An Extensive Step By Step Guide for Data Preparation

Introduction

Before we get into this, I want to make it clear that there is no rigid process when it comes to data preparation. How you prepare one set of data will most likely be different from how you prepare another set of data. Therefore this guide aims to provide an overarching guide that you can refer to when preparing any particular set of data.

Before we get into the guide, I should probably go over what Data Preparation is…

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What is Data Preparation?

Data preparation is the step after data collection in the machine learning life cycle and it’s the process of cleaning and transforming the raw data you collected. By doing so, you’ll have a much easier time when it comes to analyzing and modeling your data.

There are three main parts to data preparation that I’ll go over in this article:

1. Exploratory Data Analysis (EDA)
2. Data preprocessing
3. Data splitting

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1. Exploratory Data Analysis (EDA)

Exploratory data analysis, or EDA for short, is exactly what it sounds like, exploring your data. In this step, you’re simply getting an understanding of the data that you’re working with. In the real world, datasets are not as clean or intuitive as Kaggle datasets.

The more you explore and understand the data you’re working with, the easier it’ll be when it comes to data preprocessing.

Below is a list of things that you should consider in this step:

Feature and Target Variables

Determine what the feature (input) variables are and what the target variable is. Don’t worry about determining what the final input variables are, but make sure you can identify both types of variables.

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Your Data Architecture: Simple Best Practices for Your Data Strategy

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

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

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.

Introduction

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).

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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

An Introduction to Data Connectors: Your First Step to Data Analytics

Modern analytics teams are hungry for data. They are generating incredible insights that make their organizations smarter and are emphasizing the need for data-driven decision making across the board. However, data comes in many shapes and forms and is often siloed away. What actually makes the work of analytics teams possible is the aggregation of data from a variety of sources into a single location where it is easy to query and transform. And, of course, this data needs to be accurate and up-to-date at all times.

Let’s take an example. Maybe you’re trying to understand how COVID-19 is impacting your churn rates, so you can plan your sales and marketing spends appropriately in 2021. For this, you need to extract and combine data from a few different sources:

• MySQL database that details all the interactions your users are having with your product
• Salesforce account that contains the latest information about your current and prospective customers
• Zendesk account that has all support tickets raised by your customers

#data-analytics #data-science #data-engineering #data #data-warehouse #snowflake #data-connector #machine-learning

Data Preparation: The Case for Using Automated, ML-Based Tools

Data preparation has always been challenging, but over the past few years as companies increasingly indulge in big data technologies, data preparation has become a mammoth challenge threatening the success of big data, AI, IoT initiatives.

Unlimited data, but limited capacities have led enterprises to use data lakes – a new technology that stores all your data in its natural format.

Unlike data warehouses where the data is cleansed, prepared then stored, data lakes store data in its original form; unprocessed, unprepared, untouched.

In this piece, we’ll specifically talk about data preparation as the most critical challenge and how an ML-based data preparation tool or software can make it easier to process data in the data lake.

#big-data #data-preprocessing #data-quality #data-preparation #machine-learning #data-preparation-tools #latest-tech-stories #artificial-intelligence

How Has COVID-19 Impacted Data Science?

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.

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