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Partial book profile

Advancing into Analytics

George Mount

This book empowers intermediate Excel users to transition into data analytics by leveraging their existing spreadsheet knowledge to master statistical concepts and programming in R and Python.

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What it’s about

For many professionals comfortable with Excel, the leap into the world of data analytics with programming languages like R and Python can seem daunting. 'Advancing into Analytics' demystifies this journey by framing your Excel expertise not as a crutch, but as a powerful head start. Author George Mount rejects the common 'Excel bad, coding good' narrative, instead using the visual and intuitive nature of spreadsheets to first teach core statistical concepts like exploratory data analysis, probability, and hypothesis testing. Once you've grasped these fundamentals conceptually, the book seamlessly pivots your knowledge, showing you how to execute the same tasks in R and then Python. This approach dramatically lowers the cognitive overhead of learning programming and statistics simultaneously, building your confidence and equipping you with the in-demand skills to become a proficient data analyst.

The through-line

Who it’s for
An intermediate Excel user who is proficient with spreadsheets but feels their career growth is capped. They want to advance into a formal data analytics role but are intimidated by the steep learning curve of programming languages like R and Python and feel their Excel skills are often dismissed.
The problem
They need to learn programming and statistical concepts to perform more advanced, reproducible data analysis, but most learning resources force them to start from zero, ignoring their existing data literacy. They feel overwhelmed by the need to learn new statistical theories and new programming syntax at the same time. They suffer from 'impostor syndrome' and are frustrated that their hard-won Excel expertise is seen as a liability rather than an asset.
The plan
  1. Master the foundations of analytics (EDA, probability, inference) in the familiar environment of Excel.
  2. Translate your conceptual understanding from Excel into the R programming language.
  3. Pivot your skills again to perform complete data analyses in the Python programming language.
The payoff
The reader becomes a confident, well-rounded data analyst, fluent in Excel, R, and Python. · They can intelligently select the right tool for any given analytics task. · They overcome their impostor syndrome and unlock new career opportunities in the field of data analytics.

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