11 answers
Tasawar
Student
Lahore, Punjab, Pakistan
3
Questions
Asked
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what resources do i need to learn data science and artificial intelligence?
i am an undergrad software engineering student trying to pursue a career as data scientist.
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11 answers
Joshua Allard, Ph.D.
Data Science & AI, Quantum AI designer developer
40
Answers
Port St. Lucie, Florida
Updated
Joshua’s Answer
To learn data science and artificial intelligence (AI), you'll need a combination of resources that cover theoretical knowledge, hands-on practice, and industry insights. Start with foundational programming skills, focusing on languages like Python and R, widely used in data science. Familiarize yourself with essential libraries such as Pandas, NumPy, and TensorFlow, which are key to data manipulation and machine learning. Additionally, online platforms like Coursera, edX, and DataCamp offer excellent courses ranging from beginner to advanced. For theory, delve into statistics, probability, linear algebra, and calculus, as they form the backbone of many AI and machine learning models. Don't forget to practice regularly on platforms like Kaggle, where you can work on real-world datasets and participate in competitions to build your portfolio. Joining data science communities, reading research papers, and attending webinars or conferences will help you stay current with the latest trends and developments. As you’re already pursuing a software engineering degree, you’re in a great position, as it gives you a solid foundation in coding and problem-solving skills, which are essential in data science. let take a deeper look
To pursue a successful career in data science and artificial intelligence (AI), you’ll need comprehensive resources and skills beyond basic programming. As an undergraduate software engineering student, you’re already on the right path, but transitioning into data science requires additional knowledge in critical areas. First and foremost, mastering programming languages like Python and R is crucial. Python is widely used for data manipulation, machine learning, and deep learning, with libraries like Pandas, NumPy, TensorFlow, and Scikit-learn essential tools. SQL is another vital skill, as you’ll need to work with databases to manage and retrieve large datasets.
Mathematics plays a fundamental role in data science, so you should strengthen your understanding of linear algebra, calculus, and probability. These topics are critical for algorithms, optimization techniques like gradient descent, and understanding data distributions. A good grasp of statistics is also vital, as it forms the basis for hypothesis testing, regression, and other machine learning models. As you advance, you’ll delve into machine learning and AI topics such as supervised and unsupervised, reinforcement, and deep learning using frameworks like TensorFlow or PyTorch. Platforms like Kaggle provide a hands-on way to apply what you’ve learned through real-world projects and competitions.
In addition to these technical skills, learning to work with big data using tools like Hadoop, Spark, and cloud platforms like AWS, Google Cloud, or Azure is essential for scaling AI models. Many online courses from platforms like Coursera, edX, and Udemy offer detailed instruction in data science and AI. Courses such as Andrew Ng’s Machine Learning on Coursera or Deep Learning Specialization by Deeplearning.ai are highly recommended. These courses and hands-on practice will provide you with the foundational knowledge you need.
To complement your technical education, internships, and capstone projects offer invaluable practical experience. Seek opportunities to apply your knowledge in real-world settings, as this will enhance your resume and demonstrate your skills to potential employers. Additionally, I learned to use Git for version control and collaboration, as it’s an industry-standard tool for managing code. Soft skills like communication and business acumen are equally important, as you’ll need to explain complex data-driven insights to non-technical stakeholders clearly and concisely.
Joining data science communities on platforms like Kaggle, Reddit, and Stack Overflow will help you stay up-to-date with the latest trends, get advice, and network with other professionals. With continuous learning, hands-on experience, and a commitment to mastering technical and soft skills, you’ll be well-prepared to build a successful career in data science and AI. Remember, if you are passionate about your goals, you will find that learning these skills is second nature, and you will be able to see your goals easily to obtain
To pursue a successful career in data science and artificial intelligence (AI), you’ll need comprehensive resources and skills beyond basic programming. As an undergraduate software engineering student, you’re already on the right path, but transitioning into data science requires additional knowledge in critical areas. First and foremost, mastering programming languages like Python and R is crucial. Python is widely used for data manipulation, machine learning, and deep learning, with libraries like Pandas, NumPy, TensorFlow, and Scikit-learn essential tools. SQL is another vital skill, as you’ll need to work with databases to manage and retrieve large datasets.
