Overview

Almost every user of a computer or smartphone has faced a dilemma: crashes. Suddenly and for no discernable reason, a piece of technology can crash and not function properly. This has become increasingly prevalent (and even more frustrating) with the introduction and advancement of smartphones. Naturally, a motive for analytics has been created. Crash Analytics uses data that is generated from Crash Reporting to make inferences and draw conclusions about how, or why crashes occurred. Analytics can be used as a resource to stop the same types of crashes from occurring. Crash Reporting is an application that provides details in crashes and alerts its users that a crash has occurred. Its data also includes archived records of previous crashes, including the type of crash that incurred and what applications were affected. This allows for its users to understand what is going wrong with their application and will provide insight into possible solutions or fixes to the crash. Bugsnag is popular tool that can meet any companies Crash Reporting and Crash Analytics needs. Another tool that is downloadable for free is Crashlytics.

Why is it Useful?

Without proper guidance, an enormous amount of time can be spent on finding the root cause of a crash and then developing a solution. It is almost like finding a needle in a haystack. Also, without proper reporting and analytics, the same type of crash can plague an application. Therefore, developers would have to make the same solutions multiple times rather than fixing the issue at hand when it primarily occurs.

Common Issues

Applications that are created and coded perfectly will never crash. However, no application or software is ever originally created perfect. Also, they must be constantly updated to be compatible with other applications or software. The first reason why an application would crash would be errors in its original construction. Upon millions of lines of code, there are bound to be human errors within some. These errors in code may lead to an application crash if certain situations are met. To avoid these errors, multiple rounds of testing before release is crucial.

Another issue that may cause an application to crash are discrepancies in Wi-Fi. There can be issues and possible crashes when an application is being used on a unreliable and spotty network For example, if a user is accessing an application with only a 3G connection, they are going to have a much harder time than a user with lightning-fast Wi-FI connection. AN application needs to be designed and prepared for variable connections.

An application may also crash if it acceptable by a variety of devices or operating systems. Individual operating systems require different memory power to function. Also, if an application is available for more than one device, it needs to be tested thoroughly for each device as there can be vast discrepancies (especially between IOS and Android).

The last major issue that can plague an application and cause it to crash are updates. Application’s inherent need to be constantly updated can create problems for developers. In order to continue to be compatible with other applications, increase user experience, or just general bug fixes, an application needs to be updated. This involves editing the code and making additions. With every new addition, the application needs to be thoroughly tested again as unique issues may arise.

Tools

Many applications and tools have been created to develop Crash reporting and Crash Analytics. In this article, I will discuss two popular tools that are used.

Bugsnag: Bugsnag utilizes intelligent crash grouping, allowing its users to prioritize crashes and quickly identify the root causes of bugs. Bugsnag uses advanced analytics to categorize and group crashes, allowing you to comprehend errors by root. It also utilizes filters to give its users more control of the type of data you’d like to see.

Crashyltics: A free-to use crash reporting and analytics application. It has been named the number one SDK on IOS and Android.

2017 Statistics

For a long time, IOS devices were considered much more crash-resilient than their Android counter-parts. Although IOS devices still outperform Android devices, their applications are more susceptible to crashing. According to a study conducted by Blancco, new statistical data from 2017 is showing that IOS devices are now more likely to crash than Android devices. The crash application rate of iPhones in Q2 of 2017 was a high 54%; Android’s crash rate is only 10% in Q2 of 2017.

This increase in application crashes is most likely due to an increase in downloads for applications for IOS. In Q2 of 2017, both IOS and Android have reached 25 billion downloads of applications in their respective stores. That leaves plenty of opportunity for error. An alarming statistic for Apple is the increase in crashing rate from Q1 of 2017 to Q2 of 2017. The crash rate increased from 50% to 54%. If this trend continues, Apple will have a serious dilemma on their hands. Much of their profitability come from their Brand and it’s product’s consistent high performance. Increases in application crashes lead to frustration in user experience, perhaps pushing customers to competitor’s products. Android, however, has no problem. Their already-low crash rate decreased from 18% in Q1 2017 to a very low 10% crash rate in Q2 2017.

