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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
You can see from the following chart which languages are most used for Data Science, Data Analytics, Big Data, and Mining.
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 visualization is the process of presentation of data in a graphical or pictorial format like charts, figures, bars, etc. Simply put, Data visualization is using pictures to represent data. Data visualization helps to discover patterns, trends, and correlations that might go unnoticed in a text-based data which helps the decision makers to make a fact-based decision.
You may ask, “But why to do data visualization when the same information might be available in excel tables or spreadsheets?” Well, it is better to represent the data in a picture format as ‘A picture is worth a thousand words‘ but more importantly, we can’t really see insights using spreadsheets. Another reason is that data visualization helps to process data faster.
For instance, suppose you are given a spreadsheet and a pictorial representation of the same data. Which one do you think gives you instant information quickly and beautifully?
But this is just a small amount of information. What about the large amount of information that you get from so many sources, i.e. Big Data? Data visualization helps to process and represent millions of data into a nice looking information providing picture.
Data visualization is a business analytics tool because it assists in data-driven or fact-based decision making. Decision makers can study through visualization and drill down to the details that they need really quickly. If a decision maker can’t drill down and the information is not readily available, they will have to ask the analyst for more information, which could take weeks or months.
Business Analytics (BA)
Before answering this question, think what you do before you make an important decision. You do some study, research, make notes, look at statistics and charts, compare things, and after all the calculations, you finally get some result that may help you to take a right decision. Like this, business organizations also look at their data, analyses them using various tools, techniques, and methodologies to produce a valuable result that would help the organization to take a right decision, predict things and grow.
So, Business Analytics (BA) is the combination of skills, technologies, applications, and processes used by business organizations to gain insight into their business based on data and statistics to drive business planning. Business Analytics is not only used by companies to make decisions and predict things but also to optimize and automate business processes and also to evaluate an entire company.
Business Analytics is used even more in such companies where it contains lots of data, for example, e-commerce, banks, etc. Such data-driven companies treat their data as a corporate asset and use it for competitive advantage. Business analytics is a vital part of any business.
Business analytics can be broken down into two parts – Business Intelligence and Statistical Analysis.
Business Intelligence involves examining past data to get an insight of how a business department, team, individual, product, project or the company itself, performed over a particular time.
Statistical Analysis involves predictive analysis by applying statistical algorithms to past data to make a prediction about future performance of a product, service or company itself.
R is one of the most popular choices of language to learn data science. Despite this fact, most beginners often find themselves lost or confused about finding out the right learning path. The internet is filled with data science and R learning materials which could be overwhelming at first.
Like the saying ‘Think Twice, Code Once’, if you have a well-guided layout, you can save time and effort. This blog will help you lay out a clear systematic approach to learning R for data science.
1. Download R and RStudio
The first thing you need is R, which is the core piece. R is free and open-source language and environment for statistical analysis. The next thing you need is RStudio, which is a nice integrated development environment (IDE) that makes it much easier to use R.
Download R from HERE
Download RStudio from HERE
If you open R itself, it will look very plain.
It is not necessary to open RStudio to use R, but having RStudio as your default IDE for R development makes R a little more user-friendly as it provides all the plots, package management and the editor in one place.
2. Get familiar with the RStudio IDE
When you first open RStudio, this is what you see.
The left panel is the console for R. Type 1 + 1, hit ‘Enter’ and R will return the answer.
It’s a good idea to use a script so you can save your code. Open new script by selecting File -> New File -> RScript and it will appear in the top left panel of RStudio.
It’s basically a text document that can be saved (File -> Save As). You can type and run more than one line at a time by highlighting and clicking the ‘Run’ button on the script toolbar.
The top right panel gives you information about what variables you’re working with during your R session.
The bottom right panel can be used to find and open files, view plots, load packages, and look at help pages.
3. Learn the basics
The next thing is to learn the basics of R. Try doing some math operations.
Create a variable
To create a variable in R, we use:
Variable <- Value
e.g. number <- 1
Learn about variable types
R has three main variable types – character, numeric and logical.
Learn about Grouping Data
Learn about Vectors, Lists, Matrix, Data Frames.
Vectors – contain multiple values of the same type (e.g., all numbers or all words)
Lists – contain multiple values of different types (e.g., some numbers and some words)
Matrix – a table, like a spreadsheet, with only one data type
Data Frames – Like a matrix, but you can mix data types
Learn about Functions
Functions are a way to repeat the same tasks on a different data.
e.g. x <- c (2, 4, 5, 6, 10)
Another example of a function is plot ().
To write a comment, type # in front of your comment. Comments do not get evaluated.
e.g. #This is a comment
4. Learn to access the help files
This will be the most useful tool. You should learn how to get the help of a particular function, search the help files for a word or phrase and find help for a package.
?function_name –> get help of a function
help.search (‘phrase’) –> search the help files for a word or phrase
e.g. help.search (‘weighted mean’)
help (package = ‘package_name’) –> find help for a package
e.g. help (package = ‘dplyr’)
5. Learn about R packages
R comes with basic functionality, meaning that some functions will always be available when you start an R session. However, you can write functions for R that are not the part of the base functionality and make it available to other R users in a package. Packages must be installed first then loaded before using it.
To install a package, click on the ‘Packages’ tab on the bottom right panel of RStudio and then click ‘Install’ on the toolbar.
Once you click on ‘Install’, a window will pop up. Now type the name of the package into the ‘Packages’ box, select that package, and click ‘Install’.
Now that you’ve installed the package, you still can’t use the function you want. You must load the package first. For this, you will have to use the library () function to load ‘dplyr’ package (for example).
You don’t have to download the package again once you close and re-open RStudio, but you do need to load the package to use any function of it.
6. Data Loading
Getting the data into R is the first step of the data science process. R has varieties of options to get data of all forms into R. This is a common list of packages best suited for data loading.
7. Exploring the data
Use the following code to obtain the data.
airquality is a data frame that comes inbuilt so that you can play with the data.
Try the following commands and see what you get:
Viewing the data
RStudio has a special function called View () that makes it easier to look at data in a data frame.
8. Data Analysis and Visualization
After learning how to get data into R, and some data exploration techniques, now it’s time to learn some exploratory analysis. Install a list of some wonderful R packages given below that helps to simplify data analysis and visualization.
· dplyr – helps you do simple and elegant data manipulation
· data.table – handles big data with ease, provides faster data analysis
· ggplot2 – awesome package for data visualization
9. Data Preparation
Data preparation is another important step because clean data is hard to find, and often needs to be transformed and molded into a form on which we can run models.
· reshape2 – melt and cast the dataset into the shape you want
· tidyr – provides a standardized way to link the structure of a dataset (its physical layout) with its semantics (its meaning)
· Amelia – missing value imputation
10. Communicate Result
You have learned to extract insights from data, but it could be useless without effective communication or display of result. R Markdown is a great tool for reporting your insights and share your findings with a fellow data scientist.