Mathematics plays a fundamental role in data science, so you should strengthen your understanding of linear algebra, calculus, and probability. These topics are critical for algorithms, optimization techniques like gradient descent, and understanding data distributions. A good grasp of statistics is also vital, as it forms the basis for hypothesis testing, regression, and other machine learning models. As you advance, you’ll delve into machine learning and AI topics such as supervised and unsupervised, reinforcement, and deep learning using frameworks like TensorFlow or PyTorch. Platforms like Kaggle provide a hands-on way to apply what you’ve learned through real-world projects and competitions.
In addition to these technical skills, learning to work with big data using tools like Hadoop, Spark, and cloud platforms like AWS, Google Cloud, or Azure is essential for scaling AI models. Many online courses from platforms like Coursera, edX, and Udemy offer detailed instruction in data science and AI. Courses such as Andrew Ng’s Machine Learning on Coursera or Deep Learning Specialization by Deeplearning.ai are highly recommended. These courses and hands-on practice will provide you with the foundational knowledge you need.
To complement your technical education, internships, and capstone projects offer invaluable practical experience. Seek opportunities to apply your knowledge in real-world settings, as this will enhance your resume and demonstrate your skills to potential employers. Additionally, I learned to use Git for version control and collaboration, as it’s an industry-standard tool for managing code. Soft skills like communication and business acumen are equally important, as you’ll need to explain complex data-driven insights to non-technical stakeholders clearly and concisely.
Joining data science communities on platforms like Kaggle, Reddit, and Stack Overflow will help you stay up-to-date with the latest trends, get advice, and network with other professionals. With continuous learning, hands-on experience, and a commitment to mastering technical and soft skills, you’ll be well-prepared to build a successful career in data science and AI. Remember, if you are passionate about your goals, you will find that learning these skills is second nature, and you will be able to see your goals easily to obtain
Adit Sheth
Senior Software Engineer
134
Answers
Bellevue, Washington
Updated
Adit’s Answer
Fantastic! The realms of data science and artificial intelligence are truly thrilling. Here are some helpful tools to kickstart your journey:
Programming Languages: Initiate your learning with Python or R. Libraries in Python such as Pandas, NumPy, TensorFlow, and PyTorch are essential for mastering data science and AI.
Web-based Courses: Websites like Coursera, edX, and Udacity provide courses on Data Science, Machine Learning, and AI. Andrew Ng’s Machine Learning course on Coursera is worth exploring.
Literature: The book “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” authored by Aurélien Géron is an excellent starting point.
Kaggle: This platform allows you to practice with datasets, participate in contests, and gain insights from other data science enthusiasts.
Mathematics: Refresh your knowledge in statistics, linear algebra, and calculus, as these are the pillars of AI.
Stay inquisitive, maintain regular practice, and don't shy away from undertaking projects! Best of luck!
Programming Languages: Initiate your learning with Python or R. Libraries in Python such as Pandas, NumPy, TensorFlow, and PyTorch are essential for mastering data science and AI.
Web-based Courses: Websites like Coursera, edX, and Udacity provide courses on Data Science, Machine Learning, and AI. Andrew Ng’s Machine Learning course on Coursera is worth exploring.
Literature: The book “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” authored by Aurélien Géron is an excellent starting point.
Kaggle: This platform allows you to practice with datasets, participate in contests, and gain insights from other data science enthusiasts.
Mathematics: Refresh your knowledge in statistics, linear algebra, and calculus, as these are the pillars of AI.
Stay inquisitive, maintain regular practice, and don't shy away from undertaking projects! Best of luck!
Wojciech Gradzki
Software System Senior Engineer
33
Answers
Warsaw, Masovian Voivodeship, Poland
Updated
Wojciech’s Answer
To learn data science and artificial intelligence, start with online courses from platforms like Coursera, edX, or Udacity, which offer comprehensive programs in these fields. Books such as “Python for Data Analysis” by Wes McKinney and “Deep Learning” by Ian Goodfellow are essential reads. Practice coding regularly on platforms like Kaggle and GitHub to build your portfolio. Join data science communities and attend webinars or conferences to stay updated and network with professionals. Lastly, work on real-world projects and internships to gain practical experience.