Apple has been extremely successful with its global branding of its products and has consistently had the highest-quality products. The increase in application crashes is concerning for Apple, who currently charges a premium for its products. At what point will Apple be forced to drop its prices due to increasing crash rates and decreasing consumer experience?

Conclusion

When considering any problem, it is critical to identify the cause and methods of relieving the issue. An issue such as an application crash is no different. A problem with an application could affect millions of users, therefore, solutions need to be made in a timely manner. If an application does not solve its issues quickly, its users may move on from it. Luckily, many Crash Reporting and Crash Analytics tools have been created to help application developers prevent these issues.

Bibliography

“IPhone’s high ‘failure rate’ gives Android the edge on reliability.” Cult of Mac, Cult of Mac, 1 Mar. 2017, www.cultofmac.com.

“Crash reporter.” Wikipedia, Wikimedia Foundation, 14 Dec. 2017, en.wikipedia.org.

Deveney, Kara. “The Top 5 Reasons Your Apps Are Crashing.” Information Security Buzz, ISBuzz News, 5 Sept. 2014, www.informationsecuritybuzz.com.

“Get powerful iOS crash reporting.” Bugsnag, Bugsnag Inc. , www.bugsnag.com.

“Crashlytics for Android – Fabric Summary.” Fabric, Fabric, fabric.io.

Bhalla, Ragini. “Results: Q2 2017 State of Mobile Device Performance & Health Report .” Blancco , Blancco Technology Group, 30 Nov. 2017, www.blancco.com.

Overview

Amazon Web Services offers a variety of tools and services that are tailored to meet the needs of it’s user’s cloud storage needs. AWS can create a new cloud storage from scratch or work with existing methods to refine the data storage and manipulation. Each user is unique, and Amazon has a tool that can meet even the most distinctive requests. Amazon Elastic File System (EFS) is a service designed to meet one of these unique needs. EFS allows for its users to store data in the Amazon cloud and be accessed for various applications. This data is able to be accessed by thousands of EC2 instances across multiple Availability Zones simultaneously. Data that is stored within the EFS cloud is automatically scaled and has no cap on storage. The advanced capabilities of EFS storage are pertinent to the increasing necessity and demands of cloud computing. As more Big Data is leveraged in today’s corporate landscape, the tools that make cloud computing need to be as advanced as possible and scalable.

Use Cases

EFS is extremely useful in storing massive amounts of data. This is because there are no limitations of the storage size and data entries are scalable. Each individual file that is uploaded to EFS can also be extremely large; single files can be 52 tebibytes. EFS should be used when a business is using extremely large data sets and has to process data that other inferior tools are incapable of doing. Examples of business functions that often create these massive amounts of data are web serving, content management, media processing workflows, Bid Data, analytics, and home directories. With it’s increased price, EFS offers much more use cases and capabilities than other similar products. EFS uses max performance modes, allowing it to process larger amounts of data at a faster rate. Its services are also available on a much larger geographical scale and area available in multiple of Amazon’s Availability Zones. EFS is not intended for an individual’s use. EFS was created to handle extremely large amounts of data and large workloads, too much for one individual to generate or handle.

EFS is integrated easily within pre-existing systems. It uses only standard I/O’s and API’s. If a business variably stores large amounts of data and removes them periodically, EFS is a good tool for this. It scales to perform the same whether files are added or removed, without disrupting applications. EFS is also a fully managed service, meaning the file storage infrastructure is managed within the tool. This saves its user time by developing and maintaining its own system.

Best Practices

In order to maximize control of data manipulation and storage, there are multiple best practices that users can utilize. The first practice is to ensure that EFS files are encrypted. Whenever entering data that must be encrypted, use Amazon’s KMS Customer Master Keys. These data keys do not store or manage the data, but protects it form unwanted intruders. The keys are created individually for every AWS account. The keys can also be updated every year for security purposes. In order to take advantage of this encryption and protecting your files, enable encryption for EFS files. When dealing with sensitive data, it is important to ensure that it is encrypted and accessible only by those whom it is meant for.

