Linear algebra is one of the most important mathematical topics that is highly useful to do a good data science. Learning the basics of linear algebra adds a valuable tool set to your data science skill. Python’s NumPy has fast efficient functions for all standard linear albegra/matrix operations. Here we will see 9 important and […]
10 quick tips for effective dimensionality reduction
Dimensionality reduction techniques like PCA, SVD, tSNE, UMAP are fantastic toolset to perform exploratory data analysis and unsupervised learning with high dimensional data. It has become really easy to use many available dimensionality reduction techniques in both R and Python while doing data science. However, often it can be little bit challenging to interpret low […]
3 Basic Commands to Manipulate NumPy 2d-arrays
NumPy or Numerical Python is one of the packages in Python for all things computing with numerical values. Learning NumPy makes one’s life much easier to compute with multi-dimensional arrays and matrices. A huge collection of very useful mathematical functions available to operate on these arrays these arrays makes it one of the powerful environment […]
How To Separate a Column into Multiple Rows with in R?
I just came across a useful little function in tidyr called separate_rows(). Often you may have a data frame with a column containing multiple information concatenated together with a delimiter. For example, we might have data frame with members of a family in a column separated by a delimiter. Here is a pictorial representation of […]
How To Reshape Pandas Dataframe with melt and wide_to_long()?
Reshaping data frames into tidy format is probably one of the most frequent things you would do in data wrangling. In this post, we will learn how to use Pandas melt() function and wide_long_long() function to reshape Pandas dataframe in wide form to long tidy form. A data frame is tidy when it satisfies the […]