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Data Science and ML in Cricket

 Objective of the data science in cricket  - Increate team performance  - Maximiser winning chances  Here's a simplified version: --- The IPL has expanded cricket, increasing the number of matches and the amount of data collected. Modern cricket data analysis involves tracking various factors like player positions, ball movements, shot types, delivery angle, spin, speed, and trajectory, which makes data cleaning and preprocessing more complex. **Dynamic Modeling** In cricket, numerous variables must be tracked, including player actions, ball attributes, and potential outcomes. The complexity of modeling depends on the type of predictive questions asked. Predictive models become especially challenging when analyzing hypothetical scenarios, like how a batsman’s shot might change with different ball angles or speeds. **Predictive Analytics Complexity** Cricket decision-making often relies on queries like "how often does a batsman play a specific shot against a certain b...
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Data Mining

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Data Mining  Introduction  Data mining finds valuable information hidden in large volumes of data. Data mining is the analysis of data and the use of software techniques for finding patterns and regularities in sets of data. The computer is responsible for finding the patterns by identifying the underlying rules and features in the data. Databases used in Data Mining  Flat Files. Relational Databases. DataWarehouse. Transactional Databases. Multimedia Databases. Spatial Databases. Time Series Databases. World Wide Web(WWW) <script data-ad-client="ca-pub-6042744672336815" async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"></script> Copy code snippet
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Data Analysis

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Get your Database Simplified . Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusion and supporting decision-making. Tools  R Programming R is the leading analytics tool in the industry and widely used for statistics and data modeling  Tableau Public:  SAS:  Apache Spark  Excel  RapidMiner: KNIME  QlikView
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Agile Project Methodology

The agile software development emphasizes on four core values. Individual and team interactions over processes and tools Working software over comprehensive documentation Customer collaboration over contract negotiation Responding to change over following a plan
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Business Analysis

Business Analysis  Basic things to know about BA  Business Analysis  Analysis of Stakeholder Need Software Development Lifecycles Requirement Lifecycles General SDLC plus Waterfall Model Rapid Software Development (RAD) Incremental Model Spiral Agile
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Business Intelligence in Action

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Business Intelligence  What is Business Intelligence? BI(Business Intelligence) is a set of processes, architectures, and technologies that convert raw data into meaningful information that drives profitable business actions Why BI  Providing BI ready Data  Data Driven decisions Comparisons of multiple vendors  Risk Analysis  Controls on business  Business Opportunities Implementing an Effective Strategy Competitive market advantage Long-term stability What is BI  To understand the structure and the dynamics of the organization in which a system is to be deployed. To understand current problems in the target organization and identify improvement potentials. To ensure that the customer, end user, and developers have a common understanding of the target organization . Data Requirement  Trend Data  Data files  Current Data  Real-time Data  Department wise data  Data Mining  ...
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Pandas : Data Manipulation Techniques

Data Manipulation techniques using Pandas  Data Manipulation using Pandas  Boolean Indexing  Apply function  Impotting missing files  Pivot table  Multi Indexing  Crosstab  Merger DataFrames Sorting DataFrames Plotting (Boxplot & Histograme) Cut function for Binning  Nominal Data coding  Iterating over rows of a DataFrame
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Data Science Algorithms

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Algorithms   That you must know for your Data Scientist career  Following Algorithms are very important  K-means  Linear Regression. Logistic Regression. Decision Tree. SVM. Naive Bayes. kNN. K-Means. Random Forest. Dimensionality Reduction Algorithms  Gradient Boosting Algorithms  XGBoost LightGBM Catboost
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How AI playing role in Automation Testing

  Automated testing, enhanced by AI, has revolutionized how software quality is ensured. Here’s a breakdown of how AI is used in automated testing: 1. Test Case Generation:    - AI-Based Test Design: AI can generate test cases automatically by analyzing the code or application behavior. It can create scenarios that might not be immediately obvious to human testers, covering edge cases and complex user interactions. 2. Test Execution and Optimization:    - Smart Test Execution: AI algorithms can prioritize test cases based on code changes, usage patterns, or historical data. This ensures that the most critical tests are run first, optimizing test execution time and resources.    - Test Suite Optimization: AI can help in selecting the most relevant tests to run, reducing redundancy and focusing on tests that are more likely to find new bugs. 3. Defect Prediction and Analysis:    - Predictive Analytics: AI models can analyze ...
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How AI is transforming software engineering industry

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Data science has become an integral part of the software industry, driving innovation and efficiency across various domains. Here are some key areas where data science plays a pivotal role in the software industry:        1.  Product Development and Enhancement    User Behavior Analysis : Data science helps in understanding user behavior through data analytics, enabling software companies to tailor their products to meet user needs more effectively.    Feature Optimization : By analyzing data on feature usage, companies can determine which features are most valued by users and prioritize their development efforts accordingly.     2.  Predictive Analytics and Decision Making    Forecasting Trends : Data science enables companies to predict market trends, user demand, and potential challenges, allowing for proactive decision making.    Risk Management : Predictive mo...
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Business Analyst Roles and Responsability

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Roles and Responsibilities of Business Analyst  Business Analyst Roles and Responsibilities as below   Creating a detailed business analysis, outlining problems, opportunities and solutions for a business. Budgeting and forecasting. Planning and monitoring. Variance analysis. Pricing. Reporting. Defining business requirements and reporting them back to stakeholders. Tools for Business Analyst   SAP Business Intelligence SAP Business Intelligence offers several advanced analytics solutions including real-time BI predictive analytics, machine learning, and planning & analysis  MicroStrategy  Datapine  SAS Business Intelligence  Yellowfin BI  QlikSense  Zoho Analytics  Sisense\ What Actually BA Do ? Creating a detailed business analysis, outlining problems, opportunities and solutions for a business. Budgeting and forecasting. Planning and monitoring. Variance analysis. Pricing. Reporting. Defining business requirements and reporti...
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