Sarat Kollimarla
Experience Designer
10
Answers
Waltham, Massachusetts
Updated
Sarat’s Answer
Learning Data Science and Artificial Intelligence:
As an undergrad software engineering student, you're taking a great step by pursuing a career in data science. To get started, you'll need to acquire a combination of technical skills, knowledge, and resources. Here are some essential resources to help you learn data science and artificial intelligence:
Online Courses:
Coursera: Offers a wide range of data science and AI courses from top universities like Stanford and Duke.
edX: Provides a variety of data science and AI courses, including a MicroMasters program in Data Science.
Udemy: Offers a vast array of data science and AI courses, often at an affordable price.
Kaggle: Provides hands-on experience with data science and AI through competitions and tutorials.
Books:
"Python Data Science Handbook" by Jake VanderPlas: A comprehensive guide to data science with Python.
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron: A practical guide to machine learning with Python.
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: A foundational textbook on deep learning.
"Data Science for Business" by Foster Provost and Tom Fawcett: A business-focused guide to data science.
Tools and Software:
Python: A popular programming language for data science and AI.
Jupyter Notebook: An interactive environment for data science and AI development.
Scikit-Learn: A widely used machine learning library for Python.
TensorFlow: An open-source machine learning framework developed by Google.
Keras: A high-level neural networks API for Python.
Communities and Forums:
Kaggle: A community of data scientists and AI enthusiasts, with forums and discussions.
Reddit: r/MachineLearning and r/DataScience are popular communities for discussing data science and AI.
Stack Overflow: A Q&A platform for programmers, including data science and AI enthusiasts.
GitHub: A platform for open-source projects, including data science and AI repositories.
Data Sources:
UCI Machine Learning Repository: A collection of machine learning datasets.
Kaggle Datasets: A wide range of datasets for data science and AI competitions.
Google Dataset Search: A search engine for datasets.
Data.gov: A repository of government datasets.
Career Development:
Build a portfolio: Showcase your data science and AI projects to demonstrate your skills.
Network: Attend conferences, meetups, and online communities to connect with professionals in the field.
Stay up-to-date: Follow industry leaders, researchers, and news outlets to stay informed about the latest developments in data science and AI.
Undergrad Software Engineering Background:
As a software engineering student, you already have a solid foundation in programming and software development. To transition into data science and AI, focus on acquiring the following skills:
Programming languages: Python, R, or Julia.
Data structures and algorithms: Familiarize yourself with data structures like arrays, linked lists, and trees.
Machine learning: Study machine learning fundamentals, including supervised and unsupervised learning.
Deep learning: Learn about neural networks, convolutional neural networks, and recurrent neural networks.
Getting Started:
Take online courses: Start with introductory courses on data science and AI.
Practice with projects: Apply your knowledge to real-world projects and datasets.
Join online communities: Engage with data science and AI communities to learn from others and get feedback on your projects.
Read books and articles: Stay up-to-date with the latest developments in data science and AI.
As an undergrad software engineering student, you're taking a great step by pursuing a career in data science. To get started, you'll need to acquire a combination of technical skills, knowledge, and resources. Here are some essential resources to help you learn data science and artificial intelligence:
Online Courses:
Coursera: Offers a wide range of data science and AI courses from top universities like Stanford and Duke.
edX: Provides a variety of data science and AI courses, including a MicroMasters program in Data Science.
Udemy: Offers a vast array of data science and AI courses, often at an affordable price.
Kaggle: Provides hands-on experience with data science and AI through competitions and tutorials.
Books:
"Python Data Science Handbook" by Jake VanderPlas: A comprehensive guide to data science with Python.
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron: A practical guide to machine learning with Python.
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: A foundational textbook on deep learning.
"Data Science for Business" by Foster Provost and Tom Fawcett: A business-focused guide to data science.
