Data science is a new and upcoming field that is attracting a lot of new talent and highly-paid positions. In short, data science is the field of analyzing large amounts of data and finding unique insights and patterns. To be a data scientist, you must be familiar with programming, data analysis platforms, unstructured data, and data visualization. In certain advanced applications, a data scientist may be required to understand machine learning and AI development. Let’s review the main requirements involved in learning data science.
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Education for Data Science
For the most part, data scientists have a high level of education. This is not to say you can’t become a data scientist without advanced degrees, but most data scientists do. The majority of data scientists have at least a master’s degree, and many of them have doctorates. As for bachelor degrees, most data scientists have accreditation in mathematics, statistics, computer science, and engineering.Without advanced degrees, a career in data science
can still be attained through self-education and professional certifications. Cybrary provides a wealth of free and paid resources for learning and certification. Be wary though; self-education is an uphill battle. You must dedicate yourself to learning your craft and building a portfolio of professional works. Attaining an advanced degree is the most well-trodden path towards becoming a data scientist. Once achieved, Cybrary can provide advanced professional instruction and resources towards data science.
Programming in Data Science
Programming is an essential skill for anyone seeking to enter the field of data science. Primarily, the R programming language is widely used by data scientists. The learning curve for programming in R can be pretty steep. It is used for statistical computing to analyze data. It is also popular among big data and data mining professionals. These fields are virtually identical to data science.Besides knowing R programming, Python
is very popular among data scientists. This language can be used to work with SQL tables and quickly write scripts that interpret and process large amounts of data. Similarly, SQL
programming is very valuable for data scientists. It is essential to be able to write detailed queries for SQL databases. SQL is used to store and organize data, so, naturally, data scientists make use of the structure and programming language. In summary, knowledge of R, Python, SQL are essential for a complete understanding of data science.
Applications and Platforms Used in Data Science
On top of programming knowledge, there are several programs and data analysis platforms that are widely used by data science professionals. One such program is Apache Hadoop, a platform used for quickly analyzing vast amounts of data. Hadoop is useful for working with data that is too great to be stored on the local system. It is also helpful in transferring data to different servers. It’s not completely necessary to be familiar with Hadoop, but it is very much preferred in many scenarios.Aside from Apache Hadoop, Apache Spark is another platform that is quickly becoming the primary technology for data science. It is very similar to Hadoop in that it is used to analyze large amounts of data. While Hadoop
works by reading and writing to the hard disk, Spark works by analyzing data in dynamic memory. Meaning Spark is much faster than Hadoop. Apache Spark can run a single device or be adapted to run on clusters and cloud computing platforms.Data visualization is another task that is necessary for the practices of data scientists. Data scientists must be able to quickly organize data into readable visual formats, such as charts and data maps. This organization is achieved through tools built specifically for data visualization; these include Matplottlib, ggplot, and d3.js. Tableau
software is also sometimes used in visualizing data. Data visualizing isn’t just useful for data scientists. It can be used to present data patterns to team members and publications.
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Advanced Technical Skills for Data Science
Besides programming and application-specific knowledge, many advanced technical skills are regularly employed by data scientists. One of these skills is being familiar with machine learning. Knowing how to create machine learning applications will differentiate you from other data scientists and give you a distinct advantage. Meaning, creating neural networks and programming special machine learning scenarios.In a similar vein, being familiar with artificial intelligence development can be useful when carrying out analysis in data science. Data science and AI are closely connected. For one, both make use of machine learning to process data. While data science is used to find patterns in data for making decisions, AI uses programmed intelligence to read data and make decisions. Much like machine learning, being familiar with artificial intelligence will make your credentials stand out when finding work in data science.