Most Important Programming Languages for Data Science

Most Important Programming Languages for Data Science

Many people have different opinions when it comes to choosing the programming language for Data Science projects depending on their career backgrounds and domain they have worked in.
There are many tools used for data analytics like R Programming, SAS, Python, Hadoop, SQL and others.

 

The following are the list of most important programming languages for Data Science.

  1. R

    R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

    R logo

  2. Python

    Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace. It provides constructs that enable clear programming on both small and large scales. Python features a dynamic type system and automatic memory management. It supports multiple programming paradigms, including object-oriented, imperative, functional and procedural, and has a large and comprehensive standard library.

    Python logo

  3. Hadoop

    Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model.Hadoop logo

  4. SAS

    SAS is an integrated system of software solutions that enable you to perform the following tasks: data entry, retrieval, and management, report writing and graphics design, statistical and mathematical analysis, business forecasting and decision support, operations research and project management, and applications development.SAS logo

  5. Scala

    Scala is a modern multi-paradigm programming language designed to express common programming patterns in a concise, elegant, and type-safe way. It smoothly integrates features of object-oriented and functional languages. Scala is object-oriented, statically typed, and extensible. Scala has many features of functional programming languages like Scheme, Standard ML and Haskell, including currying, type inference, immutability, lazy evaluation, and pattern matching. It also has an advanced type system supporting algebraic data types, covariance and contravariance, higher-order types (but not higher-rank types), and anonymous types.

    Scala logo

  6. Java

    Java is a general-purpose computer-programming language that is concurrent, class-based, object-oriented, and specifically designed to have as few implementation dependencies as possible. It is intended to let application developers “write once, run anywhere” (WORA), meaning that compiled Java code can run on all platforms that support Java without the need for recompilation. Java applications are typically compiled to bytecode that can run on any Java virtual machine (JVM) regardless of computer architecture. As of 2016, Java is one of the most popular programming languages in use, particularly for client-server web applications, with a reported 9 million developers.
    java logo

  7. RapidMiner

    RapidMiner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. It is used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process including data preparation, results visualization, model validation and optimization.RapidMiner logo

  8. SQL

    SQL (pronounced “ess-que-el”) stands for Structured Query Language. SQL is used to communicate with a database. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems. SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database. Some common relational database management systems that use SQL are: Oracle, Sybase, Microsoft SQL Server, Access, Ingres, etc. Although most database systems use SQL, most of them also have their own additional proprietary extensions that are usually only used on their system. However, the standard SQL commands such as “Select”, “Insert”, “Update”, “Delete”, “Create”, and “Drop” can be used to accomplish almost everything that one needs to do with a database.SQL logo

  9. Spark

    Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.Spark logo

  10. Tableau

    Tableau produces interactive data visualization products focused on business intelligence. Tableau products query relational databases, OLAP cubes, cloud databases, and spreadsheets and then generates a number of graph types. The products can also extract data and store and retrieve from its in-memory data engine. Tableau has a mapping functionality and is able to plot latitude and longitude coordinates and connect to spatial files like Esri Shapefiles, KML, and GeoJSON to display custom geography.Tableau logo

  11. KNIME

    KNIME is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. A graphical user interface and use of JDBC allows assembly of nodes blending different data sources, including preprocessing (ETL: Extraction, Transformation, Loading), for modeling, data analysis and visualization without, or with only minimal, programming. To some extent as advanced analytics tool KNIME can be considered as a SAS alternative. Since 2006, KNIME has been used in pharmaceutical research, it also used in other areas like CRM customer data analysis, business intelligence, and financial data analysis.

    Knime logo

  12. Scikit-learn

    Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.scikit learn logo

  13. TensorFlow

    TensorFlow™ is an open source software library for high-performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

    TensorFlow logo

  14. Julia

    Julia is a high-level general-purpose dynamic programming language that was originally designed to address the needs of high-performance numerical analysis and computational science, without the typical need of separate compilation to be fast, also usable for client and server web use, low-level systems programming or as a specification language.Julia logo

 

 

You can see from the following chart which languages are most used for Data Science, Data Analytics, Big Data, and Mining.

Chart

 

The following chart taken from KDnuggets will show you the poll of preferred language for Data Science in 2016:

Similarly, this chart will show you the poll of preferred language for Data Science from 2016-2018:

Python has gained more popularity if we are to see the difference from 2016 to 2018.

 

Data Scientist vs Data Analyst vs Data Engineer – What are the difference?

Data Scientist vs Data Analyst vs Data Engineer – What are the difference?

These are the terms most people often get confused with. Who are Data Scientists, Data Analysts and Data Engineers? Are they analogous to each other? What do they do? What are the roles of each individual? Which technologies do they use? What skills are required to become a data scientist, data analyst, and data engineers?

Data Analyst

As the title suggests, data analysts are the professionals who analyze data. Data analyst takes the raw data, cleans and organizes them, analyzes them, produces a valuable result and delivers it to the company which helps the company to make better decisions. Data analysts go by various other names depending on the industry like business analyst, database analyst and business intelligence analyst. Data analysts are responsible for cleaning and organizing data, performing analysis on it, creating visualization and presenting the result to internal team and business clients of the company. Data Analyst helps the company to make better business decisions in the future.

