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Common data technologies used by business analytics students

Technology plays a major part in the modern business world. This is easy to grasp when we think of how common it is to see well-known tech which has been around for many years in the majority of firms now. From e-mail to social media, video calls and VoIP, there are many examples of how technology allows businesses across all industries to flourish.

As is to be expected, technology is always advancing and always developing, and many innovations have come to the fore recently which have had a positive impact on the corporate world. Alternative reality (AR) is a very good example to consider and something that lots of companies are beginning to use, as is virtual reality tech, which some industries are also exploring.

Perhaps the best example of business innovation through tech, though, is big data and the way experts in this field collect, store and analyze it to drive positive business decision-making.

What are Data-Driven Business Analytics?

In simple terms, business analytics involves trained professionals using the latest data technologies to seek out actionable insights from batches of information. As many businesses collect a lot of data each day, these datasets can often be large and require higher-level learning to dig into them. The sophisticated tools business analytics professionals use also require high-level training to operate properly.

Professionals in this field use a variety of concepts and techniques to help them derive key insights from the data they are considering, and there are several examples of these techniques.

  • Data visualization
  • Data warehousing
  • Predictive analysis
  • Prescriptive analysis
  • Data programming

By making full use of the relevant techniques and the right tools to power them, business analytics professionals are able to pass on key insights from the data businesses collect to decision-makers. This can then help the company in question make more accurate and more informed decisions in the future. It can also help firms get a better understanding of their customer base and pick up on trends in their sector.

How do you learn about Data Analysis for Business?

Data analysis for business requires expert knowledge to be truly effective. This means that anyone who is thinking of this career path needs to get the right qualifications behind them first. This enables aspiring business analysts to pick up skills that employers value and also get used to working with the kind of technologies that any future role may involve.

It is important, therefore, to study via a higher education program that prepares graduates to thrive in the world of business analysis. This naturally can include studying for a bachelor’s degree in the subject initially but can also involve picking up more advanced master’s level knowledge afterward.

St. Bonaventure University’s online Masters in Business Analytics is a popular advanced course and ideal for anyone thinking of carving out a career in the industry. With a 100% online mode of study and the chance to finish the program in only two years, students leave possessing the higher-level of knowledge and skills employers need to succeed, such as data warehousing and analytical programming.

Why choose a career in Business Analytics?

There are a number of very good reasons to choose a career in this sector. To begin with, business analysts are in demand and a valuable addition to any company’s workforce. It is thought that business analyst is the most sought after job in the USA, with a constantly rising need to fill specialist roles, such as AI expert, Data Scientist, and Programmer. As a result, it is clear to see this is a thriving industry.

In addition, business analysts can usually enjoy competitive rates of pay as part of their role. This is clear from the average salary for US business analysts, which is around $91,000. Working in this role is also ideal for anyone who enjoys investigating data, solving problems and making a real difference in their job. It is also a good choice if you enjoy maths and using the latest technologies in your daily role.

Which Technologies do Students use in their Business Analytics Studies?

Working with the latest software, tools and platforms is central to any business analyst’s position. It is, therefore, important for anyone planning to move into the industry to have an idea of the common technologies that students may use in a master’s level program in business analytics.

This ensures students go into any course fully prepared for what it entails and ready to do their very best. Using the most common technologies when studying also means students are ready to hit the ground running when finding work after graduation.

But what are some of the most widely seen technologies for business analytics students at master’s level?

Databases and Structured Query Language

One type of data technology that all students at this level are sure to encounter are databases. These digital applications enable data to be stored in an easily accessible format and enable analysts to create a secure place to store the data a business may collect. From this, they are then able to safely access this information to derive actionable insights from it. Common databases used in data analytics include BigQuery, Amazon Redshift and Snowflake.

One type of analytics database which is also very popular in the industry is MySQL. As you may guess, this specific database is based on structured query language (SQL for short). This language is important for data analysts to learn because it allows users to interact with different databases at the same time.

This is very handy if you need to work with data stored in various types of databases. SQL is a flexible language for data analysts to use and one that has advanced tools for analyzing information – despite it having an accessible learning curve. Although it may be comparatively simple to learn, SQL enables users to carry out in-depth data analysis and is taught on most high-level programs in this field.

Big Data Platforms

Big data platforms are specific online storage mediums used in the sector for medium to large datasets. These digital platforms are normally based in the cloud and allow aggregated datasets to be stored using the latest information management software tools. In addition, the latest data management hardware is also utilized by these platforms to enable larger sets of data to be safely stored.

