Without a doubt, the technology sector is evolving and advancing at a rapid pace, making the current job market an increasingly challenging environment to compete in. With the emergence of new, highly advanced technological tools, the market requires professionals with the skills and experience to resolve complex business problems. This is where data analysts come in.
Businesses all over the world are using data analytics to drive their business strategy. In this article, we’re going to highlight the tools to learn if you want to stay ahead of the curve in business and propel your career exponentially.
SQL means Structured Query Language, which is a programming language used to store, retrieve, and manipulate information in a relational database. There can be more than one relational database, and all of them are stored in an even larger database called a data warehouse.
In a relational database, there is often more than one type of category of data, for example, a student’s name or address, therefore it needs to be organised and structured. This means that the data is represented in tables of rows containing different “objects” and columns containing various “features” that are clearly defined.
For example, if the DVD rental business wanted to know which customers paid more than 40 times for a movie, they could do that directly through a query instead of going through downloading data, processing it on excel or R, etc… Therefore, it is very agile to access and retrieve this information using SQL.
SELECT first_name, last_name
WHERE customer_id IN
GROUP BY customer_id
HAVING COUNT (payment_id) > 40);
Scripting with R, Python, or creating pipelines with RapidMiner
When executing tasks in data analytics, the need for automating the process is required instead of an operator manually completing each task individually as this is much more efficient. In order to do this, the method of scripting is implemented, meaning that programs written in scripting languages such as R or Python are used to carry out multiple tasks in run-time environments. Alternatively, you can use data science platforms such as RapidMiner, which does much of the work for you without the need for scripting or coding.
What is R?
R is a statistical programming language combined with free software for analytics and graphics, making it the sovereign of all scripting languages and an extremely powerful tool within the field of data analytics. It creates an environment in which a wide variety of statistical techniques can be implemented and presented.
“R has extensive and powerful graphics abilities, that are tightly linked with its analytic abilities.” J H Maindonald writes in his book, Using R for Data Analysis and Graphics
What is Python?
Python is a free and open source, lightweight, multi-model, general-purpose programming language. It’s renowned for its universality and is considered by many to be the second best programming language as it is extremely adaptive for any domain, leading to major companies like Google using it in their central applications.
What is particularly refreshing about Python is that although it’s notoriously difficult to get started with, because it’s not domain specific it attracts a wider and diverse audience. This then creates an even vaster…and friendlier network of people to call upon in times of need.
What is RapidMiner?
Unlike R and Python, RapidMiner is not a programming language and therefore does not serve the function of scripting, rather it is an all inclusive data science platform. You can use it throughout the whole data analytics process; from data preparation to machine learning, deep learning to text mining, predictive model deployment to visualisation.
RapidMiner almost eliminates all need for any coding because of its system of readymade templates for each process. The RapidMiner frameworks can be used to create analytical workflows with multiple “operators,” similar to a chain reaction, and can be extended using R and Python scripts. With its inbuilt structures and easy usability, RapidMiner is an extremely beneficial platform for a data analyst that can be used throughout each step of the data analytics cycle.
It seems that big data just keeps on getting bigger, meaning that data analysts need to somehow come up with a way to analyse it all… which is where machine learning comes into play.
Machine learning is a branch of artificial intelligence and a method of data analysis used to devise complex algorithms to automate the process of building analytical models. It uses data to identify patterns and learn from them in order to make decisions and forecast future trends in a more sophisticated way. Ideally, the more data that is provided, the more the machine can learn and therefore the more accurate and insightful forecasts it can make.
Unfortunately, this is not always the case, as a major component to be considered when modelling, and the primary limitation of machine learning, is data bias. There is always a risk of bias when modelling, especially when considering what you want to predict. A good example of this would be Amazon’s secret AI recruiting tool being scrapped due to it showing bias against women. This was simply due to the fact that the model was trained to observe data from patterns of resumes submitted in the last 10 years, all of which turned out to be from predominantly male applicants.
Big data analytics can be applied to any industry with huge amounts of data. For example, the healthcare system could drastically improve its patient care and potentially diagnose diseases through machine learning by gaining insights from correlations between medical protocols, medicine, patient symptoms, and outcomes.
Data Visualisation with Power BI
Data visualisation is the process of presenting and illustrating data clearly and interactively with the intention of helping people understand the implications of analytical findings, which might not be possible without seeing visually.
What is Power BI?
Power Business Intelligence is a free suite of analytics software created by Microsoft that provides data analysts the ability to create interactive visualisations with self-service business intelligence capabilities. The interface’s resemblance to Excel gives users an intuitive navigation and its extreme robustness allows you to create unique and personalised dashboards that completely encompass and reflect business objectives.
Some features of the software include the ability to aggregate data from hundreds of up-to-date and reliable sources, giving you access to deep and accurate insights easily. It simplifies the data preparation portion in order for you to analyse data swiftly and systematically. AND most importantly, its interactive visualisation tool allows you to create beautiful reports that communicate your results effectively and then publishes them online for all to see.
So… are you keen to learn more about these widely used data analytics tools?
At Ubiqum, we train students to become data analysts. Our Data Analytics & Machine Learning course covers the tools you need to advance your career and stay ahead of the curve in business. If you’re interested and want to know more, then contact us at email@example.com and we’ll be happy to answer any questions!