Data Science Software Students Should Be Familiar With

Being able to use programs and applications are imperative for success in data science. Programming is a requirement at almost every profession in the field, either for software development, building databases to extract and interpret data, or for analyzing statistics and probability. Even if some analysts will not be working with programming directly, they need to understand the process and be familiar with it. Examples of common programming software includes Java, Python, and R for statistics.

Benefits of Learning Software with Degrees

Being able to use programs and applications are imperative for success in data science. Programming is a requirement at almost every profession in the field, either for software development, building databases to extract and interpret data, or for analyzing statistics and probability. Even if some analysts will not be working with programming directly, they need to understand the process and be familiar with it. Examples of common programming software includes Java, Python, and R for statistics.

Many organizations will use one or multiple tools that are used to comb through raw data and visualize it, pulling specific information from a database, or organizing the flow of data through complicated networks and cloud-based storage. Many higher education programs will have courses that teach students these specific tools, but another way to gain competence with this software is to become certified. For example, there is the Cloudera Certified Professional (CCP) and Cloudera Certified Associate (CCA) that will give designation to individuals that are experienced with Hadoop and Apache Spark, offering educational material and an opportunity to pass an exam for certification.

Certification is perfect for those that have base knowledge in the field of data science, but do not have a degree and are looking to become engineers or a data analyst. Exams are offered at a premium, and in some instances, there are instructor-led courses to fully prepare for exams at a higher cost. Usually, individuals will need to pay the exam fee again to have another attempt if they are unsuccessful in passing. Remember to look at all restrictions and prerequisites that are needed before taking the exam, and determine where eligible testing centers are located.

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Examples of Data Science Software

Having a quick glance at programs and tools int he field of data science may look complicated and confusing, but there are a lot of similarities among them. They all have unique aspects and a major contribution in the study of data, and provide their own strengths and weaknesses in the field. In some instances, they can be combined together to further analyze information that could not be done by itself. For example, querying information with SQL can be implemented into Microsoft Excel spreadsheet files, or accessing SQL databases through programming languages.

Python

One of the two most popular programming options in the field of data science. Python has gained traction due to the simplicity of creating code and the helpful libraries it utilizes to do a variety of tasks. The program is able to create data visualization, algorithms for machine learning, statistical analysis, and more. Programming is object-oriented, and some examples of what it uses includes: strings, lists, sets, classes, functions, and numbers. All of this information is presented in a clean and truncated format when compared to other programming options, like C++ and Java. Attaching external libraries to the software provides even more use:

  • NumPy: Adds support for working with multiple dimensions.
  • SciPy: Specialized scientific functions, like orthogonal distance regression and linear algebra.
  • MatPlotLib: Additional data visualization features.
  • Pandas: Gives user more flexibility to work with datasets, such as writing and transferring information, manipulating and adding missing data.
  • Scikit-learn: Advanced statistical features and machine learning capability.

R

The other popular programming option in data science that is specialized for statistical analysis and data visualization. While Python has implemented features in performing these tasks, there are still more options available within R. There is a bigger learning curve when mastering R with its specific terminology. Similar to Python, external packages can be installed to enhance programming features, and adds features like predictive modeling and univariate or multivariate analysis. Data is typically entered and manipulated through a .csv file created by spreadsheet software, like Microsoft Excel. One of the most popular data visualization packages within R is ggplot2, which can add unique analysis features within a variety of traditional charts and graphs.

Java

Even though Java has been passed over in terms of popular, that does not mean Java has become a useless programming option in data science. A number of organizations use software that has been built on Java, and it is still used within popular big data programs like Spark, Hadoop, and Hive. There are many companies that list Java has a requirement or recommendation in their job listings. It may not be as simplified as Python, but most programmers will be able to break things down when looking through code. As a strongly-typed programming language, all data and variables must be defined specifically, making it ideal for data management and software development. Another popular language in big data, Scala, is built using the Java Virtual Machine, a unique tool that can be used on many platforms for better programming compatibility.

SQL

Abbreviated for Structured Query Language, this is a popular tool used to working with relational databases. Information can be added, pulled up (or queried), manipulated, or removed from an organization's database. Programming tools can do this feature as well, but SQL is the quickest way to complete basic tasks as it is focused on commands and syntax. By itself, SQL is not for programming, and has its own language geared toward working with structured data. For an example on how SQL works, instead of manually changing information on multiple pages of a spreadsheet file, this tool simplifies the process of finding and changing data. This makes stored information more accurate and less prone to inputting errors.

