The klib package provides a number of very easily applicable functions with sensible default values that can be used on virtually any DataFrame to assess data quality, gain insight, perform cleaning operations and visualizations which results in a much lighter and more convenient to work with Pandas DataFrame.
Over the past couple of months I’ve implemented a range of functions which I frequently use for virtually any data analysis and preprocessing task, irrespective of the dataset or ultimate goal.
These functions require nothing but a Pandas DataFrame of any size and any datatypes and can be accessed through simple one line calls to gain insight into your data, clean up your DataFrames and visualize relationships between features. It is up to you if you stick to sensible, yet sometimes conservative, default parameters or customize the experience by adjusting them according to your needs.
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The genius in the world of science Albert Einstein once quoted :
“If you are given a problem to solve in 60 minutes then spend 55 minutes defining the problem and spend the next 5 minutes solving it.”
Well, whatever I did to date in the subject of Data Science, I would like to reframe the quote in a changed version for now as below:
“If you are given a dataset to perform analysis to create visualization and later apply the algorithms to train the models to generate the best performance, then spend most of the time in Data Preprocessing techniques to get cleaner and correct data.”
#data-preprocess #data-cleaning #data-analysis #data-preprocessing #data-mining
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Living in this modern age, we face thousands of data every day. When we start our day by waking up in the morning, the first thing we do for most of us is to check the smartphone to see “is there an important email that I haven’t read?” or we check our social media to see “is my friend having a birthday today?” or do we check the news on the smartphone to see “what’s hot today?” Those are all examples of the application of data. Can you imagine if you face raw data that has not been processed before?
“A simple definition could be that data preprocessing is a data mining technique to turn the raw data gathered from diverse sources into cleaner information that’s more suitable for work. In other words, it’s a preliminary step that takes all of the available information to organize it, sort it, and merge it.”
Raw data can have missing or inconsistent values as well as present a lot of redundant information. The most common problems you can find with raw data can be divided into 3 groups:
· Missing data: you can also see this as inaccurate data since the information that isn’t there creates gaps that might be relevant to the final analysis. Missing data often appears when there’s a problem in the collection phase, such as a glitch that caused a system’s downtime, mistakes in data entry, or issues with biometrics use, among others.
· Noisy data: this group encompasses erroneous data and outliers that you can find in the data set but that is just meaningless information. Here you can see noise made of human mistakes, rare exceptions, mislabels, and other issues during data gathering.
· Inconsistent data: inconsistencies happen when you keep files with similar data in different formats and files. Duplicates in different formats, mistakes in codes of names, or the absence of data constraints often lead to inconsistent data, that introduces deviations that you have to deal with before analysis.
If you didn’t take care of those issues, the final output would be plagued with faulty insights. That’s especially true for more sensitive analysis that can be more affected by small mistakes, like when it’s used in new fields where minimal variations in raw data can lead to wrong assumptions.
By now, you’ve surely realized why your data preprocessing is so important. Since mistakes, redundancies, missing values, and inconsistencies all compromise the integrity of the set, you need to fix all those issues for a more accurate outcome. Imagine you are training a Machine Learning algorithm to deal with your customers’ purchases with a faulty dataset. Chances are that the system will develop biases and deviations that will produce a poor user experience.
Thus, before using that data for the purpose you want, you need it to be as organized and “clean” as possible. There are several ways to do so, depending on what kind of problem you’re tackling. Ideally, you’d use all of the following techniques to get a better data set. This picture below will help you to understand the steps you can do in Data Preprocessing.
#machine-learning #data-transformation #data-preprocessing #data-reduction #data-cleaning #data analysis
The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.
Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.
#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt