Top 10 Hacks to speed up your data analysis

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Data analysis can be a tedious task. Sometimes it feels like there is so much data and not enough time to analyze it all. But some simple tricks will save you a ton of time! In this blog post, we will share 10 top hacks to speed up your data analysis process. You'll learn to quickly find insights in data without wasting precious hours waiting for programs to run or crunch numbers.

Ten hacks to speed up data analysis

  1. Use hash tables instead of unsorted arrays:
  • An unsorted array is an ordered collection of objects accessible by numerical index, where the index indicates the sequence of its element's appearance in the variety.
  • A hash table is an associative array, map, lookup table, and dictionary (in programming languages with a limited vocabulary, as Python), a data structure that associates keys to values. 
  1. Store data in row-major order:
  • Use row-major order when storing data, which is faster to load into memory. Row major storage orders memory by rows.
  • Row major storage orders memory by rows instead of ordering memory by columns (called column-major storage).
  1. Group like items in buffers:
  • To speed up processing, store data in the most efficient order. 
  • For example, focus on grouping items in separate buffers instead of creating a different pad for every item.
  1. Store many data sets in memory:
  • If your data sets can fit into the RAM, many data sets into memory by using a hash table to map from keys to their corresponding data sets.
  1. Use persistent objects to pass data between function calls:
  • Endless things are less expensive to construct and maintain than ephemeral objects.
  • For example, instead of passing data from one function call to another, give object references and update the thing as needed.
  1. Use a meta-object system to add behavior to data:
  • A meta-object system is a software framework that provides ways to add behavior to objects.
  • Use a meta-object system to add behavior to data so that you don't have to write the same code for every data set.
  1. Avoid garbage collection overhead:
  • Avoid using a garbage collector to reclaim unused memory if you can avoid it because the garbage collector has overhead that slows down the program.
  1. Reuse objects instead of allocating new ones:
  • To reuse objects, maintain a cache of things that get frequently used.
  • Enable garbage collection only after the cache has filled up since garbage collection is less expensive if the stock is entire.
  1. Create only the objects you need:
  • Create only the objects you need to reduce memory allocations and garbage collection overhead.
  1. Use language-specific techniques:
  • If possible, use language-specific techniques to avoid memory allocations that you can prevent in languages with control over memory allocation.

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