A Data Analyst is responsible for analyzing large data sets — both structured and unstructured.

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Programming

Database

- SQL & NoSQL

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Statistics

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Basic Mathematics

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Machine Learning

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ETL

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Data Visualization

Data Analyst extract and analyze data stored in databases.

You will also face questions about SQL and NoSQL databases in your data analyst interview.

You will work with SQL and NoSQL databases in your role as a data analyst.

Data analysts use statistics to gather, review, analyze, and draw conclusions from data.

The applications of Statistical methods in data analysis primarily involve the collection, description, analysis, and inference of conclusions from data.

Learn Essential Statistics Concepts to understand the fundamentals.

The applications of Statistical methods in data analysis primarily involve the collection, description, analysis, and inference of conclusions from data.

You'll need the statistical programming skills to clean, analyze, and visualize large data sets more efficiently.

The ETL process supports model development.

Data Analysts are expected to have the knowledge of ETL tools to automate the extraction, transforming, and loading processes for consolidating data from multiple data sources or databases.

The main reason data analysts need math is that high-value predictions improve decision making with an awareness of how choices affect outcomes.

Linear algebra has applications in machine and deep learning for predictive analytics, where it supports vector, matrix, and tensor operations.

Calculus is used to predict function and build the functions that train algorithms to accomplish their objectives.

Machine Learning supports data analysis process to generate better insights to encourage stronger decision-making.

Data analysts need to know how to build predictive analytics solutions that provide descriptive and projective results from data. These solutions are developed using existing data to generate results from future data.

Data Analysts use eye-catching, interesting charts and graphs to present their findings in a clear and concise way to assist business decision-makers at a glance.

You must also be able to explain your findings in a way that is understandable even to those who don’t have enough technical knowledge.

There are many ways you can build these skills to transition into an analytics career. It can be really helpful to take advantage of courses, books and other free resources.

If you are serious about data science, including SQL, Statistical Programming, and Data Visualization, we've got a few practical resources for you.