Challenges

The most challenging aspect of any cloud-based storage method is accessibility. Whereas other data storage methods often have access issues, EFS has a solution. Every object stored within EFS is copied, stored, and able to be accessed across multiple Availability Zones. Therefore, if there is a problem with one Availability Zone, the data will still be able to be accessed at another.

Also, EFS can be accessed by thousands of EC2 instances at the same time all while maintaining its integrity. This allows for multiple users across geographical distances to access the same data at the same time. The data is kept up-to-date.

Comparisons

There are multiple common cloud storage systems that Amazon has created. In this portion of the article, I will compare EFS to AWS Elastic Block Storage (EBS) and Amazon Simple Storage Service (Amazon S3). Amazon S3 is primarily used for static storage service. This type of storage is required by functions such as archiving or version management. EBS is primarily used for persistent storage. Amazon EFS is newer, and is fully-managed network file system. All three of these tools are designed to be efficient cloud-storage options. Both S3 and EFS have no limitations on their storage sizes. This is extremely helpful for storing Big Data and working with data analytics as massive amounts of data are usually generated. However, EBS has a maximum storage size if 16 TB. Therefore, it can only be used for smaller-scale operations. A cloud-based data storage system needs to be conveniently accessible for its users. Amazon S3 is accessed over the internet and its configuration is customizable by the user. Amazon EBS and EFS are accessed by EC2 instances. EBS can only be accessed by one instance at a time while EFS has much more accessibility. EFS can be accessed by thousands of EC2 instances across multiple Availability Zones at the same time. Much of the data that is collected and used today is sensitive and is needed to be protected by encryptions. EFS and EBS both use KMS-Managed Customer Master Keys to encrypt their files. S3, however, uses both server-side encryptions and client side encryptions.

Conclusion

Amazon Web Services has drastically increased the services and capabilities available for cloud based storage and computing. It continues to release tools that meet its customers’ expectations and needs. EFS is an easy to use tools that provides advanced capabilities that other tools do not offer. Although it is more expensive than other tools, EFs is cost-effective for corporations as it is extremely scalable and able to be accessed by thousands of instances simultaneously. This creates a common data source for a company that has many employees or spans large geographical regions.

Bibliography

“Amazon Elastic File System (Amazon EFS) .” Amazon, Amazon Web Services, Inc., aws.amazon.com.

Kovacs, Gali. “EBS, EFS, or Amazon S3: Which is the Best Cloud Storage System for You?” NetApp Cloud Solutions Homepage, NetApp, cloud.netapp.com.

“AWS Key Management Service Concepts.” Amazon, Amazon Web Services, Inc., docs.aws.amazon.com.

“How does Amazon EFS differ from Amazon S3?” CloudRanger, CloudRanger, 28 July 2017, cloudranger.com.

“Amazon EFS: How It Works.” Amazon, Amazon Web Services, Inc., http://docs.aws.amazon.com

Overview:

Advances in Big Data collection and the abundance of unstructured data have created a need for methods to increase the quality of data. Business is a complex and variable setting; data needs to be informative and of high quality to truly meet its user’s needs. Data Enrichment is the process of enhancing raw data to a level where it can be relied upon as a valuable resource. This improved data will have many more uses for an organization and is something that successful business have decided to invest heavily in. A myriad of organizations have adopted Data Enrichment tactics including: grocery stores, clothes outlets, sports equipment suppliers, and even governments. There are many popular tools that are available for businesses to use (for example: Lusha, Informatica, Experian, LeadGenius, Datanyze).

