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Languages

Languages of Data Science​

Criteria for Choosing a Language​

When deciding which programming language to learn for data science, consider the following:

  • Needs: What specific tasks or problems you need to solve.
  • Problems: The nature of the problems, whether they're related to the company, role, or the age of the application.
  • Target Audience: Who will use or benefit from the solution.
  1. Python
  2. R
  3. SQL
  4. Scala
  5. Java
  6. C++
  7. Julia

Additional languages with unique use cases:

  • JavaScript
  • PHP
  • Go
  • Ruby
  • Visual Basic

Roles in Data Science​

  • Business Analyst
  • Database Engineer
  • Data Analyst
  • Data Engineer
  • Data Scientist
  • Research Scientist
  • Software Engineer
  • Statistician
  • Product Manager
  • Project Manager

Introduction to Python​

Benefits of Python​

  • Clear and Readable Syntax: Easy to learn and write.
  • Large Community and Documentation: Extensive resources for beginners and advanced users.
  • Versatility: Used in various fields such as data science, AI, web development, and IoT.
  • Support from Large Organizations: Used by IBM, Google, Facebook, Amazon, and many others.
  • Scientific Libraries: Pandas, NumPy, SciPy, Matplotlib.
  • AI and ML Libraries: TensorFlow, PyTorch, Keras, Scikit-learn.
  • Natural Language Processing: NLTK.

Community and Inclusion​

  • Python Software Foundation: Governs and supports Python.
  • Diversity Efforts: Initiatives like PyLadies promote inclusivity.
  • Code of Conduct: Ensures a safe environment for all participants.

Introduction to R Language​

Open Source Vs Free Software​

  • Open Source (OSI): Business-focused, allows collaboration.
  • Free Software (FSF): Values-focused, allows private and commercial use.

Benefits of R​

  • Array-Oriented Syntax: Easier transition from math to code.
  • Statistical Knowledge Repository: Over 15,000 packages.
  • Integration: Works well with C++, Java, Python.
  • Organizations Using R: IBM, Google, Facebook, Microsoft.

R Communities​

  • useR
  • WhyR
  • SatRdays
  • R-Ladies

Introduction to SQL​

SQL Overview​

  • Pronunciation: "ess cue el" or "sequel".
  • Non-Procedural Language: Focused on querying and managing data.
  • Relational Databases: Manages structured data with relations among entities and variables.

SQL Elements​

  • Clauses
  • Expressions
  • Predicates
  • Queries
  • Statements

Benefits of SQL​

  • Direct Data Access: No need to copy data separately.
  • Interpreter Role: Acts as an intermediary between user and database.
  • ANSI Standard: Knowledge is transferable across different databases.

SQL Databases​

  • MySQL
  • IBM DB2
  • PostgreSQL
  • Apache Open Office Base
  • SQLite
  • Oracle
  • MariaDB
  • Microsoft SQL Server

Other Languages for Data Science​

Java​

  • General-Purpose OOP Language: Fast and scalable.
  • Data Science Tools: Weka, Java-ML, Apache MLlib, Deeplearning4, Hadoop.

Scala​

  • Functional and Object-Oriented Language: Runs on JVM, interoperable with Java.
  • Popular Program: Apache Spark (Shark, MLlib, GraphX, Spark Streaming).

C++​

  • Extension of C: Improves processing speed, system programming.
  • Data Science Applications: TensorFlow, MongoDB, Caffe.

JavaScript​

  • Web and Server-Side Language: Extended with Node. js.
  • Data Science Tools: TensorFlow. js, R-js.

Julia​

  • High-Performance Numerical Analysis: Compiled language, fast execution.
  • Applications: JuliaDB for large datasets.