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Data analysis involves integrated statistical, computational techniques to explore, generate insights and explore any given data through various programming languages to make work more effective and efficient. However, in this data-oriented era, R programming has extensively been used in statistical tools. 
In no particular order, below mentioned are a number of statistical analysis tools used in data analysis.
  1. SPSS - Popularly known as Statistical Package for the Social Sciences, It's extensively used in analyzing human behavior for comparative statistics, non-parametric and parametric analysis, and Graphing through a graphical user interface (GUI).
  2. R- R is the foundation of statistical computing, where it's used in other fields and human behavior. However, R requires some coding for various data processing requirements.
  3. Matlab - Matlab is a programming language and an analytical platform that is commonly used in engineering and statistical analysis.
  4. Microsoft Excel - On most occasions, Excel provides varieties of data visualization by generating customizable graphics and metrics summary.
  5. SAS - It's popularly renowned as Statistical Analysis Software with options for advanced statistical analysis through GUI options. SAS has a high capability of producing high-quality graphing and advanced statistical analysis.


We can't deny that in the current era we are at the moment, every operation is getting obsessed with big data. However, concrete data-driven data analytics conclusions can only be achieved in the following techniques.
  1. Mean: Popularly referred to as average is the most common technique used in data analysis in determining the overall trend of a given data set. This is through coming up with the central point of any data being processed.
  2. Standard Deviation: This is the measure of dispersion around the mean. Whether clustered or not, it's the dispersion of data points.
  3. Regression: It's the techniques that show the relationship between the independent and dependent variable.
  4. Hypothesis Testing: It's also called T-Testing, which tests whether an argument is true or false in a data set.


The educational arena has become dynamic with a consistent curriculum change to accommodate the technological changes we are experiencing globally. That's why today's data analysis tasks require in-depth knowledge of various statistical tools and their technicality. For that reason, below are some of the reasons driving scholars to seek data analysis project help.
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  5. For a better understanding of concepts under data analysis.
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