In business today, almost every successful corporation or enterprise uses Big Data and ultimately needs to deploy Data Enrichment to get the most out of their data. Raw data has the potential to transform how a business does business after being enriched. The potential opportunities that Big Data and Data Enrichment offer a business are too large to ignore. If a business were to shy away from the use of advanced analytics in today’s market, their competition would almost surely beat them. Competitors would have a much greater insight over their operations and may have seen helpful patterns.  Ultimately, Data Enrichment allows businesses to make well-educated decisions. By increasing accuracy in identifying customers, enhancing existing customer records, creating informative and interactive models, and personalizing customer experience, Data Enrichment opens a whole new world of opportunities for a business. It is an answer for businesses who are attempting to connect consumer’s activities with their intentions and previous behaviors. In other words, it allows for a business to better target consumers based on information that defines and categorizes them. Therefore, a business would be able to determine the target demographics and consumers they would like to reach easier and more accurately.

Often, the maximum potential of raw or unstructured data is not realized until the data goes through an enrichment process. Data Enrichment can be used to add value to data by enhancing existing data with further demographic, geographic, and psychographic information. By enhancing data to include this extra information, its quality is increased and is able to provide more potentially valuable insights. For example, demographic information that is added via Data Enrichment is useful for considerations regarding consumers and how their age, gender, or occupation affects their habits. Geographic data is useful for charting and mapping consumers actions based on geographical location and can be useful for segmenting markets. Psychographic additions to data can provide information to a business that allows them to personalize and uniquely tailor their approach to a potential consumer. Enrichment techniques are used to allow an organization to collect more data with fewer issues and make more personalized actions based on the data they have collected on a consumer.

How it Works

Using a Data Enrichment tool is not an overly-complex process. The Data Enrichment process begins after an entity has already extracted data and loaded it into an existing database or data warehouse. This data has not yet been processed and is completely raw. This data is still valuable but is not particularly useful until it has been enriched. Data Enrichment tools use machine learning to apply metadata to data sets and to categorize each entry of data properly. By categorizing data, connections between multiple entries are able to be made easier and patterns that had previously been hidden may reveal themselves. One of these machine methods is Batch Processing. Batch Processing is feeding a computer a data set to analyze and encouraging it to learn how data is categorized and what specific attributes make data unique. The Batch Processing system then creates a model in which to test its predictions on a small test portion of the data. Rather than a human going in to every single data entry and categorizing it by hand, the machine is able to learn from a small batch and apply its knowledge to an entire data set. The needs of Data Enrichment are unique to every organization. Some organizations would like for the enrichment to include simple typographic error corrections. However, other organization’s needs could be far more complex.

The first step in implementing a data enrichment process is to assess the quality of the data that is in an entity’s system already. Measure the data with key metrics (such as accuracy or integrity) to determine what the initial status of the data is at. After this initial assessment, an organization needs to decide on what type of process would best meet their needs. An organization needs to list their requirements and priorities of the data when considering which system they would like to deploy. Experienced data manipulators will know where it is possible to supplement data with other sources of data that can provide information that is not available in the current data set.

Great Tools for Data Enrichment

As the demand for Enriched Data grows and will only continue to do so, there are a variety of tools and services for businesses to choose from. These tools ensure that data has referential integrity, is up-to-date, and of high quality. There are hundreds of services available, some of the simpler Data Enrichment tools are even free to use. Here, I will list some great Data Enrichment tools available:

1) Lusha – A free Data Enrichment tool that has been used and trusted by many of the top companies in the world. Companies such as Accenture, Google, Microsoft, Amazon, Dell, and Box use this tool. All of its capabilities are even accessible through a Google Chrome extension.

2) Informatica – Over 5,000 organizations utilize Informatica to leverage their information assets.

3) Experion – Experion is a free Data Enrichment that uses over 900 data elements and the ConsumerView database to provide more consumer information.

4) LeadGenius – provides great scalable data verification methods. It allows its users to collect information on unlimited amount of potential consumers.

5) Datanyze – A tool that is integrated with social media platforms such as LinkedIn and Salesforce.

Bibliography

“What is Data Enrichment? .” Techopedia, www.techopedia.com.

“Free data enrichment tool .” Experian Data Quality, Experian, 3 Sept. 2015, www.edq.com.

“Informatica Data as a Service Dun & Bradstreet Data.” Informatica, Informatica Corporation, www.informatica.com.

