1. Data Cleaning and Preparation The ability to clean and prepare data is fundamental. This involves handling missing data, removing outliers, and ensuring that the dataset is in a usable format for analysis.

2. Data Analysis and Exploration Data analysts must use statistical methods and techniques to explore and analyze data, identifying patterns, trends, and relationships within the dataset.

3. Statistical Analysis A robust understanding of statistical concepts and methods is crucial for interpreting data and performing analyses that can inform decision-making processes.

4. Programming Knowledge of languages such as Python, R, or SQL is essential for manipulating data, automating tasks, and performing complex analyses.

5. Database Management Skills in managing and querying databases are vital for accessing and managing large datasets, using SQL for structured databases or NoSQL for unstructured data.

6. Creating Dashboards and Reports The ability to create interactive dashboards and detailed reports is important for presenting data insights in a clear and accessible manner to stakeholders.

7. Data Visualization Proficiency in data visualization tools and techniques, such as Tableau or Power BI, helps present data findings visually to enhance understanding and decision-making.

8. Machine Learning Understanding basic machine learning concepts and algorithms enables data analysts to apply predictive models and enhance their analysis with predictive insights.

9. Excel Mastery of Excel is crucial for data analysis, especially for handling smaller datasets, performing quick analyses, and creating pivot tables and charts.

Data Preparation tools :   

- Python, AirFlow, Java, Kafka, Spark, etc.

Data Visualization & Analysis tools :   

- Tableau, Power BI, R, etc.

DB :   

- SQL, MySQL, Oracle, SQL Server, Postgres, etc.