Tamale  Moses

Tamale Moses

1622909460

Data Visualization — Pokémon Dataset Part-II

Welcome to the Next Post in the series of Data Visualization, & 2nd part of one of the most favorite and loved Cartoon of all time — Pokémon…

Gotta Catch ’em All…. (The Visualization Part -II of the dataset)😉

In 1st Part we saw some interesting graphs related to the Generation of Pokémon and tried to figure out relation of other fields with it. So continuing with this we are going to analyze few more graphs related to other fields…

Let’s begin with some re-overview of the dataset as a refresher to see what we are going to analyze…

Fig 2:- Description of columns of Pokémon Dataset

Fig 3:- Pokémon Data Overview

Starting with some graphs related to type of Pokémon…

Fig 4:- Count of Pokémon based on Primary Type

We have seen a similar graph in Part 1 of Pokémon Dataset at the time of analyzing Generation link on the Primary type of Pokémon.

We can see here Water, Normal, Grass, Bug, and Psychic are the more common Primary type for Pokémon whereas Flying seems to be a rare type for Type 1 Pokémon.

Now Let’s analyze what is the ability distribution for the type of Pokémon…

1.1 Attack VS Primary Type

Fig 5:- Count of Pokémon based on attack group and Type

We can notice here the major portion for each type is occupied by groups 41–60,61–80 & 81–100. Also, the attack group 21–40 which was seen as a major part of our analysis with generation in part-1 has a very little contribution for each type.

The point to note here is… Attack group 181–200 is present only for Bug and Psychic-type. Also, attack group 161–180 is present only for few types, and that too in very less count. Lastly, Flying-type has 1 each for 101–120, 21–40, 61–80 & 81–100 attack groups…

1.2 Defense VS Primary Type

Fig 6:- Count of Pokémon based on defense group and Type

Similar to Attack Group we can see Defense Group 41–60, 61–80 & 81–100 has a major share for all types… Also, 21–40 has a minor portion of all the types. We can also notice here that the top groups in range 161–200 do not have much Pokémon.

Apart from this highest defense is 201–300 which is present only for Bug and Steel Type. If we go back to the Attack group Bug-type had the highest Attack group also. If we see for Flying-type, it had 1 each Pokémon in different Attack group but here 3 out of 4 Pokémon has the Defense Group of 61–80 and 1 for 21–40.

So we can say Most of the Pokémon in each category has an average attack and Defense of 41–100.

1.3 HP VS Primary Type

Fig 7:- Count of Pokémon based on HP group and Type

Contrary to first 2 graphs, here only 41–60 & 61–80 groups have major portion for all type of Pokémon’s… Group 81–100 has only major portions in top 3 types. Also, we notice here that highest group 200–300 has only 2 Pokémon of Normal Type.

2nd and 3rd highest groups i.e. 181–200 and 161–180 also has only 1 and 2 Pokémon in Psychic and Water types respectively. Flying Type has 1 each for 21–40 and 81–100 group and 2 for 61–80, which denotes Flying Type seems to be rare but they do not have very rare abilities.

#pokemon #data-visualization #data-analysis

What is GEEK

Buddha Community

Data Visualization — Pokémon Dataset Part-II
Siphiwe  Nair

Siphiwe Nair

1620466520

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

Sid  Schuppe

Sid Schuppe

1617988080

How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation

Data Visualization — Pokémon Dataset

Welcome to the Next Post in the series of Data Visualization, one of the most favorite and loved Cartoon of all time — Pokémon…
Gotta Catch ’em All…. (The Visualization part of the dataset)😉

Without wasting much time let’s Jump into our Data Set and begin with visualization… Pikka Pikka….

#data-science #data-analysis #big-data #pokemon #data-visualization

Gerhard  Brink

Gerhard Brink

1620629020

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).


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

Tamale  Moses

Tamale Moses

1622909460

Data Visualization — Pokémon Dataset Part-II

Welcome to the Next Post in the series of Data Visualization, & 2nd part of one of the most favorite and loved Cartoon of all time — Pokémon…

Gotta Catch ’em All…. (The Visualization Part -II of the dataset)😉

In 1st Part we saw some interesting graphs related to the Generation of Pokémon and tried to figure out relation of other fields with it. So continuing with this we are going to analyze few more graphs related to other fields…

Let’s begin with some re-overview of the dataset as a refresher to see what we are going to analyze…

Fig 2:- Description of columns of Pokémon Dataset

Fig 3:- Pokémon Data Overview

Starting with some graphs related to type of Pokémon…

Fig 4:- Count of Pokémon based on Primary Type

We have seen a similar graph in Part 1 of Pokémon Dataset at the time of analyzing Generation link on the Primary type of Pokémon.

We can see here Water, Normal, Grass, Bug, and Psychic are the more common Primary type for Pokémon whereas Flying seems to be a rare type for Type 1 Pokémon.

Now Let’s analyze what is the ability distribution for the type of Pokémon…

1.1 Attack VS Primary Type

Fig 5:- Count of Pokémon based on attack group and Type

We can notice here the major portion for each type is occupied by groups 41–60,61–80 & 81–100. Also, the attack group 21–40 which was seen as a major part of our analysis with generation in part-1 has a very little contribution for each type.

The point to note here is… Attack group 181–200 is present only for Bug and Psychic-type. Also, attack group 161–180 is present only for few types, and that too in very less count. Lastly, Flying-type has 1 each for 101–120, 21–40, 61–80 & 81–100 attack groups…

1.2 Defense VS Primary Type

Fig 6:- Count of Pokémon based on defense group and Type

Similar to Attack Group we can see Defense Group 41–60, 61–80 & 81–100 has a major share for all types… Also, 21–40 has a minor portion of all the types. We can also notice here that the top groups in range 161–200 do not have much Pokémon.

Apart from this highest defense is 201–300 which is present only for Bug and Steel Type. If we go back to the Attack group Bug-type had the highest Attack group also. If we see for Flying-type, it had 1 each Pokémon in different Attack group but here 3 out of 4 Pokémon has the Defense Group of 61–80 and 1 for 21–40.

So we can say Most of the Pokémon in each category has an average attack and Defense of 41–100.

1.3 HP VS Primary Type

Fig 7:- Count of Pokémon based on HP group and Type

Contrary to first 2 graphs, here only 41–60 & 61–80 groups have major portion for all type of Pokémon’s… Group 81–100 has only major portions in top 3 types. Also, we notice here that highest group 200–300 has only 2 Pokémon of Normal Type.

2nd and 3rd highest groups i.e. 181–200 and 161–180 also has only 1 and 2 Pokémon in Psychic and Water types respectively. Flying Type has 1 each for 21–40 and 81–100 group and 2 for 61–80, which denotes Flying Type seems to be rare but they do not have very rare abilities.

#pokemon #data-visualization #data-analysis