{"id":7680,"date":"2019-06-20T14:53:10","date_gmt":"2019-06-20T21:53:10","guid":{"rendered":"https:\/\/www.springboard.com\/?p=7680"},"modified":"2023-07-12T01:51:21","modified_gmt":"2023-07-12T08:51:21","slug":"data-analytics-tools","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-analytics\/data-analytics-tools\/","title":{"rendered":"The Definitive List of Data Analytics Tools"},"content":{"rendered":"\n

Data analysts take raw input and make magic happen. This \u201cmagic\u201d process of making data more palatable (or more importantly, useful) is called the data pipeline. To get there, you need the right data analytics tools, but what are<\/em> the right tools?<\/span><\/p>\n\n\n\n

We\u2019ve curated a list of useful software\u2014an extended data analytics stack\u2014that data analysts should master to efficiently move data through the pipeline\u2014from raw data to clear insights. Some tools fit neatly into one section of the pipeline, such as data collection and mining. Others bleed into other parts of the pipeline. And others do it all, so they don\u2019t really fit into any one box. Nonetheless, all of them will help you do your job better.  <\/span><\/p>\n\n\n\n

Let\u2019s roll. <\/span><\/p>\n\n\n\n

Data Collection and Mining<\/span><\/h2>\n\n\n\n

Before you can conduct data analysis, you need data\u2014more data than you think you need. Sometimes you can find data sets that have done some of the work for you, and you\u2019ll want to mine those data sets for patterns. <\/span><\/p>\n\n\n\n

Here are some useful tools that can mine data. <\/span><\/p>\n\n\n\n

KNIME<\/b><\/a><\/h4>\n\n\n\n

KNIME is an open-source solution that has over 1,000 modules, several hundred useable demos, which covers all the data analytics<\/a> functionality,<\/span> and the largest repository of algorithms of any data analytics tool. It\u2019s great for mining data, but it has many other useful features as well.  <\/span><\/p>\n\n\n\n

Learn more:<\/b> Check out this video<\/a> overview of the KNIME Analytics Platform.<\/p>\n\n\n\n

RapidMiner<\/b><\/a><\/h4>\n\n\n\n

RapidMiner is an open-source platform based on visual programming. Its environment is best for machine learning and data mining experiments. It makes it possible to create analytical workflows in a short space of time. Better yet, RapidMiner is an entire data analytics stack in a single environment, allowing everything from data prep and machine learning to model validation and deployment. That being said, as the name suggests, RapidMiner does best for data mining-specific tasks.<\/span><\/p>\n\n\n\n

Learn more: <\/b>RapidMiner has a library of resources here<\/a>.<\/p>\n\n\n\n

Just as often as you must wrangle data you must collect data yourself. Not all of these tools will work all of the time. That\u2019s because there are as many ways to collect data as there are data types (surveys, interviews, research, etc.).<\/span><\/p>\n\n\n\n

For deciding this, it might help to think of data as an answer to a particular question\u2014an answer you\u2019ve got to translate. Say you want to know the answer to the question, \u201cHow are our customers using our products?\u201d Face-to-face interviews with customers could work, but it would probably be more time consuming than you can afford. A well-designed survey would probably do the trick. <\/span><\/p>\n\n\n\n

Here are a couple of tools for collecting new data. <\/span><\/p>\n\n\n\n

Typeform<\/b><\/a><\/h4>\n\n\n\n

Data analysts primarily use Typeform as a survey form tool. That\u2019s where it shines. It allows you to create custom forms to collect data. It integrates with Google Sheets and MailChimp and provides quick insights to make your job easier. <\/span><\/p>\n\n\n\n

Learn more: <\/b>These posts<\/a> will take you through the survey creation and analysis process.<\/p>\n\n\n\n

Heap<\/b><\/a><\/h4>\n\n\n\n

Sometimes collecting data is less direct than a survey. Heap allows you to collect behavioral data on customers by tracking events such as clicks, taps, changes, swipes, mouseovers, and much more. Along with data capture, it also provides insights into understanding customer behavior. <\/span><\/p>\n\n\n\n

Learn more:<\/b> This Q&A<\/a> will introduce you to what Heap can do.<\/p>\n\n\n

