantify the impact of changes to the user experience across our software & websites.
6. Research and Development: Contribute to advancing the field by researching new algorithms and models.
7. Data-Driven Product Development: Join product teams to develop data-driven products or services.
8. Learning Python or R and gaining knowledge in statistics and machine learning concepts will be helpful. Pursue relevant courses, certifications, and projects to enhance your skills. This combination of expertise will open up opportunities like data scientist, machine learning engineer, or data analyst in various industries.
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Christina’s Answer
Foundational Computer Science skills is in demand in the data science field. Being able to optimize code and switch code implementations between Python, Go or SQL is invaluable in reducing the cost to run the code. Companies have budgets for the servers and complex Machine learning models or packages will expend those budgets quickly. An AI Algorithm Engineer is a field in data science that requires great computer science knowledge and C++ / C# skills at a software engineer level. Most data science jobs do not.
Here are two excellent Python resources:
realpython.com
https://runestone.academy/ns/books/published/pythonds/index.html
I would start working on data science open source projects or competitions sites such as kaggle.com or hackathon.com.
Lastly, start listening to conference videos on YouTube such a TWIML , Pycon, OSDC etc.
Here are two excellent Python resources:
realpython.com
https://runestone.academy/ns/books/published/pythonds/index.html
I would start working on data science open source projects or competitions sites such as kaggle.com or hackathon.com.
Lastly, start listening to conference videos on YouTube such a TWIML , Pycon, OSDC etc.
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Cole’s Answer
A background in computer science is a great way to get a start in data science. In my time applying to data science/ analytics related roles, I found that job descriptions tend to have a great focus on technical backgrounds (CS, STEM, etc). One of the major differences between something like software engineering and data science is that the former focuses on developing systems and applications, while the latter focuses on utilizing these systems to derive insight from the data collected in order to help businesses make better decisions. These two disciplines have a lot of overlap, and if you are looking for a good bridge between the two its worth considering something like cloud engineering. This gives you the opportunity to write code for custom integrations of cloud based services, while also giving you exposure to analytical systems used by data scientists. Roles in this field can vary quite a bit (even if they have the same job title), so your mileage may vary. However, I think its definitely worth looking into because there is certainly a demand for this type of work in the job market, and it will provide you with a great platform to move into pure data science or other similar positions that you may be interested in as well.