Data Science concepts

to mine the zettabytes of available data for business intelligence. I

three main career options:

Data Analyst = junior data scientist.  Data analysts don’t have the mathematical or research background to invent new algorithms, but they have a strong understanding of how to use existing tools to solve problems.

programming,
statistics,
machine learning,
data munging, and
data visualization.


Beyond technical skill, attention to detail and the ability to effectively present results

 they acquire, process, and summarize data. Data analysts are the ones managing the quality assurance of data scraping, regularly querying databases for stakeholder requests, and triaging data issues to come to timely resolutions. They also then package the data to provide digestible insights in narrative or visual form.





Data Scientist, and

—to glean insights from the massive pool of data available—a data scientist’s work requires more sophisticated skills to tackle a higher volume and velocity of data.

a data scientist is someone who can do undirected research and tackle open-ended problems and questions. Data scientists typically have advanced degrees in a quantitative field, like computer science, physics, statistics, or applied mathematics, and they have the knowledge to invent new algorithms to solve data problems.

y identifying hidden patterns in data (for example, highlighting surprising customer behavior or finding potential storage cluster failures

Whereas a data analyst might look at data from only a single source, a data scientist explores data from many different sources. Data scientists use tools like Hadoop (the most widely used framework for distributed file system processing), they use programming languages like Python and R, and they apply the practices of advanced math and statistics.

Data scientists essentially leverage data to solve business problems. They interpret, extrapolate from, and prescribe from data to deliver actionable recommendations. A data analyst summarizes the past; a data scientist strategizes for the future.


Data Engineer.

A data engineer builds a robust, fault-tolerant data pipeline that cleans, transforms, and aggregates unorganized and messy data into databases or data sources. Data engineers are typically software engineers by trade. Instead of data analysis, data engineers are responsible for compiling and installing database systems, writing complex queries, scaling to multiple machines, and putting disaster recovery systems into place. Data engineers essentially lay the groundwork for a data analyst or data scientist to easily retrieve the needed data for their evaluations and experiments.

As such, data engineers have deep knowledge of and expertise in: ● Hadoop-based technologies like MapReduce, Hive, and Pig ● SQL based technologies like PostgreSQL and MySQL ● NoSQL technologies like Cassandra and MongoDB ● Data warehousing solutions