Data analysis skills such as statistical analysis, machine learning, and data visualisation are increasingly becoming among the most sort after in the job market.Organisations are seeking to leverage data and technology to propel themselves to success.So in this article we look into what it takes to become a pro data analyst.
So what is data analysis and why is it important?
It is the process of collecting, cleansing, and interpreting data sets to answer questions and solve problems for a business which helps ground business decisions with empirical data, allowing decisions to be made based on real world evidence. By extracting insights and deriving knowledge from data, businesses can enhance their decision-making processes.It is a great tool in predicting future trends.
What tools do you need as a data analyst?
You need mastery of these Top Data Analytics Tools:

1. Tableau
2. Excel or Google Sheets
3. SQL
4. Power BI
5. Python or R Types of Data Analysis Descriptive Analysis

This is a simple type of analysis that is not so in-depth. It looks at what has happened in the past. It includes two main processes: data aggregation, which includes gathering the data, and data mining, which includes discovering patterns in the data. It is simply an analysis that just determines and describes the "what.”

_Diagnostic Analysis
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This type of analysis explores the “why”. This type of analysis is where data analysts try to investigate the cause of why something has happened. If there is a drop in sales in September from a high in August, the analyst will try to figure out why such a thing has happened. Relationships are uncovered in this stage of the analysis.

_Predictive Analysis
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Just like the name suggests, predictive analysis tries to predict what is likely to happen in the future. In this type of analysis, data analysts start to come up with actionable, data-driven insights that the company uses to inform their next steps. It eliminates guesswork from key business decisions as future outcomes are based on historical data.

Prescriptive Analysis

Prescriptive analysis advises on the actions and decisions that should be taken. It is built upon predictive analysis, as it shows how a business can take advantage of the outcomes that have been predicted. It is very complex and sometimes even involves the use of computational modeling procedures and even machine learning.

Understanding and applying these methods and techniques empower data analysts to derive valuable insights, make informed decisions, and contribute to the strategic goals of organizations across diverse industries.