9 answers
Ivy
Student
Pasadena, Maryland
1
Question
Asked
485 views
What is the recommended coursework for a computer science major who wants to enter the data science field directly after graduation?
I'm currently a rising college freshman aspiring to be a data scientist in the future. What courses should I take in college to set myself up for success?
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9 answers
Arup Sarkar
Solution Engineering
4
Answers
New York, New York
Updated
Arup’s Answer
Adding to what has already been said about courseware, google has Advanced Data Analytics certification as well. Once you wrap your head around the basics, here is the following paths in you can specialize in
I would like to break the Data Science field into 3 categories
1) Operations - Gathering data (you need huge amounts of data set, something kaggle) , feeding into a model, understanding the parameters, tune it, test it, publish it to production. So in order to take care of operations you have to understand the basics of a data science project end-to-end from inception to production.
2) Model Development - Develop statistical models to make predictions accurate as much as you can (simply put) all you need to learn here is a coding language like python or R and statistics and linear algebra. All day you develop models tune them, test it.
3) Analyzing Data - This the final piece to understand the data, data speaks and you have to break it down for people to understand in simple english.
Hope it will clear up when you do take a course and start learning, you can also do all 3.
Thank you,
I would like to break the Data Science field into 3 categories
1) Operations - Gathering data (you need huge amounts of data set, something kaggle) , feeding into a model, understanding the parameters, tune it, test it, publish it to production. So in order to take care of operations you have to understand the basics of a data science project end-to-end from inception to production.
2) Model Development - Develop statistical models to make predictions accurate as much as you can (simply put) all you need to learn here is a coding language like python or R and statistics and linear algebra. All day you develop models tune them, test it.
3) Analyzing Data - This the final piece to understand the data, data speaks and you have to break it down for people to understand in simple english.
Hope it will clear up when you do take a course and start learning, you can also do all 3.
Thank you,
Mahmoud Marayef
Software Engineer
5
Answers
Alexandria, Alexandria Governorate, Egypt
Updated
Mahmoud’s Answer
1. Introduction to Data Science: This course will introduce you to the fundamentals of data science, including data visualization, statistical analysis, and machine learning.
2. Statistics: A solid understanding of statistics is critical for a data scientist. You should take at least one course in statistics, covering topics such as probability, regression analysis, and hypothesis testing.
3. Linear Algebra: Linear algebra is important for data science, especially for machine learning. You'll want to take a course that covers matrix algebra, vector spaces, and eigenvalues/eigenvectors.
4. Database Systems: This course will introduce you to the basics of database design and implementation, including SQL, normalization, and indexing.
5. Programming Languages: You'll want to become proficient in one or more programming languages, such as Python, R, or Java. Take courses that cover data structures, algorithms, and object-oriented programming.
6. Data Mining: This course will teach you how to extract useful information from large datasets using techniques such as clustering, association rule mining, and anomaly detection.
7. Machine Learning: A course in machine learning will teach you the basics of supervised and unsupervised learning, as well as common algorithms like decision trees, neural networks, and support vector machines.
8. Big Data: You'll want to learn about the technologies and tools used to manage and process large datasets, such as Hadoop, Spark, and MapReduce.
9. Communication Skills: Being a data scientist requires strong communication skills. Take courses in technical writing and public speaking to develop your ability to communicate complex ideas clearly and persuasively.
2. Statistics: A solid understanding of statistics is critical for a data scientist. You should take at least one course in statistics, covering topics such as probability, regression analysis, and hypothesis testing.
3. Linear Algebra: Linear algebra is important for data science, especially for machine learning. You'll want to take a course that covers matrix algebra, vector spaces, and eigenvalues/eigenvectors.
4. Database Systems: This course will introduce you to the basics of database design and implementation, including SQL, normalization, and indexing.
5. Programming Languages: You'll want to become proficient in one or more programming languages, such as Python, R, or Java. Take courses that cover data structures, algorithms, and object-oriented programming.
6. Data Mining: This course will teach you how to extract useful information from large datasets using techniques such as clustering, association rule mining, and anomaly detection.
7. Machine Learning: A course in machine learning will teach you the basics of supervised and unsupervised learning, as well as common algorithms like decision trees, neural networks, and support vector machines.
8. Big Data: You'll want to learn about the technologies and tools used to manage and process large datasets, such as Hadoop, Spark, and MapReduce.
