FAQ's

Data visualization is the graphical and pictorial representation of information and data.  Using tables, graphs and maps, data visualization tools provide a comprehensive method to understand trends, correlations or patterns in data.

Why is visualization important?

Data proliferation has made it difficult to manage and benefit from it.  Data visualization is essential to portray this massive amount of information and make data-driven decisions.  Of course, data is only as good as your ability to understand and communicate it, which is why choosing the right visualization is essential.

What are the main benefits of using visualization in data science?

There are several benefits to using visualization, including:

• Improved Insights. Data visualization allows us to spot data patterns, and correlations. Identifying these data relationships helps organizations focus on areas most likely to influence their most important goals.
• Better and Faster Decision Making. By using graphical representations of information, businesses can draw conclusions from large amounts of data. And since, it’s significantly faster to analyze information in graphical format, businesses can address problems in a timelier manner.
• Pinpoint emerging trends. By discovering trends visualization can provide businesses an edge over the competition, and ultimately affect the bottom line. It gives an easier path to identify outliers that affect product quality or customer churn, and address issues before they become bigger problems.
• Meaningful storytelling. Using visual elements with an engaging narrative, will get the message across to your audience.

If you would like to know some more then read about How JTA The Data Scientists does its work or have a look at some other FAQs.

You could also explore our case studies or whitepapers.

The benefits of big data can differ by industry. There are, however, common benefits from using big data.  For example, lower cost, reduced time and better competitive advantage.  Other benefits which may be possible include:

• Recognize opportunities
• Reduce customer churn
• More business insights
• Better planning and forecasting
• Identify the root causes of cost

Unfortunately, there are also challenges with big data:

Integrating Data Sources for Big Data

Big data comes from a lot of different places.  Applications, social media, email, employee-created documents and others. It is very difficult to combine all that data effectively.  Unfortunately, most machine analysis algorithms expect homogeneous data to work properly.

Data Inconsistency

Big Data usually has information from many sources. Furthermore, the sources may be of varying reliability. Much of that data is unstructured, meaning that it doesn’t come from a database. Documents, photos, audio, videos and other unstructured data can be difficult to analyze.

Data Storage

As data grows in volume we need real-time techniques to decide what should be stored.  It is often not economically viable to store all the raw data. Companies must be good at curating their data.

Staffing for Benefits of Big Data

Many organizations are still new to big data. The skill set is not the same as that for business intelligence and data warehousing, for which most organizations have developed their skills.

Privacy and Data Ownership

Managing privacy effectively is both a technical and a sociological problem.  Also, the value of the data owned by an organization becomes important. Organizations are concerned with how to leverage this data, while keeping their data advantage.  Questions such as how to sell data without losing control are becoming important.

If you would like to know some more then read about How JTA The Data Scientists does its work or have a look at some other FAQs.

You could also explore our case studies or whitepapers.

Big Data is more than just a large volume of data. It is a technology that allows you to capture, store, process, analyze and discern value. For example, Big Data allows one to acquire new knowledge at high speed.

The main characteristics inherent in Big Data are volume, variety and velocity. We call these three characteristics the three Vs:

• Volume refers to the quantity of generated and stored data
• Variety refers to the type and nature of the data, and
• Velocity refers to the high speed at which the data is processed

However, there are researchers who claim that the three Vs are a too simplistic view of the concept. Possible new Vs are:

• Veracity which refers to data quality and value, and
• Value which refers to the economic value of the data

All industries have applications for big data.

If you would like to know some more then read about How JTA The Data Scientists does its work or have a look at some other FAQs.

You could also explore our case studies or whitepapers.

The choice of R versus Python is largely academic.  At JTA we prefer to use R although both languages are perfectly acceptable.  There are a few differences between the two which we can summarize here:

• R has a much more extensive library of statistical packages and specialized techniques.
• You can find R packages for a wide variety of disciplines, from Finance to Medicine to Meteorology.
• Python is a general-purpose programming language, which can be used to write websites and applications whereas R is a Data Science tool.
• R builds in data analysis functionality by default, whereas Python relies on packages.
• Python currently has more packages for deep learning although this is changing.
• R is better for data visualization with plotting being more customizable.
• R is being integrated in a lot of mainstream products such as SQL and Power BI.

We also recommend using Microsoft Open R because of its multi threading features.

If you would like to know some more then read about How JTA The Data Scientists does its work or have a look at some other FAQs.

You could also explore our case studies or whitepapers.

It is necessary to undertake a journey to become a data scientist. One of the best ways to become a data scientist is to join a reputable data science provider like JTA. Data Scientists need to solve problems in a logical and analytical way. Mathematical ability is important, and you will need to understand some algebra, statistics and probability. If you can handle calculus, then so much the better.

The main languages used for Data Science are Python and ‘R’. You will need to learn at least one of these languages.

Next, you will need to understand how data are stored and manipulated, however pay careful attention to big data concepts and techniques.

If you would like to know some more then read about How JTA The Data Scientists does its work or have a look at some other FAQs.

You could also explore our case studies or whitepapers.

In 2012 the Harvard Business Review called it “The sexiest job of the 21st century”. Some claim that it is nothing more than a sexed-up term for statistics and so a lot of confusion reigns. We believe that Data Science is a merger of many traditional disciplines, bringing together statistics, processes, algorithms and machine learning. This means that it can have different interpretations but at its heart data science is the extraction of knowledge from data.

https://en.wikipedia.org/wiki/Data_science

If you would like to know some more then read about How JTA The Data Scientists does its work or have a look at some other FAQs.

You could also explore our case studies or whitepapers.

In the future of data science we will discover Causality without needing to understand the “why” or “how”. As data volumes increase, we discover patterns that may trigger us to investigate why. Data Science finds patterns so that humans solve problems that we didn’t know we had. This has an immense impact on our lifestyles. We will start to truly understand the impact on our lives, diets and behaviour.

If you would like to know some more about the future of data science then read about How JTA The Data Scientists does its work or have a look at some other FAQs.

You could also explore our case studies or whitepapers.