Get To Know Other\tData Analytics Students<\/h4>
\"Maura<\/a>

Maura Fields<\/p>

Data Analyst at Northeastern University<\/p><\/div>

<\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Gilles<\/a>

Gilles Ngomeni<\/p>

Sales Analyst at Medline Industries<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Bart<\/a>

Bart Teeuwen<\/p>

Global Business Analyst, Global Talent Intelligence (GTI) at Meta<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n

Data Storage<\/span><\/h2>\n\n\n\n

Once you\u2019ve collected your data, you need a place to store it. Sometimes this is simple, and it\u2019s handled with the collection software you use. Other times, especially when you\u2019re working with large amounts of data, you need more robust solutions. More often than not, though, data will be stored in one of two types of databases: a centralized, relational database (such as MySQL) or a decentralized database (such as HDFS). <\/span><\/p>\n\n\n\n

MySQL <\/b><\/a><\/h4>\n\n\n\n

You don\u2019t necessarily need to know how to write your own SQL to navigate a SQL-based database server such as MySQL. MySQL houses data in centralized, structured tables and, although there\u2019s no need for a user interface, you\u2019ll almost certainly have one available to you, such as PHPMyAdmin. Through tools like PHPMyAdmin, you can directly edit tables, import new data, export data, write custom queries, and more. <\/span><\/p>\n\n\n\n

Learn more: <\/b>This tutorial<\/a> tackles how to install MySQL, create databases and tables, and much more.<\/p>\n\n\n\n

HDFS<\/b><\/a><\/h4>\n\n\n\n

HDFS stands for Hadoop Distributed File System. HDFS operates on the premise that hardware is bound to fail, so rather than housing data in a centralized location, where the system might have a single point of failure, it uses many hardware components to store data. HDFS allows users to work with terabytes of data and, although MySQL databases can also work with terabytes of data, many cite performance issues when relational databases scale to this size. <\/span><\/p>\n\n\n\n

Learn more: <\/b>Get started with the HDFS Architecture Guide<\/a>.<\/p>\n\n\n\n

Amazon Redshift<\/b><\/a><\/h4>\n\n\n\n

Amazon Redshift is a data warehouse service for relational databases. It\u2019s primarily used for big data. Like Hadoop, it\u2019s a distributed framework, meaning data is distributed on different nodes (servers) connected on a cluster. <\/span><\/p>\n\n\n\n

Learn more:<\/b> This resource library<\/a> is a great place to start.<\/p>\n\n\n\n

Data Analytics<\/span><\/h2>\n\n\n\n

Once you\u2019ve got data collected and stored, you\u2019ll need to put it through its paces to comprehend it. In other words, you\u2019ll need tools to analyze it. <\/span><\/p>\n\n\n\n

Apache Spark <\/b><\/a><\/h4>\n\n\n\n

Apache Spark is an open-source processing engine designed specifically for data analytics. A big advantage is that it can easily be integrated with the Hadoop ecosystem.<\/span><\/p>\n\n\n\n

Spark is a useful tool for working on large data sets, particularly unstructured, static data. Furthermore, the addition of its own machine learning library makes Spark one of the best data analytics tools for machine learning tasks. It\u2019s also equipped with GraphX, a specialist API for graph computations.<\/span><\/p>\n\n\n\n

Learn more: <\/b>Here\u2019s a nice <\/span>beginner\u2019s guide to Apache Spark<\/span><\/a>.<\/span><\/p>\n\n\n\n

Microsoft Excel<\/b><\/a><\/h4>\n\n\n\n

What list would be complete without Excel, one of the most popular and useful tools, even among experts in more advanced systems. It has some advanced analytics options, such as time grouping and automatic relationship detection, as well as a plethora of visualization tools.<\/span><\/p>\n\n\n\n

We could just as easily categorize Excel as a data visualization tool, but its popularity and use across all segments of the data pipeline is why we\u2019ve chosen to spotlight its analytics capabilities. <\/span><\/p>\n\n\n\n

Learn more: <\/b>If you want to get more out of Excel, check out Springboard\u2019s blog post: <\/span>Excel Functions for Data Analysis<\/span><\/a>.<\/span><\/p>\n\n\n\n

Looker<\/b><\/a><\/h4>\n\n\n\n

Looker is an advanced analytics platform that can connect with relational databases like Google BigQuery or Amazon Redshift to create unique data models. You can refine these models to focus on key metrics and generate customized reports.<\/span><\/p>\n\n\n\n

