I started to teach myself Data Science/Analysis about 3 years ago to change my career from a marketer to a data analyst. I'm not yet there to break into the data world, and my journey is still going. However, I just hit one of the biggest milestones; starting a Master's program ✨ My first class is starting tomorrow 💪 🤓
So, I decided to share my study plan, hoping to help those who are about to start or just started their data science journey. Also, if you have any suggestions or know good courses to add to the list below, please share them in the comment section!
By the way, if you're learning Data Science or trying to change your career to the data field, join our Data Science Study Group on elpha! https://elpha.com/c/data-science-study-group
We share study progress, connect with fellow learners, and help each other to achieve goals! ✨
Data Science/Analysis Study Guide for beginners 📈📚
-----
My background – Check if this study guide is useful for you ✅
- Zero STEM background
- No programming experience (I know some basic level HTML)
- Working full time as a marketer, I can only spend 2-3 hours a day for study
-----
Note:
- I recommend taking these courses from top to bottom, but feel free to skip any of them if you have enough experience or knowledge about the topic.
- Most of the courses on this list are free or relatively affordable.
- Math is one of the essential fields in data science, but I didn't have a chance to work on it as much as I should, mostly because there is a lot to cover! So I omit math courses from this list, but I recommend taking courses from Khan Academy based on your math level.
-----
Learn Python
- datacamp: Introduction to Python
- datacamp: Intermediate Python
- Kaggle micro-course: Python
- Practice your python skill with edabit.com
- This website is a very good way to practice python problem-solving. It's more challenging than entry-level python quizzes but not as difficult as other coding challenge sites, like Hackerrank. (I tried, but I failed because it was not designed for beginners like me.)
- Codeacademy: Analyze Data with SQL
- Girls in Tech: Global Classroom: Code G Level 1 - Introduction to Python
- This course is not always available. Girls in Tech offer this 4-week program once a year (Around November, I think). It's an instructor-led live course + office hours. Being able to ask questions is a very rare and valuable opportunity for me, especially because I have been teaching myself.
Learn Basic Statistics
- edX Statistics: Unlocking the World of Data
- Coursera: Introduction to Probability and Data with R
- Khan Academy: Statistics and Probability
- This course is very intensive, but after taking this course, you will be able to overcome your fear of statistics, like I did!
Learn Pandas Library
- datacamp: Data Manipulation with pandas
- datacamp: Python Data Science Toolbox
- Udacity: Data Analyst nano degree program
- This course is the most expensive online course I have taken so far. But Udacity offers promotions very frequently (up to 75% off). I would recommend checking their website from time to time.
- Thanks to this nano degree, my prerequisites for MS in Data science were waived! This program is intensive but worth investing in.
Get a taste of Machine Learning (This is optional, you will be inspired by the unlimited possibility of data science!)
- Kaggle micro-course: Intro to Machine Learning
- Kaggle micro-course: Intermediate Machine Learning
- Girls in Tech: Global Classroom: Code G Level 2 - Data Science
- Again, this is not always available. I think the program is offered around July. I recommend checking the Girls in Tech website.
Practice, practice, and practice!
I've proactively looked for opportunities to apply what I learn to my day-to-day marketing job to practice with real-world data (and help the company's business as well.) The list below is my go-to source to get raw data. Looks for what kind of data is available to your organization! (if you have a right to access the data, of course.)
- Facebook Ad manager
- Google Ad manager
- SalesForce
- Email marketing tool (MailChimp, Pardot, etc.)
- Tableau
- Google Analytics
-----
I hope this list helps! I'm not a data analyst/scientist yet, so I can't share what you need to study next at this moment, but I believe these courses will help you get the foundational skills. Let me know what you think! Your feedback is always appreciated ❤️
Also, don't forget to join our Data Science Study Group on Epha! I will continue to share my data science journey and share the updated study guide again!
