Podcast
Questions and Answers
What is the suggested first programming language for someone starting in data science?
What is the suggested first programming language for someone starting in data science?
- Java
- C++
- R
- Python (correct)
Which statistical concept is particularly emphasized as important to understand in data science?
Which statistical concept is particularly emphasized as important to understand in data science?
- Variance
- Standard Deviation
- Normal Distribution (correct)
- Chi-Square Distribution
What resource is recommended for learning about Linear Algebra for data science?
What resource is recommended for learning about Linear Algebra for data science?
- Linear Algebra Notes by Queen Mary University of London (correct)
- Coursera Linear Algebra Course
- MIT OpenCourseWare
- Khan Academy
Which of the following is NOT a database technology mentioned for learning CRUD operations?
Which of the following is NOT a database technology mentioned for learning CRUD operations?
When transitioning to Machine Learning, which book is recommended?
When transitioning to Machine Learning, which book is recommended?
Which programming library is specifically mentioned for data visualization?
Which programming library is specifically mentioned for data visualization?
What is an example of a resource for learning Linux commands?
What is an example of a resource for learning Linux commands?
What mathematical concept related to optimization should one learn for data science?
What mathematical concept related to optimization should one learn for data science?
Flashcards
Mean
Mean
The average of a dataset, calculated by summing all values and dividing by the number of values.
Median
Median
The middle value in a sorted dataset. If there are two middle values, the average is taken.
Mode
Mode
The most frequent value in a dataset.
Standard Deviation
Standard Deviation
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Linear Algebra
Linear Algebra
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Gradient Descent
Gradient Descent
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Python
Python
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NumPy
NumPy
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Study Notes
Data Science Roadmap
- To become a data scientist, learn the necessary things
- College degree not strictly required, basic math understanding sufficient
- Resources for each subject are available at the end of the document
Requirements
-
Statistics and Maths:
- Learn linear algebra from resources like Queen Mary University of London
- Understand basic concepts like mean, median, mode, and dy/dx
- Review probability and statistics using a book like by William Hines
- Focus on normal distribution and optimization
- Learn about Gradient Descent and Graphs
-
Programming (Python):
- Python is recommended as the first programming language
- Use online resources like 100 Days of Code by CodeWithHarry
- Learn basic Python concepts, data science libraries (NumPy, Pandas)
- Free resources and paid courses on platforms like Udemy and Amazon are available
- Practice creating quick data reports
-
Databases:
- Learn CRUD operations (Create, Read, Update, Delete) on different database technologies like MySQL, MongoDB, PyMongo, SQLAlchemy.
- Pick the technology based on data fetching needs
-
Machine Learning:
- Transition to Machine Learning after gaining proficiency in Python and data science projects
- Use resources like "Hands-on ML with Scikit-learn and TensorFlow" and relevant project videos
- Update knowledge by following relevant guides on GitHub or other platforms.
-
Linux and Git:
- Learn basic Linux commands using videos like from CodeWithHarry
- Learn version control using Git and how to use it to share code with GitHub
-
Optional Tools:
- Consider AWS for cloud-based solutions, web scraping using BeautifulSoup, and tools like Tableau or Excel VBA based on needs
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