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What are the programming languages needed to be a data scientist?
My career related interest is to become a data scientist
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The role of a data scientist is multifaceted: Part mathematician, part computer programmer, and part storyteller, they play a critical role in the world of data-driven decision-making.
DATA SCIENTIST EDUCATIONAL
REQUIREMENTS
To become a data scientist you'll need a bachelor's degree in data science or a related field such as statistics, computer science, computer engineering or information systems. While a degree in computer science teaches you about computing systems, math, programming languages and database management, computer engineering focuses on systems engineering, cybersecurity and overall network architecture.
ACQUIRED SKILLS
Data scientists need to have several technical skills to help them perform their duties efficiently. Their technical skill set allows them to gather insight that can help them solve complex problems. Here are some of the technical skills you typically need as a data scientist:
• PROGRAMMING: Data scientists typically possess a strong knowledge of programming languages like Python, SQL and C++ to turn raw data into actionable insights.
• DATA VISUALIZATION: Data scientists use data visualization to translate data into a format that's easily understandable by other audiences. Data scientists visualize data through the use of tools such as Tableau, SAS and the R libraries, which allow them to create various visualizations such as histograms, scatter plots, relationship maps and pie charts.
• STATISTICS: Data scientists use several types of math to gain greater insight from the data they're analyzing. For example, they use statistics to perform mathematical computations to identify underlying relationships between two variables, find data anomalies or predict future trends based on past data trends.
• DATA WRANGLING: Data scientists use data wrangling to handle data imperfections. This means it allows them to prepare data for further analysis by transforming raw data from one format to another.
The path to becoming a data scientist is as exciting as it is rewarding. With data science permeating every sector and industry, the role of a data scientist has never been more crucial. Whether you're driven by intellectual curiosity, the promise of a lucrative salary, or the desire to make impactful data-based decisions, a data science career offers endless possibilities.
DATA SCIENTIST EDUCATIONAL
REQUIREMENTS
To become a data scientist you'll need a bachelor's degree in data science or a related field such as statistics, computer science, computer engineering or information systems. While a degree in computer science teaches you about computing systems, math, programming languages and database management, computer engineering focuses on systems engineering, cybersecurity and overall network architecture.
ACQUIRED SKILLS
Data scientists need to have several technical skills to help them perform their duties efficiently. Their technical skill set allows them to gather insight that can help them solve complex problems. Here are some of the technical skills you typically need as a data scientist:
• PROGRAMMING: Data scientists typically possess a strong knowledge of programming languages like Python, SQL and C++ to turn raw data into actionable insights.
• DATA VISUALIZATION: Data scientists use data visualization to translate data into a format that's easily understandable by other audiences. Data scientists visualize data through the use of tools such as Tableau, SAS and the R libraries, which allow them to create various visualizations such as histograms, scatter plots, relationship maps and pie charts.
• STATISTICS: Data scientists use several types of math to gain greater insight from the data they're analyzing. For example, they use statistics to perform mathematical computations to identify underlying relationships between two variables, find data anomalies or predict future trends based on past data trends.
• DATA WRANGLING: Data scientists use data wrangling to handle data imperfections. This means it allows them to prepare data for further analysis by transforming raw data from one format to another.
The path to becoming a data scientist is as exciting as it is rewarding. With data science permeating every sector and industry, the role of a data scientist has never been more crucial. Whether you're driven by intellectual curiosity, the promise of a lucrative salary, or the desire to make impactful data-based decisions, a data science career offers endless possibilities.