9. Communication Skills: Being a data scientist requires strong communication skills. Take courses in technical writing and public speaking to develop your ability to communicate complex ideas clearly and persuasively.
Mrinalini Singh
Project Manager
125
Answers
Bengaluru, Karnataka, India
Updated
Mrinalini’s Answer
Hey there, Ivy! It's fantastic to hear you're considering a career in Data Science. Maryland is such a charming and vibrant city, and it seems like the perfect place for you to embark on this enriching journey. There's actually an opportunity for a Transportation Data Scientist at the University of Maryland, which offers a remarkable salary of $100,000 per year.
Pursuing a postgraduate degree in Data Science could be an excellent choice, but let me also suggest a few certifications to boost your entry into the field – not just in Maryland, but globally. To assist you in exploring various areas of interest, I've gathered some essential links for free education so you can build a strong foundation in this domain. These resources can even prove helpful during job interviews! So, even without formal certification, understanding the topic will pave your way towards an exciting career in Data Science.
1. DataCamp's Understanding Data Science: https://www.datacamp.com/courses/understanding-data-science
2. Coursera's free courses on Data Science: https://www.coursera.org/courses?query=free%20courses%20data%20science
Additionally, specializing in a particular area of Data Science can boost your career prospects further. Some popular tracks include:
- Business Analytics and Intelligence
- Data Science with a Computational Track
- Data Engineering and Warehousing
- Database Management and Architecture
- Data Mining and Statistical Analysis
- Machine Learning
Remember, Ivy, the sky's the limit once you dive into the world of Data Science. Best of luck on this wonderful journey, and I'm confident you'll excel in whichever path you choose!
Pursuing a postgraduate degree in Data Science could be an excellent choice, but let me also suggest a few certifications to boost your entry into the field – not just in Maryland, but globally. To assist you in exploring various areas of interest, I've gathered some essential links for free education so you can build a strong foundation in this domain. These resources can even prove helpful during job interviews! So, even without formal certification, understanding the topic will pave your way towards an exciting career in Data Science.
1. DataCamp's Understanding Data Science: https://www.datacamp.com/courses/understanding-data-science
2. Coursera's free courses on Data Science: https://www.coursera.org/courses?query=free%20courses%20data%20science
Additionally, specializing in a particular area of Data Science can boost your career prospects further. Some popular tracks include:
- Business Analytics and Intelligence
- Data Science with a Computational Track
- Data Engineering and Warehousing
- Database Management and Architecture
- Data Mining and Statistical Analysis
- Machine Learning
Remember, Ivy, the sky's the limit once you dive into the world of Data Science. Best of luck on this wonderful journey, and I'm confident you'll excel in whichever path you choose!
Surya Vijayaraghavan
Software Engineer
1
Answer
Mountain View, California
Updated
Surya’s Answer
Hey there! It's amazing that you're interested in Data Science - it's such an exciting and rapidly growing field! There are tons of courses out there that can guide you on your journey to becoming a successful data scientist. 😊
1. **Statistics**: This essential course will teach you the basics of probability, statistics, and data analysis. It's a great starting point!
2. **Computer Science**: This area will give you a solid foundation in computer programming, including algorithms, data structures, and object-oriented programming. It's the backbone of data science!
3. **Machine Learning**: In this course, you'll learn how to build and train machine learning models. I personally took a course from https://developers.google.com/machine-learning and found it super easy to understand!
4. **Programming**: Starting with Python and R is a fantastic choice for budding data scientists. They're excellent languages to learn for this field.
5. **Data Mining**: This is a bit more advanced, so I'd recommend getting comfortable with the fundamentals before moving on to this area.
Best of luck in your data science journey! 🌟 Remember, learning is a journey, and you're going to do great!
1. **Statistics**: This essential course will teach you the basics of probability, statistics, and data analysis. It's a great starting point!
2. **Computer Science**: This area will give you a solid foundation in computer programming, including algorithms, data structures, and object-oriented programming. It's the backbone of data science!
3. **Machine Learning**: In this course, you'll learn how to build and train machine learning models. I personally took a course from https://developers.google.com/machine-learning and found it super easy to understand!
4. **Programming**: Starting with Python and R is a fantastic choice for budding data scientists. They're excellent languages to learn for this field.
