【Learning Record】 2021.05.11

coletangsy
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IPFS
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Today's study time is 4H 59M, including 30M study while waiting to watch the game in the early morning.
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.

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Like my work? Don't forget to support and clap, let me know that you are with me on the road of creation. Keep this enthusiasm together!

coletangsy學習 Data Science、Machine Learning 中,透過記錄,一步一步往目標前進。
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