Topics to Cover in Data Analytics

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28 Sept 2022
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This is a comprehensive scheme of work for aspiring data analysts to cover in order to gain a level of expertise in the field. This could also be a guide for people doing self-learning on youtube and other learning platforms.

Important subjects in Data Analytics are, Statistical Analysis, Excel, SQL, Tableau, Power BI, and so on.  Learning a programming language such as SQL, Python, or R programming in data analytics is an additional advantage. These are the top listed skills according to job descriptions on websites for gaining a data analytics job.

Level of Expertise


Statistics:    Intermediate level 
Excel:          Advanced level
Power bi:    Advanced level
SQL:            Intermediate level
Business Fundamentals
Projects Portfolios


Scheme of Work


Statistics:
  1. Branches of Statistics: Descriptive statistics and Inferential statistics
  2. Types of Data
  3. Basic Chart types
  4. Aggregation of Data
  5. Variation of Data
  6. Linear and Regression analysis
  7. Hypothesis testing
  8. Power Analysis
  9. Errors and Estimation
  10. Bayesian Inferences


Excel- Manipulating & Exploring Data
  1. Excel Interface
  2. Basic Formulas
  3. Cell Referencing
  4. Range-table conversion
  5. Find and Replace
  6. Text and Date Function
  7. Lookup, Vlookup, Hlookup
  8. Index and Merger
  9. Conditional Formatting
  10. Sort and Filters
  11. Statistics Formulas
  12. Charts
  13. Pivot tables
  14. Slicers
  15. Excel VBA



 




References 

[2] BULB, 'Write to Earn. Read to Earn' (online, 2022) <https://www.bulbapp.io/>

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