Zupan, Jane. “All About Machine Learning in Cognitive Search.” Attivio, Attivio, 16 Feb. 2017, www.attivio.com

White, Wesley. “What is Data Enrichment? Improving Your Data to Add Value.” Consult Paragon, Paragon, www.consultparagon.com.

“Data Enrichment – Enhancing Online Data.” Semphonic Blogs, SemAngel, semphonic.blogs.com.

O’Neal, Andrew. “5 ways to use Clearbit Enrichment.” Clearbit, Clearbit, 18 Jan. 2017, blog.clearbit.com.

“Get Personal. Fast.” Lusha, Lusha, www.lusha.co/.

Overview

The Role of Testing in Agile

Agile software development is characterized by the software’s ability to adapt and transform to meet the ever-changing needs of a project. Due to these rapid changes, responsive feedback is needed to check the effectiveness of a program. Agile testing is examining for errors in software such as bugs or discrepancies in performance, all the while conforming to the characteristics of agile software development.

Testing in Agile is different than testing in other types of software development, especially the Waterfall software development method. The Waterfall method has rigidly defined stages with independent teams assigned to each stage. All work needs to be finished in one stage before advancing to the next. Developers need to pay particular attention to early stages as they cannot go back. Independent quality assurance teams use the following test metrics to ensure the software meets its original target: product quality, test effectiveness, test status, and test resources. Agile testing differs greatly from Waterfall because it takes advantage of cross-functional team and relies heavily on collaboration. Rather than assessing test metrics at the conclusion of every stage, testing is conducted throughout the whole development process.

The Role of Metrics in Agile

Open collaboration and using test metrics effectively are crucial for Agile testing. It is more efficient to assess performance dynamically as development progresses than to measure at the end of a stage. Key performance indicators measure how well a project is coming along and if the project will be completed on schedule. This will avoid confusion of progress and will allow for project managers to gauge whether or not efficiency is increasing during the projects duration. Tracking metrics also will expose and highlight any obstacles or room for improvement within a project.

What are Test Metrics?

In order to track a project and measure the effectiveness of its processes, there needs to be an organized and uniform method in place. To do this, project managers may utilize test metrics. Test metrics are quantitative measures regarding the quality of a project. These metrics are geared towards a specific attribute, and may reveal to its observers important realizations about a project. Test metrics may count number of defects within software, measure individual user’s statistics, or measure performance of software.

These metrics are extremely useful to project managers or lead software testers. They are able to aid in decision making processes. By estimating costs and effectiveness of current projects, decision makers are able to use this information to make more informed decisions on future projects. These metrics also highlight inefficiencies and areas where a project can improve, allowing project managers to redistribute resources to fix problems. By evaluating many aspects of software, test metrics are able to measure the quality of the software.

There are two main types of test metrics, known as Base Metrics and Calculated Metrics. Base Metrics are collected during the development and execution phases and is tracked throughout the entire life cycle of the testing process. An example of a Base Metric would be Sprint Burndown. Sprint Burndown measures the rate in which teams complete tasks and is represented graphically. The graph compares the actual rate of completion to the desired rate of completion. Progress is divided into periods of time called, “sprints”. This chart is relevant to testing as it reflects progress and efficiencies of each team and tracks each key feature of a project.

Another example of a Base Metric would be Velocity. Velocity measures the average work completion per team per sprint. It then compares the results from this analysis to the desired efforts. This metric is used in estimating the duration a project requires.

Calculated Metrics use data that is collected within Base Metrics to draw further assumptions. These metrics are designed to be useful in test reporting. An example of a calculated metric would be Code Complexity and Static Code Analysis. This analysis examines source code of a program, looking for possible risks such as lexical errors, syntax errors, or semantic errors. It compares the risks of code without running it to the complexity of the code. This is useful in ensuring that a program has adequate standards within its code.