Tools and Software:
Python: A popular programming language for data science and AI.
Jupyter Notebook: An interactive environment for data science and AI development.
Scikit-Learn: A widely used machine learning library for Python.
TensorFlow: An open-source machine learning framework developed by Google.
Keras: A high-level neural networks API for Python.
Communities and Forums:
Kaggle: A community of data scientists and AI enthusiasts, with forums and discussions.
Reddit: r/MachineLearning and r/DataScience are popular communities for discussing data science and AI.
Stack Overflow: A Q&A platform for programmers, including data science and AI enthusiasts.
GitHub: A platform for open-source projects, including data science and AI repositories.
Data Sources:
UCI Machine Learning Repository: A collection of machine learning datasets.
Kaggle Datasets: A wide range of datasets for data science and AI competitions.
Google Dataset Search: A search engine for datasets.
Data.gov: A repository of government datasets.
Career Development:
Build a portfolio: Showcase your data science and AI projects to demonstrate your skills.
Network: Attend conferences, meetups, and online communities to connect with professionals in the field.
Stay up-to-date: Follow industry leaders, researchers, and news outlets to stay informed about the latest developments in data science and AI.
Undergrad Software Engineering Background:
As a software engineering student, you already have a solid foundation in programming and software development. To transition into data science and AI, focus on acquiring the following skills:
Programming languages: Python, R, or Julia.
Data structures and algorithms: Familiarize yourself with data structures like arrays, linked lists, and trees.
Machine learning: Study machine learning fundamentals, including supervised and unsupervised learning.
Deep learning: Learn about neural networks, convolutional neural networks, and recurrent neural networks.
Getting Started:
Take online courses: Start with introductory courses on data science and AI.
Practice with projects: Apply your knowledge to real-world projects and datasets.
Join online communities: Engage with data science and AI communities to learn from others and get feedback on your projects.
Read books and articles: Stay up-to-date with the latest developments in data science and AI.
venkat viswanathan
Technology Architect
3
Answers
Leesburg, Virginia
Updated
venkat’s Answer
To learn data science and artificial intelligence (AI), you'll need a mix of technical resources, hands-on practice, and theoretical understanding. Here's a structured approach to get started:
### 1. **Mathematics and Statistics**
- **Linear Algebra**: Learn matrix operations, vector spaces, eigenvalues, and eigenvectors.
- **Calculus**: Focus on differentiation and integration, especially for optimization.
- **Probability & Statistics**: Understand distributions, hypothesis testing, and statistical models.
- **Resources**:
- *Essence of Linear Algebra* by 3Blue1Brown (YouTube)
- *Introduction to Probability* by MIT OpenCourseWare
- *Khan Academy* for Calculus
### 2. **Programming**
- **Languages**: Learn Python and R, the most commonly used languages in data science and AI.
- **Key Libraries**:
- **Python**: Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib/Seaborn
- **R**: dplyr, ggplot2, caret, tidyverse
- **Resources**:
- *Python for Data Science Handbook* by Jake VanderPlas
- *R for Data Science* by Hadley Wickham
- Coursera: *Python for Data Science* by IBM
### 3. **Data Analysis and Visualization**
- Learn how to clean, analyze, and visualize data using tools like Pandas, Matplotlib, and Seaborn (Python) or dplyr and ggplot2 (R).
- **Resources**:
- *Practical Statistics for Data Scientists* by Peter Bruce
- *Storytelling with Data* by Cole Nussbaumer Knaflic
- *Exploratory Data Analysis with Python* by Maarten Grootendorst (free online)
### 4. **Machine Learning (ML)**
- **Supervised Learning**: Linear regression, decision trees, support vector machines, etc.
- **Unsupervised Learning**: Clustering, dimensionality reduction (e.g., PCA, t-SNE).
- **Reinforcement Learning**: Basics of agents, rewards, and environments.
- **Resources**:
- *Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow* by Aurélien Géron
- Coursera: *Machine Learning* by Andrew Ng (Stanford University)
- Fast.ai: *Practical Deep Learning for Coders* (free)
### 5. **Deep Learning and AI**
- **Neural Networks**: Learn the architecture and training processes (backpropagation, gradient descent).
