https://www.youtube.com/watch?v=pMQSgriJqR8

 Lore So What: How much SQL, PowerBI Tableau, Python do you actually need in Data Analyst Job

SQL - ESSENTIAL

 1. Currently, in my opinion it is PREFERABLE to use PostgreSQL:

  ✔ From Reddit reviews it is faster, cleaner that MySQL, SSMS

  ✔ Oracle is not really mentioned

  ✔ UI is better looking

  ✔ GitLab using PSQL

2. Comfortable using tools:

  + Add a lot of comments to run the SQL Query independently by selecting only the needed part

  + Basic concepts + Syntax + Basic commands + Aggregate manipulation + Do constant Sorting

  + CTEs - very important and also simple. It basically creates a temporary table with all values needed

  + Windows function - Generally the Aggregates + OVER() (PARTITION BY column) - extremely useful

  + Null Values with Coalesce

  + Joining tables 

  + String Manipulation: CONCAT, LOWER, UPPER, DATEDIFF, DATEADD, EXTRACT, YEAR, MONTH, ROUND, FLOOR, CEIL

  + Model DATA and Create DATA pipeline - Advanced concept, needs further research. It creates relationship in RDMBS

 

PYTHON - NOT NECESSARY, GOOD TO HAVE

1. Manipulation  = SQL

2. Visualization = PowerBI or Tableau

3. Web scraping + Collecting Data from APIs

4. Analyzing and consolidating DATA from multiple excel files - Because Python capable of reading general infomation at fast speed.

5. Should know BASIC Syntax + Reading DATA + Manipulating DATA + Using libraries

6. Machine Learning - Advanced

 

 PowerBI or Tableau - basically visualization tools

1. Create Dashboard from scratch

2. Charts

3. Filters

4. Slicers

5. Fonts

6. Colors

7. Calculated fields