Looker is a user-friendly tool that works well for startups and large enterprises alike. Its collaboration features make it excellent for customized data reporting.<\/span><\/p>\n\n\n\n

Learn more: <\/b>The <\/span>Looker website<\/span><\/a> provides demo videos and other resources to show how Looker works.<\/span><\/p>\n\n\n\n

Data Reporting and Visualization<\/span><\/h2>\n\n\n\n

Microsoft PowerPoint<\/b><\/a><\/h4>\n\n\n\n

You might think you escape Microsoft PowerPoint when you leave school. You probably never will, though. And you shouldn\u2019t. While PowerPoint won\u2019t do any heavy analytical lifting, it will help you communicate your insights. <\/span><\/p>\n\n\n\n

PowerPoint is a presentation platform first and foremost, so you should leverage that to maximum effect when analyzing data. <\/span><\/p>\n\n\n\n

Learn more: <\/b>Check out these tips to create <\/span>beautiful data science presentations<\/span><\/a>.<\/span><\/p>\n\n\n\n

Tableau<\/b><\/a><\/h4>\n\n\n\n

Tableau is one of the best data analytics tools on the market. As a user-friendly option, it\u2019s quite easy for beginners to get to grips with, offering Excel advocates a big step up in terms of visualizations and sheer data handling capacity.<\/span><\/p>\n\n\n\n

In addition to analyzing data, Tableau is very useful for visualizing data and creating interactive dashboards.<\/span><\/p>\n\n\n\n

Learn more: <\/b>This <\/span>guide to Tableau<\/span><\/a> explores a diverse range of advanced analytics techniques for business intelligence professionals.<\/span><\/p>\n\n\n\n

Microsoft Power BI<\/b><\/a><\/h4>\n\n\n\n

If you want to focus on data visualization, you\u2019ll struggle to find anything better than Microsoft Power BI. BI stands for business intelligence. Using this web-based tool, you can analyze trends in real-time from basically anywhere, which paves the way for greater agility in marketing. <\/span><\/p>\n\n\n\n

This is a powerful tool for data analysts, as they can evaluate data and compile interactive visual reports in a matter of minutes.<\/span><\/p>\n\n\n\n

Learn more: <\/b>Check this <\/span>step-by-step guide<\/span><\/a> to get the most out of Power BI.<\/span><\/p>\n\n\n\n

Chartio<\/b><\/a><\/h4>\n\n\n\n

Chartio is another superb business intelligence and data analytics tool. It\u2019s great for visualization of data across your stack, as it connects easily with Amazon Redshift and Google BigQuery and allows you to import CSVs. <\/span><\/p>\n\n\n\n

It uses visual SQL, which does what it sounds like. It helps you view and write SQL queries graphically, minimizing the effort it takes to write different queries for different flavors of SQL. Once you\u2019ve gotten your queries, you can output to one of 15 chart options in Chartio to gain insights. <\/span><\/p>\n\n\n\n

Learn more:<\/b> There’s a comprehensive set of video resources here<\/a>.<\/p>\n\n\n\n

Languages <\/span><\/h2>\n\n\n\n

Languages are a special case in the data analytics stack. They don\u2019t really fit neatly into one section of the pipeline because, in most cases, they perform well at multiple places in the process\u2014but they\u2019re not typically designed for, say, data mining alone. You don\u2019t necessarily need to know <\/span>any<\/span><\/i> language to be a successful data analyst<\/a>, but knowing a language or two can come in handy for some specific use cases. <\/span><\/p>\n\n\n\n

Related<\/i><\/b>: <\/span><\/i>Data Analysis With Python, SQL, and R<\/span><\/i><\/a><\/p>\n\n\n\n

SQL<\/b><\/h4>\n\n\n\n

Structured Query Language<\/a> is a programming language that works well for editing and querying information stored in a relational database. SQL can also be used for advanced analytical operations and for transforming the queried database\u2019s structure. You can add or delete tables of data, for example. There are open-source framework implementations of SQL, including the most popular one: MySQL. <\/span><\/p>\n\n\n\n