Another example of a calculated metric would be Defect Cycle Time. This metric measures how long it took to fix a bug after it was found and had initially been worked on. This is compared to an ideal target time for resolving bugs. However, every bug in unique and the time taken to solve one will vary. This is useful as it will show Agile teams whether or not they are being efficient in solving bugs.

Best Practices for Agile Metrics

There are many ways to ineffectively use Test Metrics. However, there are guidelines that can be followed to ensure that these metrics are being used to their maximum effectiveness. Here are a few best practices:

1) Record Context

Without having more information to specify what a metric is referring to, the metric will only lead to confusion and inefficiency in tracking issues root causes. It is way better to know where a problem is occurring rather than just knowing there is a problem.

2) Use Multiple Metrics

A single metric may provide a lot of insight into issues within a project. However, if a project manager relies solely on one or few metrics, it will narrow their vision and may not give them a broad enough picture on the status of a project. Diversify with multiple metrics to address this issue.

3) Use Common Sense

Metrics are great at reporting on the status of projects, however, they should not be blindly followed. For example, while an increasing metric may be positive in one situation, it can be negative in another situation. Take into account the context of the metric and what the nature of the project is when selecting Test Metrics to use.

Closing Thoughts

Unfortunately in life, as well as software development, things don’t always go as initially planned. The Agile methodology accounts for these sudden changes in the life cycle within a project, therefore the testing methods need to be flexible and adaptable in multiple situations. Agile Testing allows for decision makers to access the effectiveness of a project and monitor areas that possibly need improvement.

Bibliography

“Test Metrics.” SeaLights, SeaLights, https://www.sealights.io/.

“Agile Testing Metrics.” SeaLights, SeaLights, https://www.sealights.io/.

“What Is Agile Testing?” What is Agile Testing | QAComplete, SmartBear Software, https://qacomplete.com/.

“Agile testing.” Wikipedia, Wikimedia Foundation, 20 Oct. 2017, en.wikipedia.org/wiki/Agile_testing.

“Agile Testing.” Www.tutorialspoint.com, Tutorials Point, 15 Aug. 2017, www.tutorialspoint.com/software_testing_dictionary/agile_testing.ht

Atlassian. “Five Agile Metrics You Won’t Hate The Agile Coach.” Atlassian, Atlassian, www.atlassian.com.

Kolluri, Suresh. “Important Software Test Metrics and Measurements.” LinkedIn, LinkedIn Corporation, 31 May 2016, www.linkedin.com.

Wolpers, Stefan. “Agile Metrics - The Good, the Bad, and the Ugly – The Startup – Medium.” Medium, The Startup, 11 Dec. 2016, medium.com

Cleff, Andy. “30 Metrics for Agile Software Development Teams.” Front Row Agile, Front Row Agile, 4 Nov. 2016, www.frontrowagile.com.

Overview

With the introduction of Big Data and advanced in data manipulation, many careers have been created to address the opportunities data brings. In this article, I will be discussing multiple careers regarding data, including their roles, differences, and recommendations. Innovation within the data industry is inevitable and skilled Data Engineers, Scientists and Analysts are necessary to handle these rapid advances. These careers handle the collection, storage, usage, and opportunities that arise from the use of data.

What is a Data Engineer?

Data Engineers are vital in the process of collecting and storing data. They’re responsible for prospecting potential data acquisitions, maintaining data storage architecture such as databases, and set processes regarding the data. Data Engineers must deal initially with raw data. This data is often unstructured and may contain errors. It is the duty of the Data Engineers to make sure this data is suitable for Data Scientist use and for other users. If the architecture housing the data does not meet the requirements for a business, the Data Engineer is not doing his job correctly. In order to make sure data is usable, various tools are necessary to merge multiple systems. If a Data Engineer successfully completes the noted responsibilities, the data will be of much higher quality and able to be used by other roles, such as a Data Scientist. Data Engineering is a lucrative industry, as the median income for Data Engineers is $90,932.