- **Convolutional Neural Networks (CNNs)**: For image processing.
- **Recurrent Neural Networks (RNNs)**: For sequence data.
- **Resources**:
- *Deep Learning* by Ian Goodfellow and Yoshua Bengio
- Coursera: *Deep Learning Specialization* by Andrew Ng
- PyTorch and TensorFlow documentation
### 6. **Big Data Tools**
- Learn tools for working with large-scale datasets, such as Hadoop, Spark, and NoSQL databases like MongoDB.
- **Resources**:
- *Big Data Processing with Apache Spark* (edX)
- *Learning Spark* by Jules S. Damji
### 7. **Projects and Hands-On Practice**
- Work on real datasets to apply your knowledge and build your portfolio. Kaggle provides numerous datasets and competitions.
- **Resources**:
- Kaggle (Competitions, Datasets)
- UCI Machine Learning Repository (for data)
### 8. **Cloud Platforms and Deployment**
- Learn how to deploy AI models using cloud services like AWS, Google Cloud, or Microsoft Azure.
- **Resources**:
- AWS Machine Learning Labs
- *Google Cloud AI & Machine Learning* by Google Cloud
### 9. **Ethics and Responsible AI**
- Understanding the societal impact, fairness, and ethical concerns in AI.
- **Resources**:
- *Weapons of Math Destruction* by Cathy O’Neil
- *Ethics of AI and Data Science* by Udacity
### 1. **Mathematics and Statistics**
- **Linear Algebra**: Learn matrix operations, vector spaces, eigenvalues, and eigenvectors.
- **Calculus**: Focus on differentiation and integration, especially for optimization.
- **Probability & Statistics**: Understand distributions, hypothesis testing, and statistical models.
- **Resources**:
- *Essence of Linear Algebra* by 3Blue1Brown (YouTube)
- *Introduction to Probability* by MIT OpenCourseWare
- *Khan Academy* for Calculus
### 2. **Programming**
- **Languages**: Learn Python and R, the most commonly used languages in data science and AI.
- **Key Libraries**:
- **Python**: Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib/Seaborn
- **R**: dplyr, ggplot2, caret, tidyverse
- **Resources**:
- *Python for Data Science Handbook* by Jake VanderPlas
- *R for Data Science* by Hadley Wickham
- Coursera: *Python for Data Science* by IBM
### 3. **Data Analysis and Visualization**
- Learn how to clean, analyze, and visualize data using tools like Pandas, Matplotlib, and Seaborn (Python) or dplyr and ggplot2 (R).
- **Resources**:
- *Practical Statistics for Data Scientists* by Peter Bruce
- *Storytelling with Data* by Cole Nussbaumer Knaflic
- *Exploratory Data Analysis with Python* by Maarten Grootendorst (free online)
### 4. **Machine Learning (ML)**
- **Supervised Learning**: Linear regression, decision trees, support vector machines, etc.
- **Unsupervised Learning**: Clustering, dimensionality reduction (e.g., PCA, t-SNE).
- **Reinforcement Learning**: Basics of agents, rewards, and environments.
- **Resources**:
- *Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow* by Aurélien Géron
- Coursera: *Machine Learning* by Andrew Ng (Stanford University)
- Fast.ai: *Practical Deep Learning for Coders* (free)
### 5. **Deep Learning and AI**
- **Neural Networks**: Learn the architecture and training processes (backpropagation, gradient descent).
- **Convolutional Neural Networks (CNNs)**: For image processing.
- **Recurrent Neural Networks (RNNs)**: For sequence data.
- **Resources**:
- *Deep Learning* by Ian Goodfellow and Yoshua Bengio
- Coursera: *Deep Learning Specialization* by Andrew Ng
- PyTorch and TensorFlow documentation
### 6. **Big Data Tools**
- Learn tools for working with large-scale datasets, such as Hadoop, Spark, and NoSQL databases like MongoDB.
