Exploratory Data Analysis of IPL Matches

The dataset consist of data about IPL matches played from the year 2008 to 2019. IPL is a professional Twenty20 cricket league founded by the Board of Control for Cricket in India (BCCI) in 2008. The league has 8 teams representing 8 different Indian cities or states. It enjoys tremendous popularity and the brand value of the IPL in 2019 was estimated to be ₹475 billion (US$6.7 billion). So let’s analyze IPL through stats.

Objectives:

• To find the team that won the most number of matches in a season.To find the team that lost the most number of matches in a season.Does winning toss increases the chances of victory.To find the player with the most player of the match awards.To find the city that hosted the maximum number of IPL matches.To find the most winning team for each season.To find the on-field umpire with the maximum number of IPL matches.To find the biggest victories in IPL while defending a total and while chasing a total.

Data Preparation and Cleaning

Let’s start by reading the csv file to Pandas DataFrame.

import pandas as pd
ipl_matches_df = pd.read_csv('matches.csv')
```<iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://cdn.embedly.com/widgets/media.html?src=https%3A%2F%2Fjovian.ml%2Fembed%3Furl%3Dhttps%3A%2F%2Fjovian.ml%2Fbipinp2013%2Fipl-analysis%2Fv%2F47%26cellId%3D16&amp;dntp=1&amp;display_name=Jovian&amp;url=https%3A%2F%2Fjovian.ml%2Fbipinp2013%2Fipl-analysis%2Fv%2F47%26cellId%3D16&amp;image=https%3A%2F%2Fjovian.ml%2Fapi%2Fgist%2Fb666a87e630444ec8ba8cd07b2036301%2Fpreview%2F6c3cac496d90416ca563a7b0ac77e2f3&amp;key=a19fcc184b9711e1b4764040d3dc5c07&amp;type=text%2Fhtml&amp;scroll=auto&amp;schema=jovian" height="793" width="680"></iframe>

So there are 756 rows and 18 columns. 756 rows imply that there were 756 IPL matches held between 2008 and 2019.

#data-science #exploratory-data-analysis #indian-premier-league #ipl

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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

Exploratory Data Analysis in Few Seconds

EDA is a way to understand what the data is all about. It is very important as it helps us to understand the outliers, relationship of features within the data with the help of graphs and plots.

EDA is a time taking process as we need to make visualizations between different features using libraries like Matplot, seaborn, etc.

There is a way to automate this process by a single line of code using the library Pandas Visual Analysis.

About Pandas Visual Analysis

1. It is an open-source python library used for Exploratory Data Analysis.
2. It creates an interactive user interface to visualize datasets in Jupyter Notebook.
3. Visualizations created can be downloaded as images from the interface itself.
4. It has a selection type that will help to visualize patterns with and without outliers.

Implementation

1. Installation
2. 2. Importing Dataset
3. 3. EDA using Pandas Visual Analysis

Understanding Output

Let’s understand the different sections in the user interface :

1. Statistical Analysis: This section will show the statistical properties like Mean, Median, Mode, and Quantiles of all numerical features.
2. Scatter Plot-It shows the Distribution between 2 different features with the help of a scatter plot. you can choose features to be plotted on the X and Y axis from the dropdown.
3. Histogram-It shows the distribution between 2 Different features with the help of a Histogram.

#data-analysis #machine-learning #data-visualization #data-science #data analysis #exploratory data analysis

How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

• Building/collecting data
• Cleaning/filtering data
• Organizing data

#big data #latest news #how are data analysis and data science different from each other #data science #data analysis #data analysis and data science different

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

Exploratory Data Analysis is a significant part of Data Science

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious. Yet, a significant key part to any data science task as often as possible underestimated is the exploratory data analysis

In this post, you will discover **Exploratory Data Analysis **(EDA), the techniques and tactics that you can use and why you should be performing EDA on your next problem.

“Exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those we believe to be there.” — John W. Tukey

Construct a Relationship with the Data

Before you can model the data and test your hypotheses, you must assemble a relationship with the data. You can manufacture this relationship by investing time summarizing, plotting, and investigating genuine data from the domain. This methodology of investigation before modelling is called Exploratory Data Analysis.

In investing time with the data up-front you can fabricate an instinct with the data formats, values, and relationships that assist with clarifying observations and modelling results later.

It is called exploratory data analysis since you are investigating your comprehension of the data, assembling an instinct for how the underlying process that created it works and inciting questions and thoughts that you can use as the reason for your modelling.

The process can be utilized to once-over to verify the data, to distinguish outliers and come up with specific strategies for taking care of them. In investing time with the data, you can spot corruption in the values that may flag a flaw in the data logging process.

#data-analysis #data-science #exploratory-data-analysis #data-visualization #data analytic