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Now a day’s companies have lots of data and needs to manage these data and needs to understood the importance of enforcing achievements of the goals defined by their business strategies through business intelligence concepts. It describes the insights on the role and requirement of real time BI by examining the business needs. The paper explores the concepts of BI, its components, emergence of BI, benefits of BI, factors influencing BI, technology requirements, designing and implementing business intelligence, and various BI techniques.

Business intelligence tools

There are different business intelligence tools which is used to give you the insights you need to achieve things like growth, resolve issues that are urgent, collect all your data in one place, forecast future outcomes and so much more.

SAP Business Intelligence
MicroStrategy
Datapine
SAS Business Intelligence
Yellowfin BI
QlikSense
Zoho Analytics
Sisense
Microsoft Power BI
Looker
Clear Analytics
Tableau
Oracle BI
Domo

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How to Learn Power BI Data Analysis and Algorithms

Get a hold over the language to be used for implementation – practice the basics till you are comfortable.
Understand and implement one algorithm at a time. You may not understand it completely in the beginning. Give it time. Do not get stuck at one place. Try something else and come back later. Get the intuition of what is going on behind the few lines of code written to implement it. With practice things will keep getting clearer. Keep reading about it from multiple sources.
Go through the quick learning videos from YouTube or other online resources.
Join or purchase any online courses that available in reasonable price, or get materials from our team. We are also providing good materials that help to learn basic to advance data science. You can contact us at realcode4you@gmail.com or call us at given number(Contact details on website menu).
Make extensive notes when trying to understand / watching videos – it helps with internalizing the information as well as with the review.
Understand limitations of each algorithm, if any.
Understand the usual application areas of each of the algorithms and why are they used there.
Try understanding how these algorithms differ from each other. Using a single problem statement and solving it using different applicable algorithms should help here.
Remember, the algorithms are just tools to solve problems. Don’t lose sight of the main problem statement during implementation.
Many times, simple implementations are good enough. Build a simple solution first quickly and then iterate - you may want to try different features, tuning the parameters and hyperparameters, different algorithms, stacking different algorithms together and so on. Make sure you try one thing at a time and not everything together since you would want to know what change made the algorithm(s) better or worse.
Explain what you have done to one person who knows about the algorithms in technical terms and to another who does not know the algorithms per se but can follow the problem and its solution logically. Gaps in understanding are best understood when explaining to others.
Learning is an iterative process – your first implementation may not be the best. It can be made better over time. Please be patient.

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Best Machine Learning Sample Projects 2023

Project 1(OpenCV): Domain- Entertainment

Company X owns a movie application and repository which caters movie streaming to millions of users who on subscription basis. Company wants to automate the process of cast and crew information in each scene from a movie such that when a user pauses on the movie and clicks on cast information button, the app will show details of the actor in the scene. Company has an in-house computer vision and multimedia experts who need to detect faces from screen shots from the movie scene. The data labelling is already done. Since there higher time complexity is involved in the

OpenCV Assignment Help, Face Detection Using OpenCV.jpg

Project 2: Statistical Analysis to Reducing Gender Inequality in Wages and Employment

Germany’s government is interested in reducing gender inequality, especially gender wage gaps and gender gaps in employment. They are considering the introduction of a set of
policies that incentivize firms to shrink gender inequality in working conditions. First, the policy forces firms to internally publish salaries of all workers, so discrepancies in salaries can be detected by workers themselves. Second, firms are incentivized to encourage salary negotiations. Third, firms are incentivized to offer childcare where needed for their employees to fulfill their duties.
The government has been made aware that, in parts of the United States, exactly these policies have been introduced and now asks you to evaluate the effectiveness of these policies in reducing gender inequality in wages and employment. The dataset genderinequality (provided in RData and csv formats) contains data on individuals in the U.S., some working in firms to which the new policies apply (these are the treated workers) and some working at firms to which the new policies do not apply (these are the untreated workers). The policies have been introduced in 2007 and we have panel data on workers for the years 2005 and 2010, i.e. before and after the introduction of the new policies.

Statistical Analysis to Reducing Gender Inequality in Wages and Employment.jpg

Project 3: ReneWind

Renewable energy sources play an important role in the global energy mix, as the effort to reduce the environmental impact of energy production increases. Wind energy is one of the most developed technologies worldwide and the U.S Department of Energy has put together a guide to achieving operational efficiency using predictive maintenance practices. Predictive maintenance means failure patterns are predictable and if component failure can be predicted accurately and the component is replaced before it fails, the costs of operation and maintenance will be much lower. ReneWind is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors. ReneWind is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors.

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Project 4: TRAIN&AHEAD PROJECT

Trade&Ahead is a financial consultancy firm who provide their customers with personalized investment strategies. They have hired you as a Data Scientist and provided you with data comprising stock price and some financial indicators for a few companies listed under the New York Stock Exchange. They have assigned you the tasks of analyzing the data, grouping the stocks based on the attributes provided, and sharing insights about the characteristics of each group.

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