SQL remains one of the most popular tools used by data scientists. About <\/span>98%<\/span><\/a> of Fortune 100 companies use SQL for data analysis. Most data in the world is stored in tables that will require SQL to access. You\u2019ll be able to filter and sort through the data with it.<\/span><\/p>\n\n\n\n

Learn more:<\/b> W3Schools has an excellent <\/span>interactive tutorial on SQL<\/span><\/a> that will get you started on how to select parts of a database for further analysis.<\/span><\/p>\n\n\n\n

R<\/b><\/h4>\n\n\n\n

In recent years, R has risen above all other data analytics tools to become the go-to option for many companies. This is due, in part, to major developments that have turned R into a robust, versatile solution capable of handling massive data sets with relative ease. Better yet, in addition to some 8,000+ packages, R can integrate with a wide range of big data platforms, making it more appealing in an age where integration is a growing priority.<\/span><\/p>\n\n\n\n

As a procedural programming language, R effectively breaks any programming task into a logical series of steps. This makes it a fantastic tool to build data models with.<\/span><\/p>\n\n\n\n

Learn more: <\/b>Check out <\/span>this course<\/span><\/a> to learn more about data analytics with R.<\/span><\/p>\n\n\n\n

Python<\/b><\/h4>\n\n\n\n

Python is one of those languages that does just about everything, and as such, knowledge of it is a huge boon for data analysts. Python is relatively easy to learn, so it\u2019s best to start early in your career if you plan to transition into more advanced data roles such as data scientists. It\u2019s a powerful data analytics tool that has a host of mathematical and statistical functions. <\/span><\/p>\n\n\n\n

Due to its clear, easily legible syntax, Python is great for coding and debugging tasks. It\u2019s the go-to language for new programmers, but also a favorite of anyone performing statistical techniques.<\/span><\/p>\n\n\n\n

Learn more: <\/b>This <\/span>step-by-step guide<\/span><\/a> will show you how to do data analysis with Python.<\/span><\/p>\n\n\n\n

MATLAB<\/b><\/h4>\n\n\n\n

MATLAB is an all-in-one desktop environment and programming language that is a mathematician\u2019s dream. Of course, it\u2019s also great for data analysis. <\/span><\/p>\n\n\n\n

Learn more: <\/b>This article<\/span><\/a> makes the case for why data scientists (and data analysts by extension) should use MATLAB. <\/span><\/p>\n\n\n\n

The Most Important Tool<\/span><\/h2>\n\n\n\n

If you plan on making your career in data analysis successful, you\u2019ll want to be familiar with many of the tools in this stack, even if they don\u2019t ultimately become part of your day-to-day workflow. However, it\u2019s worth noting that there\u2019s one tool more important than any software on this list that you\u2019ve already got: your brain. <\/span><\/p>\n\n\n\n

Every data analyst needs to learn to <\/span>think like a data analyst<\/span><\/a>. Once you\u2019ve got that under control, regardless of what data analytics tools are in your stack, you\u2019ll make a successful career. In the meantime, don’t forget to check out these free public data sets<\/a> to get you thinking.<\/span><\/p>\n\n\n\n

Since you’re here…
<\/strong>Switching to a career in data analytics is possible, no matter your background. We\u2019ve helped over 10,000 students <\/a>make it happen. Check out our free data analytics curriculum<\/a> to gauge your interest, or go all-in with our Data Analytics Bootcamp<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Data analysts take raw input and make magic happen. This \u201cmagic\u201d process of making data more palatable (or more importantly, useful) is called the data pipeline. To get there, you need the right data analytics tools, but what are the right tools? We\u2019ve curated a list of useful software\u2014an extended data analytics stack\u2014that data analysts […]<\/p>\n","protected":false},"author":47,"featured_media":9439,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","_eb_data_table":"","footnotes":""},"categories":[134],"tags":[],"marketing_tags":[],"class_list":{"0":"post-7680","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-analytics"},"acf":[],"_links":{"self":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/7680"}],"collection":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/users\/47"}],"replies":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/comments?post=7680"}],"version-history":[{"count":3,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/7680\/revisions"}],"predecessor-version":[{"id":48159,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/7680\/revisions\/48159"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media\/9439"}],"wp:attachment":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media?parent=7680"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/categories?post=7680"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/tags?post=7680"},{"taxonomy":"marketing_tags","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/marketing_tags?post=7680"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}