A data analyst needs to have an understanding of the skills like data visualization, statistics, data mugging, data analysis and should be familiar with the tools like  Microsoft Excel, SPSS, SPSS Modeler, SAS, SAS Miner, SQL, Microsoft Access, Tableau, SSAS.

Data Scientist

Data scientists are the specialists who are expert in statistics, mathematics, programming and building machine learning algorithms to make predictions and answer key business questions. They are an advanced version of a data analyst. A data scientist still needs to be able to clean, analyze, and visualize data but they will have more depth and expertise in these skills, and will also be able to train and optimize machine learning models. Data scientists are responsible for evaluating statistical models, building better predictive algorithms using machine learning, testing and continuously improving the accuracy of machine learning models, building data visualizations to summarize the conclusion of an advanced analysis.

A data scientist needs to have an understanding of the skills like machine learning, deep learning, neural network, statistics, predictive modeling, Hadoop, R, SAS, Python, Scala, Apache Spark and should be familiar with the tools like RStudio, Jupyter, Matlab.

Data Engineer

Data engineers are the designers, builders, and managers of the “big data” infrastructure. In simple words, data engineers clean, prepare and optimize data for consumption, so that once the data becomes useful, data scientists can perform a variety of analysis and visualization techniques to produce meaningful results. They also make sure the system is working smoothly. Data engineers work closely with data scientists.

A data engineer needs to have an understanding of the skills like database systems, SQL, NoSQL, Hive, Data APIs, Data modeling, data warehousing solutions, ETL tools and should be familiar with the tools like MongoDB, Cassandra, DashDB, R, Java, Python, SPSS.

Path to Learning Data Science Simplified

Path to Learning Data Science Simplified

Data Science is an intriguing field. In 2012, Harvard Business Review named data scientist the “sexiest job of the 21st century” and most recently, Glassdoor named it the “best job of the year” for 2016. As data is increasing at a rate more than ever before, the data scientist is also becoming one of the lucrative and fastest growing career options today.

To learn Data Science, you must first understand what data science is. If you’re not sure, here’s a simple introduction to data science. Just like any other thing to learn, you need to have curiosity, dedication, patience, and practice to learn data science and become a data scientist.

The following guidelines will help you to create a path to learning data science.

 

1. Learn Programming Language

Kickstart your journey of learning data science by getting familiar with a programming language. Data scientist must know how to manipulate the code to tell the computer how to analyze the data. Python, R, and SAS are a popular choice. Personally, I started with R programming. R and Python are both free and open-source programming languages whereas SAS is a commercial software and is expensive. You can choose either Python or R.

programming language data science

 

2. Brush up your Mathematics, Statistics and Probability skills

A good data scientist must be able to understand what the data is telling you. To do that, you must have strong knowledge of linear algebra, understanding of probability and statistics. Probability is also called the science of uncertainty. You should know the concept of probability for prediction and statistics for analysis of data and patterns that can solve the problem.

maths, stats, machine learning

 

3. Learn Database

Since data science is all about data, so obviously you are going to play with lots of data. Having a sound knowledge of database is very important. A database is used to store data. There are varieties of databases like MySQL, MongoDB, Cassandra, etc. I recommend you learn MySQL.

databases

 

4. Understand the concept of Machine Learning

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine Learning tools and techniques like k-nearest neighbors, random forests, etc. are mandatory to become a good data scientist.

machine learning

 

5. Learn data munging and data cleaning techniques

Data munging (also known as Data wrangling) is the process of converting “raw” data into another format that is easier to access and analyze. Data cleaning is the process of detecting and removing data in a database that is incorrect, incomplete, improperly formatted or duplicated.

data munging

 

6. Learn Data Visualization and Reporting

You must understand the basics of good data visualization and report. Data visualization involves the creation and study of the visual representation of data. You don’t need to be a graphic designer, but you do need to be well skilled in creating data reports that a layman like a manager or CEO can understand. Tableau, R Markdown, etc. are some tools which help in reporting.

data visualization tools

 

7. Skill up with big data

Big data refers to a large volume of data – both structured and unstructured, which are so complex that traditional data processing application software is inadequate to deal with them. Lean Hadoop, Spark. Hadoop is an open-source software framework for storage and large-scale processing of data-sets whereas Apache Spark is Hadoop’s speedy Swiss Army knife, which is a fast-running data analysis that provides real-time data processing function to Hadoop.

big data

 

8. Practice

As the saying goes ‘Practice makes perfect’, so keep practicing on getting better. Start by taking a simple project. Find some data or use data that you have. There are plenty of open data sources on the internet.

practice

 

9. Follow data science blogs and engage with the community

There are many data science blogs and communities out there but here are few that you must follow and keep yourself updated with. Also, don’t just be a consumer, try to contribute whenever you can by answering questions, or asking questions or contributing your projects on GitHub, which will be helpful to other enthusiasts.

R-blogger, Analytics Vidhya, DataTau, KDNuggets, Data Science Central are some websites that I recommend you to follow.

 

data science blogs