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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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4 answers
Updated
Doc’s Answer
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.
I appreciate you taking the time to answer this.
Evans
Updated
Clement’s Answer
To pave your way towards becoming a successful data scientist, there are some key programming skills that you should definitely acquire:
1) Python
2) SQL
3) R
These are the fundamental tools of the trade. Also, getting to know Jupyter Notebooks and the Databricks platform can give you a significant edge, as they are widely used development environments in this field.
Remember, there's always room for growth! Other languages like Java, Scala, and software applications tools like SAS, Tableau, PowerBI can further broaden your horizons. However, the necessity of these additional tools and languages might differ from one company to another. So, keep learning, keep growing, and you'll surely make your mark in the data science world!
1) Python
2) SQL
3) R
These are the fundamental tools of the trade. Also, getting to know Jupyter Notebooks and the Databricks platform can give you a significant edge, as they are widely used development environments in this field.
Remember, there's always room for growth! Other languages like Java, Scala, and software applications tools like SAS, Tableau, PowerBI can further broaden your horizons. However, the necessity of these additional tools and languages might differ from one company to another. So, keep learning, keep growing, and you'll surely make your mark in the data science world!
Updated
Adrian’s Answer
As a Data Engineer there is a lot of overlap with Data Science. Languages I'd recommend are :
Python
SQL
Scala
R
Java
C++
Take one language at a time, practice, see code samples and exercises and follow along. Remember to make comments to learn why a particular section/function/library is used and why it's coded in a particular way. I'd start with Python, then move to SQL. Also, pay attention to the differences in different operating systems and data/database systems. For example, there are differences in SQL in Spark vs SQL in other RDBMS. Same applies to other languages.
Learn Python, I'd recommend PySpark (Python for Spark/Hadoop/Databricks)
SQL (I'd recommend Microsoft's T-SQL (available in SQL Server and Azure SQL) and standard ANSI SQL (compatible with all platforms)
Learn R
Python
SQL
Scala
R
Java
C++
Take one language at a time, practice, see code samples and exercises and follow along. Remember to make comments to learn why a particular section/function/library is used and why it's coded in a particular way. I'd start with Python, then move to SQL. Also, pay attention to the differences in different operating systems and data/database systems. For example, there are differences in SQL in Spark vs SQL in other RDBMS. Same applies to other languages.
Adrian recommends the following next steps:
Updated
Cary’s Answer
Hi Evan,
To embark on a successful data science career, you'll need to master several programming languages and tools. Here's a guide to the most frequently used ones in the data science realm:
1. Python
Snapshot: Python tops the list in data science due to its ease of use and flexibility.
Key Libraries: NumPy, pandas, Matplotlib, SciPy, scikit-learn, TensorFlow, and Keras are among the crucial ones.
Applications: It's perfect for data manipulation, analysis, visualization, machine learning, deep learning, and statistical modeling.
2. R
Snapshot: R is tailor-made for statistical computing and graphics.
Key Libraries: Essential libraries include ggplot2, dplyr, tidyr, caret, and randomForest.
Applications: It's used for statistical analysis, data visualization, and machine learning.
3. SQL
Snapshot: SQL (Structured Query Language) is a must-have for data manipulation and querying databases.
Applications: It's ideal for extracting, updating, and managing data housed in relational database management systems (RDBMS) like MySQL, PostgreSQL, and Microsoft SQL Server.
4. Julia
Snapshot: Julia is recognized for its high performance and is increasingly favored in the data science world.
Key Libraries: DataFrames.jl, Flux.jl, and Gadfly are among the essential ones.
Applications: It's used for numerical and scientific computing, machine learning, and data manipulation.
5. SAS
Snapshot: SAS (Statistical Analysis System) is a software suite for advanced analytics, business intelligence, and data management.
Applications: It's perfect for data manipulation, statistical analysis, and predictive modeling, especially in corporate settings.
6. MATLAB
Snapshot: MATLAB is primarily used in engineering and scientific research for numerical computing and algorithm development.
Applications: It's ideal for data analysis, visualization, and numerical computation.
7. Java
Snapshot: Java is frequently used in big data technologies and for building scalable systems.
Key Libraries: Apache Hadoop, Apache Spark, and Weka are among the crucial ones.
Applications: It's perfect for big data processing, machine learning, and enterprise-level applications.
8. Scala
Snapshot: Scala is often paired with Apache Spark for big data processing.
Key Libraries: Apache Spark and Akka are among the essential ones.
Applications: It's used for big data processing, distributed computing, and functional programming.