MySQL

A database management system that is open-sourced and one of the most popular variations of the tool, which is used on many websites. The main difference between SQL and MySQL is the former is the language itself when querying relational databases, and MySQL is the relational database. Other similarities to MySQL are Microsoft SQL Server, SQLite, and Oracle. Specific advantages of MySQL is storing data, displaying data, and updating the information. There are many tools to visualize this data, but the tool lacks when it comes to adding and removing information from the relational database. It is important to review various Relational Database Management Systems (RDMS) and determining what is best to use within the organization.

SAS

Previously known as the Statistical Analysis System, this stool was developed at North Carolina State University in 1966 and eventually formed the SAS Institute 10 years later. This is another data retrieval system that can gather information and organize it, change it, or remove it from a database. There are over 200 components that can be added onto the software in order to analyze and manipulate the information. Some of the tools within the software suite include visual analytics, detection and investigation, and machine learning. SAS has the ability to be output in various file formats for maximum compatibility.

The software suite can be found in a variety of industries, including finance, education, health care, manufacturing, and the public sector. By bringing advanced analytics to various companies, that have been able to optimize their processes to cut back on wasted time, being able to save money, and protect their information from outside intrusion.

Hadoop

Big data analysis is not handled through relational databases as the volume is just too massive to organize. This led to the development of Apache Hadoop, which is a collection of tools that creates the framework for storing and processing big data, and that includes HDFS (Hadoop Distributed File System), MapReduce, Spark, and YARN (Yet Another Resource Negotiator). The HDFS is the core of the platform and stores data in clusters. Each block holds 64 megabytes of data, and these blocks are replicated and broken up on different servers. This unique aspects is helpful when an organization experiences system failure. If one server goes down, information is automatically backed up and other places and can be retrieved.

MapReduce is the actual processing of data through its programming, reducing massive data sets and making them easier to manage and sift through. The name "MapReduce" refers to the steps it takes in order to do this. It first maps data by organizing it through key-value pairs it generates and tags. It is then reduced to a manage dataset. If data cannot automatically be tagged through MapReduce, then this process is not recommended for that large batch of information.

Hive

Built on top of the HDFS, Hive provides a centralized data warehousing solution that can query and analyze information. It has its own SQL language, called HiveQL, for real-time data processing with a goal to retrieve information much quicker than MapReduce. It is used by a number of tech giants: Amazon, Apple, Hulu, Netflix, and Slack, and was originally designed by Facebook. Because of the familiarity it has with SQL, it is not hard for analysts that understand querying to quickly adapt to Hive.

Spark is a querying application that analyzes big data in real-time. Its capability to perform streaming analytics makes it a fast alternative that can also utilize machine learning algorithms. This process can be done at data centers or through the cloud. Over time, Spark could replace MapReduce as its streaming abilities mature. Other solutions include Google’s Dremel and Cloudera’s Impala.

Google Analytics

Data within the Google Analytics platform can be used to create algorithms within machine learning and data mining. Libraries within Python and R can access the application programming interface, or API, of Google Analytics, and the rest of the process is similar to querying information from a database. There are some necessary steps to make the information more accessible, such as unsampling the data, but the data can later be stored into a database and transformed into various charts and graphs, or used within an algorithm.

Tableau

Tableau is a cloud-based platform solution that focuses on data visualization and has become part of Salesforce as of 2019. Initially, Tableau started at Stanford University and eventually was founded in 2003 in Mountain View, California. Various software products include the ability to query relational databases, spreadsheets, and databases within the cloud in order to create detailed graphs and charts. While the initial and recommended setup is to have Tableau host data needs, there is an ability for organizations to utilize Tableau on their own servers.

Information can be accessed in a variety of ways, including Tableau Mobile. This gives the organization an ability to look at data on the go with a convenient mobile app that is easy to manipulate and show certain aspects of data. Despite the smaller package, there is still full functionality when it comes to analyzing information and the application adapts to whatever device being used, such as a mobile phone or tablet. Because of the cloud-based technology Tableau uses, company information is secured by a number of vendors.