- **Resources**:
- *Big Data Processing with Apache Spark* (edX)
- *Learning Spark* by Jules S. Damji
### 7. **Projects and Hands-On Practice**
- Work on real datasets to apply your knowledge and build your portfolio. Kaggle provides numerous datasets and competitions.
- **Resources**:
- Kaggle (Competitions, Datasets)
- UCI Machine Learning Repository (for data)
### 8. **Cloud Platforms and Deployment**
- Learn how to deploy AI models using cloud services like AWS, Google Cloud, or Microsoft Azure.
- **Resources**:
- AWS Machine Learning Labs
- *Google Cloud AI & Machine Learning* by Google Cloud
### 9. **Ethics and Responsible AI**
- Understanding the societal impact, fairness, and ethical concerns in AI.
- **Resources**:
- *Weapons of Math Destruction* by Cathy O’Neil
- *Ethics of AI and Data Science* by Udacity
Cory Yeager, PE, SE, MsC, PPM
51
Answers
Updated
Cory’s Answer
I will not be able to guide you for I am not a career scientist. However used to work with a group of data scientist and they had a few things in common:
Able to write code in python and html or something similar.
Able to synthesize data down to identify unforeseen trends
Data management credentials
Able to write code in python and html or something similar.
Able to synthesize data down to identify unforeseen trends
Data management credentials
Biju Baburajan
Product Leader, Startups, Software development & AI
12
Answers
Updated
Biju’s Answer
If you are interested to become a data scientist, start with the basics like variables, data types and basic level statistical analysis. once you are comfortable, move to complex analysis, probability, distributions, and hypothesis testing. They're the building blocks of data analysis.
Once you've got those down, try to explore data cleaning, learn how to handle missing values, outliers, and inconsistencies. Be familiar with Data Visualization tools.
To get in to the next level, start exploring machine learning techniques such as supervised learning ( Train models on labeled data to make predictions) and Unsupervised Learning: (Identify patterns in unlabeled data) . If you are not familiar with programming, I would recommend to learn Python which is critical in DS and AI.
I'm not recommending any specific courses or books, as there is a vast amount of resources available online on the mentioned topic. However, feel free to reach out if you need any specific recommendations.
Once you've got those down, try to explore data cleaning, learn how to handle missing values, outliers, and inconsistencies. Be familiar with Data Visualization tools.
To get in to the next level, start exploring machine learning techniques such as supervised learning ( Train models on labeled data to make predictions) and Unsupervised Learning: (Identify patterns in unlabeled data) . If you are not familiar with programming, I would recommend to learn Python which is critical in DS and AI.
I'm not recommending any specific courses or books, as there is a vast amount of resources available online on the mentioned topic. However, feel free to reach out if you need any specific recommendations.
Tejas Gupta
Data Analyst
2
Answers
Indore, Madhya Pradesh, India
Updated
Tejas’s Answer
As an undergraduate software engineering student aiming to become a data scientist, you’ll need a well-rounded understanding of various topics such as programming, statistics, machine learning, and artificial intelligence (AI). Here are some resources to help you build a strong foundation:
1. Programming Languages
Python and R are the most widely used languages in data science.
Python: Start with Real Python for Python tutorials.
R: Use R for Data Science by Hadley Wickham for a good introduction.
Familiarize yourself with libraries like Pandas, NumPy, Matplotlib, and SciPy for data manipulation and visualization.
2. Mathematics and Statistics
Statistics: A strong understanding of statistics is critical for data analysis and machine learning.
StatQuest: Great for simple, visual explanations of statistical concepts. StatQuest on YouTube
Khan Academy: Explore Statistics and Probability.
Linear Algebra and Calculus: These are the backbone of machine learning algorithms.
3Blue1Brown: Visual explanations of math concepts, including linear algebra. 3Blue1Brown on YouTube
MIT OpenCourseWare: Linear Algebra by Gilbert Strang.
3. Machine Learning
Books:
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron: Great for practical machine learning using Python libraries.
"Pattern Recognition and Machine Learning" by Christopher Bishop: A deeper dive into the theory of machine learning.
Courses:
Andrew Ng's Machine Learning Course (Coursera): One of the most popular and beginner-friendly courses to get a solid grasp on machine learning.
