【Learning Record】 2021.05.11

short summary

• I completed a few simple Python For loops today and updated a copy on Github ,
• Complete the Google Data Analytics Professional Certificate (4/8) ,
• Expanded the Data Science syllabus and organized online learning resources on hand

Content and Reflection

The first is Python For loops. I have completed several For loops exercises. Compared with the writing method of the website, my writing method is more result-oriented. It can meet the requirements of the title, and it is not very neat. But one thing to note is that I don’t know if it’s because I haven’t touched Python for a while, my familiarity has declined, or I’m learning two programming languages at the same time. When I type Python, I type it in the SQL format, for example:

 Should have: for x in range(1,11):
Type: FOR x  
— Get used to typing out SELECT FROM WHERE first (?) when typing SQL

This part needs more study. After that, you should add a practical practice time, and change the daily time allocation to continue to complete the Google Professional Certificate with a higher theoretical weight + a time to learn the content of Udemy Bootcamp.

Then there is the second half of Google - Process Data from Dirty to Clean, which mainly explains Documentation- Changelog and several inspection directions in Data Cleaning ; several commonly used SQL Functions (DISTINCT, LENGTH, SUBSTR, TRIM, CAST, CONCAT, COALESCE, CASE ) and the actual operation; and several directions for modifying Resume .

Google After reading this Professional Certificate course, I feel that it is more inclined to [constructing an understanding of Data Science], which can just add a part of the theory, and more operations should be added in the Udemy Bootcamp Course.

personal study outline

Only a part of the content has been sorted out today. The current major learning topics include the following:

• Programming languages (Python, R)
• Statistics related to DS
• Database & SQL
• Machine Learning
• Deep Learning
• Data Visualisation (Tableau)

The details below the outline are well divided. Today, we also organize the e-learning resources that we have. Currently, there are Bootcamp courses on Data Science & Machine Learning and SQL, both of which include details under several major themes. . In other words, it can be said that in addition to the Data Visualisation section, there are entry-level learning resources. (Coincidentally, a lot of the theoretical part in my eyes is on the Coursera platform, and a lot of the hands-on writing is on the Udemy platform.)

plan for tomorrow

1. Indicate the expected completion time for the syllabus (try a Gantt chart)
2. Start Google - Analyze Data to Answer Questions
3. Python Function exercise
4. running

Goal of the week

1. Run
2. Complete the Google Data Analytics Professional Certificate (5/8)
3. Go outside, don't stay locked up
4. Set the direction of several small projects, the current curious questions include:

 —Current global clean energy production and usage?
—Using data to analyze different STAYCATION styles, which hotel is the best choice?
—In e-sports competitions, how much does the choice of venue affect the number of viewers?
— What will global park admissions look like under COVID?

I didn't expect to receive a message, thank you for your welcome. Being able to record my learning records in a warm place is also a kind of encouragement and motivation.