Category : Data Analysis

Exploratory Data Analysis (EDA) using Python – Second step in Data Science and Machine Learning

In the previous post, “Tidy Data in Python – First Step in Data Science and Machine Learning”, we discussed the importance of the tidy data and its principles. In a Machine Learning project, once we have a tidy dataset in place, it is always recommended to perform EDA (Exploratory Data Analysis) on the underlying data before fitting it into a Machine Learning model. Let’s start understanding the importance of EDA and some basic EDA techniques which are very useful.

What is Exploratory Data Analysis (EDA)

Exploratory Data Analysis or EDA, is the process of organizing, plotting and summarizing the data to find trends, patterns, and outliers using statistical and visual methods. It takes input data from a tabular format and represents it in a graphical format which makes it more human interpretable. It is an important step in a Machine Learning/Data Science project which should be performed before … More

Python use case – Resampling time series data (Upsampling and downsampling) – SQL Server 2017

Resampling time series data in SQL Server using Python’s pandas library

In this post, we are going to learn how we can use the power of Python in SQL Server 2017 to resample time series data using Python’s pandas library. Sometimes, we get the sample data (observations) at a different frequency (higher or lower) than the required frequency level. In such kind of scenarios, we need to modify the frequency of the given samples as per the frequency of the required outcome. Modifying the frequency of time series data using T-SQL query becomes a tedious task especially when we need to perform upsampling as we need to generate more rows than what we have in the sample dataset. The Python’s pandas module has in-built capabilities for frequency conversion. With the help of pandas resample method, we can increase or decrease the time series observation frequencies with only few lines of … More

Tidy Data in Python – First Step in Data Science and Machine Learning

Most of the Data Science / Machine Learning projects follow the Pareto principle where we spend almost 80% of the time in data preparation and remaining 20% in choosing and training the appropriate ML model. Mostly, the datasets we get to create Machine Learning models are messy datasets and cannot be fitted into the model directly. We need to perform some data cleaning steps in order to get a dataset which then can be fitted into the model. We need to make sure that the data we are inputting into the model is a tidy data. Indeed, it is the first step in a Machine Learning / Data Science project. We may need to repeat the data cleaning process many times as we face new challenges and problems while cleaning the data. Data cleaning is one of the most important and time taking process a Data Scientist performs before … More