5. **Data Mining**: This is a bit more advanced, so I'd recommend getting comfortable with the fundamentals before moving on to this area.
Best of luck in your data science journey! 🌟 Remember, learning is a journey, and you're going to do great!
Mobeen Siddiqui
Architect
6
Answers
Dallas, Texas
Updated
Mobeen’s Answer
Hey there! I'd be happy to help you learn more about data science, as it's definitely an interesting field with a lot of opportunities. It's a skill set that combines statistics, programming languages, and the ability to manipulate and wrangle data for things like feature engineering, which helps prepare your data for models.
To begin, I suggest starting with a simple programming language, like R or Python. While learning, you'll discover various models that can be used to solve different types of problems. Keep in mind that, just like in any other field, there isn't always a one-size-fits-all solution. Reading up on different statistical models will help you understand when to use which one – for instance, when to use regression models vs. classification models.
Once you feel comfortable with the basic theories, a great resource to explore is www.kaggle.com. It's an awesome community for data scientists, and you can learn a lot by simply browsing the content there. By copying and running existing code with existing models and testing data, you'll gain enough experience to move on to the next stage of learning even more about data science techniques. So dive in and enjoy the journey!
To begin, I suggest starting with a simple programming language, like R or Python. While learning, you'll discover various models that can be used to solve different types of problems. Keep in mind that, just like in any other field, there isn't always a one-size-fits-all solution. Reading up on different statistical models will help you understand when to use which one – for instance, when to use regression models vs. classification models.
Once you feel comfortable with the basic theories, a great resource to explore is www.kaggle.com. It's an awesome community for data scientists, and you can learn a lot by simply browsing the content there. By copying and running existing code with existing models and testing data, you'll gain enough experience to move on to the next stage of learning even more about data science techniques. So dive in and enjoy the journey!
Sushanta Saha
Database Administration
11
Answers
Alpharetta, Georgia
Updated
Sushanta’s Answer
Computer languages: Python, and R
Math: As many you can complete.
Statistics: As many you can complete.
Good luck!
Math: As many you can complete.
Statistics: As many you can complete.
Good luck!
Rafeh Hussain
Engineering
15
Answers
Binghamton, New York
Updated
Rafeh’s Answer
To develop a comprehensive understanding of data science, you need to delve into several key areas, including statistics, machine learning, deep learning, research papers, and data visualization.
1. Statistics: A fundamental pillar of data science, statistics provides the framework for making data-driven decisions. You should familiarize yourself with concepts like probability, distributions, hypothesis testing, regression, maximum likelihood estimation, Bayesian statistics, and experiment design. This knowledge equips you to analyze, interpret, and make predictions based on various types of data.
2. Machine Learning: This branch of artificial intelligence allows computers to learn and improve from experience without explicit programming. Develop a strong foundation in machine learning algorithms, including supervised and unsupervised learning methods, such as linear regression, decision trees, clustering, and dimensionality reduction. Additionally, gain experience working with machine learning libraries like scikit-learn in Python.
3. Deep Learning: While this is a subset of machine learning, deep learning focuses more on artificial neural networks, especially those known as deep neural networks. These models have proven to be highly effective in tasks like image and speech recognition, natural language processing, and even playing games. Familiarize yourself with deep learning libraries and frameworks like TensorFlow, Keras and PyTorch, and explore advanced topics like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
4. Read Research Papers: To stay updated and comprehend the latest advancements in the field, reading research papers is vital. Some good sources include journals like the Journal of Machine Learning Research, conferences like Neural Information Processing Systems (NeurIPS), and preprint repositories like arXiv. By engaging in these resources, you can develop a deeper understanding of the trends, techniques, and state-of-the-art solutions in data science.
5. Data Visualization: The ability to effectively present data through visual representations is essential in data science. Developing skills in data visualization helps you to communicate insights and results to stakeholders clearly and intuitively. Gain proficiency in visualization libraries like Matplotlib, Seaborn, and ggplot2, as well as advanced tools like Tableau and D3.js.
In summary, mastering these crucial areas – statistics, machine learning, deep learning, research papers, and data visualization – will provide you with a well-rounded and comprehensive understanding of data science, ultimately empowering you to create effective data-driven solutions.