Fast.ai: Practical deep learning course that’s project-driven and uses PyTorch. Fast.ai
4. Artificial Intelligence (AI)
Deep Learning Specialization by Andrew Ng (Coursera): A comprehensive AI course focusing on neural networks and deep learning.
"Deep Learning" by Ian Goodfellow: Known as the Bible of deep learning, this book covers the theory behind AI and neural networks.
OpenAI Gym: For hands-on practice with reinforcement learning and AI. OpenAI Gym
5. Data Science and Projects
Kaggle: A platform for data science competitions. It offers free datasets and tutorials, making it a great way to practice your skills. Kaggle
"Data Science for Business" by Foster Provost and Tom Fawcett: This book is perfect for understanding how data science is applied in business settings.
6. Tools and Frameworks
Jupyter Notebooks: For interactive coding and visualization.
TensorFlow and PyTorch: Popular libraries for building machine learning models.
1. Programming Languages
Python and R are the most widely used languages in data science.
Python: Start with Real Python for Python tutorials.
R: Use R for Data Science by Hadley Wickham for a good introduction.
Familiarize yourself with libraries like Pandas, NumPy, Matplotlib, and SciPy for data manipulation and visualization.
2. Mathematics and Statistics
Statistics: A strong understanding of statistics is critical for data analysis and machine learning.
StatQuest: Great for simple, visual explanations of statistical concepts. StatQuest on YouTube
Khan Academy: Explore Statistics and Probability.
Linear Algebra and Calculus: These are the backbone of machine learning algorithms.
3Blue1Brown: Visual explanations of math concepts, including linear algebra. 3Blue1Brown on YouTube
MIT OpenCourseWare: Linear Algebra by Gilbert Strang.
3. Machine Learning
Books:
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron: Great for practical machine learning using Python libraries.
"Pattern Recognition and Machine Learning" by Christopher Bishop: A deeper dive into the theory of machine learning.
Courses:
Andrew Ng's Machine Learning Course (Coursera): One of the most popular and beginner-friendly courses to get a solid grasp on machine learning.
Fast.ai: Practical deep learning course that’s project-driven and uses PyTorch. Fast.ai
4. Artificial Intelligence (AI)
Deep Learning Specialization by Andrew Ng (Coursera): A comprehensive AI course focusing on neural networks and deep learning.
"Deep Learning" by Ian Goodfellow: Known as the Bible of deep learning, this book covers the theory behind AI and neural networks.
OpenAI Gym: For hands-on practice with reinforcement learning and AI. OpenAI Gym
5. Data Science and Projects
Kaggle: A platform for data science competitions. It offers free datasets and tutorials, making it a great way to practice your skills. Kaggle
"Data Science for Business" by Foster Provost and Tom Fawcett: This book is perfect for understanding how data science is applied in business settings.
6. Tools and Frameworks
Jupyter Notebooks: For interactive coding and visualization.
TensorFlow and PyTorch: Popular libraries for building machine learning models.
JT Singh
Technology Auditor
10
Answers
Princeton, New Jersey
Updated
JT’s Answer
To become an AI expert, you’ll need a combination of knowledge, skills, and resources:
1. Foundational Knowledge:
Mathematics: Strong foundation in linear algebra, calculus, probability, and statistics.
Computer Science: Proficiency in programming (Python is highly recommended), data structures, algorithms, and databases.
2. Core AI Concepts:
Machine Learning: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), reinforcement learning.
Deep Learning: Neural networks (CNNs, RNNs, Transformers), deep learning frameworks (TensorFlow, PyTorch).
Natural Language Processing (NLP): Text processing, sentiment analysis, language modeling, machine translation.
Computer Vision: Image classification, object detection, image segmentation.
3. Tools and Technologies:
Programming Languages: Python, R, Java.
Machine Learning Libraries: scikit-learn, TensorFlow, PyTorch, Keras.
Cloud Computing Platforms: AWS, Google Cloud Platform, Azure (for large-scale data storage and processing).
Data Visualization Tools: Tableau, Power BI (for communicating insights).