9. C++
Snapshot: C++ is used for performance-critical applications and in settings where low-level programming is necessary.
Applications: It's ideal for high-performance computing, algorithm implementation, and simulations.
Extra Tools and Technologies:
Apache Hadoop: For handling and processing large data sets in a distributed manner.
Apache Spark: For speedy, in-memory data processing.
Tableau / Power BI: For data visualization and business intelligence.
Jupyter Notebooks: For interactive computing and sharing code, data, and visuals.
Git: For version control and collaborative coding.
Learning Journey:
Kick off with Python or R: They are the most user-friendly and widely utilized in the data science community.
Master SQL: It's crucial for extracting and manipulating data from databases.
Dive into Julia or Scala: Choose based on your specific interests, such as high-performance computing (Julia) or big data (Scala).
Understand the Ecosystem: Familiarize yourself with important libraries and tools like pandas, scikit-learn, TensorFlow, Apache Spark, and Jupyter Notebooks.
Practice: Engage in real-world projects, join Kaggle competitions, and contribute to open-source projects.
By gaining proficiency in these languages and tools, you'll be well-prepared to tackle the diverse tasks and hurdles that come with a data science career.
To embark on a successful data science career, you'll need to master several programming languages and tools. Here's a guide to the most frequently used ones in the data science realm:
1. Python
Snapshot: Python tops the list in data science due to its ease of use and flexibility.
Key Libraries: NumPy, pandas, Matplotlib, SciPy, scikit-learn, TensorFlow, and Keras are among the crucial ones.
Applications: It's perfect for data manipulation, analysis, visualization, machine learning, deep learning, and statistical modeling.
2. R
Snapshot: R is tailor-made for statistical computing and graphics.
Key Libraries: Essential libraries include ggplot2, dplyr, tidyr, caret, and randomForest.
Applications: It's used for statistical analysis, data visualization, and machine learning.
3. SQL
Snapshot: SQL (Structured Query Language) is a must-have for data manipulation and querying databases.
Applications: It's ideal for extracting, updating, and managing data housed in relational database management systems (RDBMS) like MySQL, PostgreSQL, and Microsoft SQL Server.
4. Julia
Snapshot: Julia is recognized for its high performance and is increasingly favored in the data science world.
Key Libraries: DataFrames.jl, Flux.jl, and Gadfly are among the essential ones.
Applications: It's used for numerical and scientific computing, machine learning, and data manipulation.
5. SAS
Snapshot: SAS (Statistical Analysis System) is a software suite for advanced analytics, business intelligence, and data management.
Applications: It's perfect for data manipulation, statistical analysis, and predictive modeling, especially in corporate settings.
6. MATLAB
Snapshot: MATLAB is primarily used in engineering and scientific research for numerical computing and algorithm development.
Applications: It's ideal for data analysis, visualization, and numerical computation.
7. Java
Snapshot: Java is frequently used in big data technologies and for building scalable systems.
Key Libraries: Apache Hadoop, Apache Spark, and Weka are among the crucial ones.
Applications: It's perfect for big data processing, machine learning, and enterprise-level applications.
8. Scala
Snapshot: Scala is often paired with Apache Spark for big data processing.
Key Libraries: Apache Spark and Akka are among the essential ones.
Applications: It's used for big data processing, distributed computing, and functional programming.
9. C++
Snapshot: C++ is used for performance-critical applications and in settings where low-level programming is necessary.
Applications: It's ideal for high-performance computing, algorithm implementation, and simulations.
Extra Tools and Technologies:
Apache Hadoop: For handling and processing large data sets in a distributed manner.
Apache Spark: For speedy, in-memory data processing.
Tableau / Power BI: For data visualization and business intelligence.
Jupyter Notebooks: For interactive computing and sharing code, data, and visuals.
Git: For version control and collaborative coding.
Learning Journey:
Kick off with Python or R: They are the most user-friendly and widely utilized in the data science community.
Master SQL: It's crucial for extracting and manipulating data from databases.
Dive into Julia or Scala: Choose based on your specific interests, such as high-performance computing (Julia) or big data (Scala).
Understand the Ecosystem: Familiarize yourself with important libraries and tools like pandas, scikit-learn, TensorFlow, Apache Spark, and Jupyter Notebooks.
Practice: Engage in real-world projects, join Kaggle competitions, and contribute to open-source projects.
By gaining proficiency in these languages and tools, you'll be well-prepared to tackle the diverse tasks and hurdles that come with a data science career.