1. Statistics: A fundamental pillar of data science, statistics provides the framework for making data-driven decisions. You should familiarize yourself with concepts like probability, distributions, hypothesis testing, regression, maximum likelihood estimation, Bayesian statistics, and experiment design. This knowledge equips you to analyze, interpret, and make predictions based on various types of data.
2. Machine Learning: This branch of artificial intelligence allows computers to learn and improve from experience without explicit programming. Develop a strong foundation in machine learning algorithms, including supervised and unsupervised learning methods, such as linear regression, decision trees, clustering, and dimensionality reduction. Additionally, gain experience working with machine learning libraries like scikit-learn in Python.
3. Deep Learning: While this is a subset of machine learning, deep learning focuses more on artificial neural networks, especially those known as deep neural networks. These models have proven to be highly effective in tasks like image and speech recognition, natural language processing, and even playing games. Familiarize yourself with deep learning libraries and frameworks like TensorFlow, Keras and PyTorch, and explore advanced topics like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
4. Read Research Papers: To stay updated and comprehend the latest advancements in the field, reading research papers is vital. Some good sources include journals like the Journal of Machine Learning Research, conferences like Neural Information Processing Systems (NeurIPS), and preprint repositories like arXiv. By engaging in these resources, you can develop a deeper understanding of the trends, techniques, and state-of-the-art solutions in data science.
5. Data Visualization: The ability to effectively present data through visual representations is essential in data science. Developing skills in data visualization helps you to communicate insights and results to stakeholders clearly and intuitively. Gain proficiency in visualization libraries like Matplotlib, Seaborn, and ggplot2, as well as advanced tools like Tableau and D3.js.
In summary, mastering these crucial areas – statistics, machine learning, deep learning, research papers, and data visualization – will provide you with a well-rounded and comprehensive understanding of data science, ultimately empowering you to create effective data-driven solutions.
Jaspreet Bakshi
Technical Program manager
1
Answer
Pleasanton, California
Updated
Jaspreet’s Answer
Hey there! 😊
It's fantastic to hear that you're thinking about diving into the world of Data Science - it's an exciting field with lots of career potential! Before anything else, let's make sure you've got the basics down, like SQL, and languages such as Python, R, and TensorFlow.
Here's a list of awesome courses to help you get started:
1. Johns Hopkins University's Data Science Specialization on Coursera: https://www.coursera.org/specializations/jhu-data-science
2. Deep learning Specialization: https://www.coursera.org/specializations/deep-learning
3. UC Davis SQL for Data Science: https://www.coursera.org/specializations/learn-sql-basics-data-science
4. Data Science Foundations using R, also by Johns Hopkins University: https://www.coursera.org/specializations/data-science-foundations-r
5. Applied Data Science with Python by the University of Michigan: https://www.coursera.org/specializations/data-science-python
For even more great Data Science courses, be sure to check out this collection on Coursera: https://www.coursera.org/collections/popular-data-science-courses
Wishing you the very best of luck on your learning journey! 😄
-Jaspreet
It's fantastic to hear that you're thinking about diving into the world of Data Science - it's an exciting field with lots of career potential! Before anything else, let's make sure you've got the basics down, like SQL, and languages such as Python, R, and TensorFlow.
Here's a list of awesome courses to help you get started:
1. Johns Hopkins University's Data Science Specialization on Coursera: https://www.coursera.org/specializations/jhu-data-science
2. Deep learning Specialization: https://www.coursera.org/specializations/deep-learning
3. UC Davis SQL for Data Science: https://www.coursera.org/specializations/learn-sql-basics-data-science
4. Data Science Foundations using R, also by Johns Hopkins University: https://www.coursera.org/specializations/data-science-foundations-r
5. Applied Data Science with Python by the University of Michigan: https://www.coursera.org/specializations/data-science-python
For even more great Data Science courses, be sure to check out this collection on Coursera: https://www.coursera.org/collections/popular-data-science-courses
Wishing you the very best of luck on your learning journey! 😄
-Jaspreet
Jon Tetzloff
Hardware data analysis and yield improvement
11
Answers
Rochester, Minnesota
Updated
Jon’s Answer
Agree with the above answer but will emphasize the Communication Skills part. As a data scientist you will be required to convince others of your findings...think of telling stories with your data. Public speaking, presentation skills, and effective chart building will be at least as important as the technical work.
Good luck!
Good luck!