4. Essential Skills:
Problem-Solving: Breaking down complex problems into manageable parts.
Analytical Thinking: Analyzing data, identifying patterns, drawing conclusions.
Critical Thinking: Evaluating different AI approaches and their limitations.
Communication: Clearly explaining complex concepts to technical and non-technical audiences.
5. Continuous Learning:
Online Courses: Platforms like Coursera, edX, Udacity, and Deeplearning.ai offer excellent AI courses.
Books and Research Papers: Stay updated on the latest advancements in the field.
Conferences and Workshops: Attend industry events to network and learn from experts.
Personal Projects: Build your own AI projects to solidify your skills and gain practical experience.
1. Foundational Knowledge:
Mathematics: Strong foundation in linear algebra, calculus, probability, and statistics.
Computer Science: Proficiency in programming (Python is highly recommended), data structures, algorithms, and databases.
2. Core AI Concepts:
Machine Learning: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), reinforcement learning.
Deep Learning: Neural networks (CNNs, RNNs, Transformers), deep learning frameworks (TensorFlow, PyTorch).
Natural Language Processing (NLP): Text processing, sentiment analysis, language modeling, machine translation.
Computer Vision: Image classification, object detection, image segmentation.
3. Tools and Technologies:
Programming Languages: Python, R, Java.
Machine Learning Libraries: scikit-learn, TensorFlow, PyTorch, Keras.
Cloud Computing Platforms: AWS, Google Cloud Platform, Azure (for large-scale data storage and processing).
Data Visualization Tools: Tableau, Power BI (for communicating insights).
4. Essential Skills:
Problem-Solving: Breaking down complex problems into manageable parts.
Analytical Thinking: Analyzing data, identifying patterns, drawing conclusions.
Critical Thinking: Evaluating different AI approaches and their limitations.
Communication: Clearly explaining complex concepts to technical and non-technical audiences.
5. Continuous Learning:
Online Courses: Platforms like Coursera, edX, Udacity, and Deeplearning.ai offer excellent AI courses.
Books and Research Papers: Stay updated on the latest advancements in the field.
Conferences and Workshops: Attend industry events to network and learn from experts.
Personal Projects: Build your own AI projects to solidify your skills and gain practical experience.
Tracey O'Halloran
Account Executive
3
Answers
Dublin, County Dublin
Updated
Tracey’s Answer
To learn Data Science and Artificial Intelligence (AI) effectively, you'll need a combination of theoretical knowledge, practical skills, and tools to get hands-on experience. Here’s a structured list of resources and topics to focus on:
1. Mathematics & Statistics
2. Programming Languages
3. Data Science Concepts
4. Machine Learning (ML)
5. Data Engineering & Big Data
6. AI Concepts & Specializations
1. Mathematics & Statistics
2. Programming Languages
3. Data Science Concepts
4. Machine Learning (ML)
5. Data Engineering & Big Data
6. AI Concepts & Specializations
Jared Allison
CyberSecurity
3
Answers
Buffalo, New York
Updated
Jared’s Answer
There are a lot of great answers above with specifics. Just a few that I'd add:
1. Curiosity is extremely important when your job is looking at data to interpret the story it tells. Be willing to spend time investigating and trying different algorithms, interpretations, visualizations, and so on that may not work out.
2. Take time to understand what the data actually represents. If you approach data from a purely mathematical perspective, it'll be much more difficult to know if any of your conclusions or presentations of the data make sense. To that end, you may want to consider combining your data science study with another field, such as biology or astronomy, that will better prepare you to not only work with the data but also understand how it fits into the big picture.
1. Curiosity is extremely important when your job is looking at data to interpret the story it tells. Be willing to spend time investigating and trying different algorithms, interpretations, visualizations, and so on that may not work out.
2. Take time to understand what the data actually represents. If you approach data from a purely mathematical perspective, it'll be much more difficult to know if any of your conclusions or presentations of the data make sense. To that end, you may want to consider combining your data science study with another field, such as biology or astronomy, that will better prepare you to not only work with the data but also understand how it fits into the big picture.