Students taking a master’s level business analytics program usually gain experience by using big data platforms. This is because they are likely to use these platforms in any future role post-graduation, and they are widely seen across the business world. The most popular big data platforms that programs focus on include Apache Spark, IBM Watson Analytics and Microsoft Azure HDInsight.

Programming Languages

One very important aspect of working with data as a business analyst is programming languages. These coding languages are a must for professionals in this industry because they are used across a variety of tasks in the sector. This can include automating data cleanup, the organization of raw data, working on machine learning algorithms and creating databases.

Due to this, programming languages are certainly one type of data technology that students in higher-level programs will use in their analysis. Becoming familiar with the most commonly used languages across the data analysis world will be a key part of future success in their career. With these skills behind them, graduates are able to stand out to potential employers and also be well-prepared to enter the working world.

Although there are many different kinds of programming languages in data analysis that have their own specific purpose, there are several that are most commonly seen in business analysis programs:

  • Python: a very popular language that is perfect for analyzing data, replicability and extracting data.
  • Java: a multi-use language that is used for integrating analytical methods into databases and statistical analysis.
  • SQL: for working with databases, collecting information and creating databases.
  • Scala: ideal for those who work with very big sets of data.
  • R: for data visualization, statistical modelling and trend recognition.

Although this list does not include every programming language that students might come across, it includes some of the most widely seen in both education and business.

Machine Learning Libraries

Along with other business innovations like artificial intelligence, machine learning is a major trend in the corporate world. Much of this is because machine learning is a key part of analyzing data for business and involves machines/applications learning how best to do it as they go.

Over time, machine learning enables applications to collect, store and analyze information in ever-more effective ways. This enables business analysts to not only spend time on other key jobs but also access data from which it is easier to glean insights.

Of course, machine learning still needs humans to function and keep an overall eye on how machines involved with data are operating. An essential data technology that students usually learn about, therefore, is machine learning libraries. These represent a core part of the machine learning ecosystem and help analysts create, train and roll out models of machine learning.

By learning what machine learning libraries are, how they function, and the most popular ones to use, students are able to integrate machine learning capabilities into any future business in which they work. Common machine learning libraries include NumPy, Keras, TensorFlow and Pandas.

Statistical Software

These specialized software applications are widely used within business analysis and are another data technology students on master’s level programs use when studying. In simple terms, these are computer programs that have been designed specifically to analyze statistics.

Business analysts find them very useful for taking figures they have collected and using the powerful tools in this kind of software to dig into them. By doing this, they are able to look for what the statistics are telling them, connections between stats and any trends the numbers are showing. When performing statistical analysis with this specific type of software, it is common for business analysts to use techniques such as predictive analysis or statistical modeling. SPSS is the big name in this data tech niche, but other examples, such as Tableau or Excel, may also be something students are taught.

Cloud Computing Platforms

Cloud computing is a massive part of modern-day business and something that has had a huge impact on how many companies function. It essentially sees computing services delivered via the internet and on demand. Due to its popularity within the corporate world, cloud computing is something that business analysts will be expected to use in their role.

This means that cloud platforms are types of data tech that master’s students learn how to operate. This not only equips them with valuable experience in this area to take into future careers but also gives them data tech skills employers value. It also enables graduates to become familiar with a key aspect of modern business and use cloud platforms to their full capabilities when in post. The most popular platforms of this type students tend to learn about are Google Cloud Platform, AWS and Oracle.

Text Analytics Tools

Although a lot of focus when learning about data analysis may go on numbers and stats, digging deeper into what text information can tell businesses is also a key topic. As a result, tools that enable users to analyze this kind of data are a vital part of any good master’s level program for business analysis.

Also known as text mining software, these tools enable users to derive insights from all types of text information using natural language processing. This can include using sentiment analysis features, key phrase recognition and pattern recognition features.

This kind of software can also provide visual representations of text data for simpler analysis. The best text analysis tools can take data from channels, including surveys, e-mails, consumer reviews and phone calls. RapidMiner, NVivo and SAS are some of the most widely seen text analysis tools for students to learn about in this field.

Common Technologies help Master’s level Students Thrive

Technology is something that any working business analyst will use every day of their working lives. This naturally involves choosing the right tool for the right job and it is therefore crucial for anyone wanting to break into this industry to learn about the most common technologies within it. Studying a master’s level business analytics course is the best way to do this and this article shows just the kind of awesome tools students can experience on these programs.

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