Data analysis refers to the process of looking at datasets to get the underlying insights about the data, including the patterns in the data, and represent in form of charts and graphs, to help make the perfect decisions. It is one of the most essential sectors in any field dealing with data. This includes Machine learning, Data Science and Artificial Intelligence. Through Data analysis, different companies and organizations can see their areas of improvement, where their efforts paid off, their top customers, employees and much more. Not only does it apply in organizational performance but also in machine learning and data science sectors to help in choosing the best models to train the data and make predictions.

For a data anlyst to achieve a perfectly analyzed data, there are four steps involved.
1.Data collection
This is the first step and it involves collecting data from different sources such as a database, scarping websites, or even conducted surveys. Most of the data collected always contain inconcistencies and are often labeled as "dirty" data. After this the next step is always data cleaning.
2.Data cleaning
In this step, the data should be all in one format. To ensure this, the missing values should be filled, the duplicates within the data should be removed, and making sure that all the columns are in the consistent format. Python offers a number of libraries that can help in cleaning data. A cleaned data gives the perfect analysis presenting an accurate information.
3.Data Analysis
This is the main and last phase of the data analytics process. Depending on what is needed, the final analysis will vary. In most cases, the process gives out some interactive dashboards that allows users and stakeholders interact with them, getting a custom analysis in just simple steps. These charts always gives the underlying relationships, current trends, and future trends that are present within the data. There are a number of tools that can be used in the process of data analysis. Some of the commonly used tools include: Excel, PowerBI, Tableu, SQL and Python. After the charts and graphs from the analysis are created, they are always shared in a dashboard containing all of the information.

Skills
SQL is one of the essential skills for a data analyst. This allows the data analysts to interact with different databases, retrieving and updating the databases based on the needed activities. It can also be used in the data analysis process because through it some rows can be dropped, null values filled, and mathematical information about the data given. Python is the second essential tool needed. It is a wide programming language that can be used in different areas. In data analysis, the libraries such as Pandas, Numpy, Matplotlib, Seaborn and SkLearn are used. The libraries allow for mathematical insights information to visualizing the information. Once the data is cleaned, the softwares such as Excel, Tableu and PowerBI can be used for presentation. One can choose between the above three for creating their charts, and come up with one dashboard containing all of the information.

Conclusion
Mastering skills in SQL, Python, Excel, PowerBi and Tableu produces a perfect data analysis based on the data given by an individual. Regardless of the field an individual is interested in, data analysis can be the best way to ensure that the data that an organization or company has is transformed to allow them get the perfect insight and understanding of the data they have. In regards to machine learning and Artificial Intelligence, analysis can help come up with an idea on the best model to be used based on the graphs that are presented in relation to the data.