Great Learning brings you this live session on “IPL Data Analysis Using EDA”. In this session, you will be practically learning about the importance of data analysis using a real-world dataset that deals with the Indian Premier League. Data Analysis is the key requirement in today’s world to get insights about data that cannot be obtained unless the data is sifted through, analyzed, and the results actively used to solve practical problems. With this, it becomes extremely important that you know what data analysis is, where it is used and why it holds a key place in hundreds, if not thousands, of businesses across the globe. Keeping this in mind, the session aims to walk you through the practical implementation of data analysis with the popular IPL use case using the Python programming language. It always adds an immense amount of value to understand the concept that governs the field of Data Analysis practically.
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
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.
Let’s understand the different sections in the user interface :
#data-analysis #machine-learning #data-visualization #data-science #data analysis #exploratory data analysis
1. The Global Terrorism Database (GTD) documents more than 190,000 international and domestic terrorist attacks that occurred worldwide since 1970. With details on various dimensions of each attack, the GTD familiarizes analysts, policymakers, scholars, and journalists with patterns of terrorism.
2. The GTD defines terrorist attacks as: The threatened or actual use of illegal force and violence by a non-state actor to attain a political, economic, religious, or social goal through fear, coercion, or intimidation.
3. For each GTD incident, information is available on the date and location of the incident, the weapons used and nature of the target, the number of casualties, and — when identifiable — the identity of the perpetrator.
4. Compared to most types of criminal violence, terrorism poses special data collection challenges. In response, there has been growing interest in open source terrorist event data bases. One of the major problems with these data bases in the past is that they have been limited to international events — those involving a national or group of nationals from one country attacking targets physically located in another country. Past research shows that domestic incidents greatly outnumber international incidents.
1. In the proposed system, we are going to use the Global Terrorism Database to analyze and derive insights on the various terrorism acts that have taken place. We will preprocess the database to get reliable and accurate patterns and insights and use descriptive statistics to draw conclusions such as — major causes of terrorism, major groups behind terrorism, categories of the targets, locations of terrorist attacks.
2. Using EDA, we will show the trend of the growth of terrorism in the world and how it spread to various parts of the world. Using graphs, we study how one factor behind terrorism is interrelated to the other.
3. We also do predictive analytics as to what the magnitude of the terrorism acts can be in the future years.
OBJECTIVE OF ANALYSIS AND STUDY
The GLOBAL TERRORISM INDEX defines terrorism as “the threatened or actual use of illegal force and violence by a non-state actor to attain a political, economic, religious, or social goal through fear, coercion, or intimidation”.
The objectives of this project are:
· to study and analyse the different terrorism acts that have taken place in various parts of the world, at different times.
· the various causes behind terrorism acts
· different types of terrorism acts
· categories of terrorism acts
· distribution of terrorism acts
· statistical conclusions
EDA and DESCRIPTIVE ANALYTICS
1. Descriptive analytics is the interpretation of historical data to better understand changes that have occurred in a business. Descriptive analytics describes the use of a range of historic data to draw comparisons. A statistical method that is used to search and summarize historical data in order to identify patterns or meaning. Preliminary stage of data processing that creates a summary.
2. Exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. It’s where the researcher takes a bird’s eye view of the data and tries to make some sense of it.
#big-data #exploratory-data-analysis #data-science #data-visualization #descriptive-statistics #data analysis
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 –
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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