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Data Analysis

Conclude Your Business research

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. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.

Data Cleaning

Data cleansing or data cleaning is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data..If You ingested a bunch of dirty data, didn’t clean it up, and you told your company to do something with these results that turn out to be wrong. You’re going to be in a lot of trouble!.

Execel Dashboard

Excel dashboard, is one pager (mostly, but not always necessary) that helps managers and business leaders in tracking key KPIs or metrics and take a decision based on it. It contains charts/tables/views that are backed by data. A dashboard is often called a report, however, not all reports are dashboards. A dashboard, on the other hand, would instantly answer important questions such which regions are performing better and which products should the management focus on. These dashboards could be static or interactive (where the user can make selections and change views and the data would dynamically update).

R Analysis

R Analysis, is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please choose your preferred CRAN mirror.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

“ You can have data without information, but you cannot have information without data. A screen full of analytics data looks like a secret code, and in a way it is. That data has a lot of information in it, and it's impossible to make sense of it without the key...”

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes 1. an effective data handling and storage facility, 2. a suite of operators for calculations on arrays, in particular matrices, 3. a large, coherent, integrated collection of intermediate tools for data analysis, 4. graphical facilities for data analysis and display either on-screen or on hardcopy, 5. a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.