Data Engineer vs. Data Scientist vs. Data Analyst

Data Scientists are similar to Data Engineers, however, Scientists are more focused on researching the meaning of data. Engineers focus is maintaining and building the infrastructure for the data. Scientists receive data that has already been cleaned and improved in quality from the Data Engineers. From here, they are able to leverage big data and draw inferences using complex algorithms to find meaningful patterns or statistics within these large data sets. Data Scientists are able to utilize machine learning and other analytic programs to research solutions to business problems or to look for opportunities. After their analysis, Scientists present interesting or relevant findings to business decision makers.

Data Analysts provide potential reasoning for patterns that have appeared within data exposed by Data Engineers or Data Scientists. Generally, their role is not as technical as the other positions and will require less knowledge on multiple systems. Analysts are able to continue and build upon the inferences that Data Engineers and Scientists have developed.

How to get started

To begin a career as any of these data roles, it is important to have developed a set of skills. One must be knowledgeable on data architecture and databases. This includes in-depth knowledge of the capabilities of SQL. They should also be experienced with data warehousing tools and Hadoop based analytic tools. Finally, knowledge on multiple operating systems will be useful to a Data Engineer.

To be considered for a role as a Data Engineer, certification is helpful on top of a college degree. Most Data Engineers have degrees in a Computer Science related field, but there is room to specialize their knowledge. The process of becoming certified will teach you more about the profession and capabilities of modern technologies. There are multiple certifications available for those who wish to pursue a career in Data Engineering. Google offers enrollees to become a Certified Professional, ensuring to prospective employees that the individual is familiar with Data Engineering concepts and practices. IBM also offers a similar certification program. However, rather than focusing on general principles of Data Engineering, advanced knowledge on big data and engineering applications are ensured. Cloudera also offers a certification, however, it specifically regards ETL tools and analytics.

There are also a plethora of adequate online courses that can train an aspiring Data Engineer. To obtain advanced knowledge in Data Engineering, programs such as Udemy, EdX, and Memrise are good places to start the search. There are also online universities that offer roles in Data Engineering, Science, and Analytics such as Southern Methodist University, Syracuse University, Villanova University, and many more.

My recommendation

If you desire to become any of the three roles I have discussed in this article, it is a good idea to begin by getting a degree in a Computer Science related major. If you do not already have a degree, this is a great opportunity to pursue as this industry will only be increasing in size. If you already have a degree in a different major, there are many affordable online colleges that are flexible and may meet your requirements if you want to pursue one of these careers.

I also strongly recommend to pursue a certification in Data Engineering or Data Science if you want to follow one of those career paths. These roles require advanced knowledge on multiple softwares and technologies, not something you will get entirely out of your college degree. Even if you do actually have all of the skills and are knowledgeable on all of the subjects regarding these two careers, it is still a good idea to get certified. The certification will prove to employees that you actually know these topics and it look great when you are applying to a position. A certification could separate you from another candidate with an identical background, experience, and qualifications.

Closing Thoughts

Any type of data related field is a good idea to consider entering or researching more about for multiple reasons. The field is only growing with the introduction of more advanced analytic software and tools as well as larger sources of data. More sophisticated data mining techniques have yielded a world of possibilities for businesses. It requires a critical-thinker to be able to sort through and make sense of this data, therefore, I believe these jobs could not easily be replaced by machines or automated computers any time soon. In order to remain competitive in a rapidly growing world, one must adapt and hone their skillset to what will be in demand. To those who wish to become a Data Scientist, Engineer, or Analyst, the path is not easy. However, with hard work and the help of numerous certifications and courses, it is possible.

Bibliography

Willems, Karlijin. “Data Scientist vs Data Engineer.” DataCamp Community, DataCamp, 23 Feb. 2017, www.datacamp.com

Leven, Yaniv. “How To Become A Data Engineer: A Guide.” Panoply.io Blog , Panoply, 27 Apr. 2017, blog.panoply.io.

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“How to Become a Data Analyst .” Master’s in Data Science, Master’s in Data Science, www.mastersindatascience.org.

“Data Analyst vs. Data scientist vs. Data Engineer:” MockInterview, MockInterview, 30 Jan. 2017, mockinterview.co.