Anuj Pal

Data Manipulation: Proficiency in tools and languages for data manipulation is crucial. This includes SQL for database querying and manipulation, as well as programming languages like Python for data cleaning, transformation, and analysis. Statistical Analysis: Data analysts should be comfortable with statistical techniques such as hypothesis testing, regression analysis, clustering, and time series analysis to extract insights from data and make informed decisions. Data Visualization: Skill in data visualization tools like Tableau, Power BI is essential for creating clear and informative visualizations that communicate insights effectively to stakeholders. Machine Learning: Familiarity with machine learning concepts and techniques enables data analysts to build predictive models and uncover patterns in data that can drive business decisions. Big Data Technologies: Understanding of big data technologies such as Big Query distributed computing frameworks is beneficial for handling and analyzing large volumes of data efficiently.