Data analysis- this refers to the process of extracting useful data from raw data. This is done in order to get useful information that can be used to make appropriate and important decisions. It involves use if various analysis tools to format the data.

What are some of the importance of Data Analysis?

1. It is essential in making appropriate and informed decisions for various businesses. When data is well organized and well presented it helps various stakeholders to avoid making decisions from guesses and also from an uninformed perspective. It therefore makes the decision making process easier.
2. Opens up various opportunities that had previously been assumed to not exist. By representing data in an organized format, trends can be identified and can be used bring new strategies and views to the organization that are unique

Due to the increase in various technological advancements in the current digital era the analysis of data has become very important. This has resulted into 'big data'.

Application of data analysis in various sectors

1.Healthcare-by analyzing patient records, patient diagnosis can be improved. It has also led to analysis of outbreaks and learning about their patterns and hence helping in handling the cases of the outbreaks when they occur.

2.Financial sector-by mitigating financial risks from analysis of trends before they happen. It has also helped in avoiding fraudulent attempts in the various processes.

3.Manufacturing sector-it has helped in ensuring that quality is maintained for the various products. It has also helped in allowing the various industries to be able to predict the times where the demand for their products is high and when low. This helps them to regulate their production.

4.Agriculture sector-It has helped in identifying helpful trends such as rainfall patterns that determine the timing of various activities such as harvesting and planting times.

Quantitative data analysis techniques

• Regression Analysis-is used to understand the relationship between a dependent variable and one or more independent variables. It is used in predicting outcomes and identifying trends.

• Monte Carlo simulation-it uses probability distributions and random sampling to estimate results.

• Factor analysis-identifies underlying factors influencing observed variables.

• Time Series Analysis-it analyzes data points ordered by time.

• Cohort analysis-studies groups with shared characteristics over time.

Data analysis Tools
Some of the analysis tools techniques include:

• Python-this is a flexible and versatile programming language. It can be used for data analysis and reporting. It has libraries like Pandas , NumPy, and Matplotlib that help in easy data representation and visualization of the data.

-R is a powerful programming language that is used for statistical computations. It also helps in statistical visualization and is used by statisticians.

-SQL(Structured Query Language)-This is essential for database management. It is useful for managing, storage and retrieval of data.