data analysts

How programming languages assist data analysts in reducing analysis bugs 
Erik Brooks
| December 23, 2022

In this blog, we are going to discuss the value addition provided by programming languages for data analysts.

Data analysts have one simple goal – to provide organizations with insights that inform better business decisions. And, to do this, the analytical process has to be successful. Unfortunately, as many data analysts would agree, encountering different types of analysis bugs when analyzing data is part of the data analytical process.

However, these bugs don’t have to be many if only preventive measures are taken every step of the way. This is where programming languages prove valuable for data analysts. Programming languages are one such valuable tool that helps data analysts to prevent and solve a number of data problems. These languages contain different bug-preventing attributes that make this possible. Here are some of these characteristics. 

 

Programming languages
Programming languages – Data Analysts

Type safety/strong typing 

When there is an inconsistency between varying data types for the variables, methods, and constants, the program behaves undesirably. In other words, type errors occur. For instance, this error can occur when a programmer treats a string as an integer or vice versa. 

Type safety is an attribute of programming languages that discourages type errors in a program. Type safety or type soundness demand programmers to define the type of each variable. This means that programmers must declare the data type that is meant to be in the box as well as give the box a variable name. This ensures that the programmer only interprets values as per the rules of the declared data type, which prevents confusion about the data type. 

Immutability 

If an object is immutable, then its value or state can’t be changed. Immutability in programming languages allows developers to use variables that can’t be muted or changed. This means that users can only create programs using constants. How does this prevent problems? Immutable objects ensure thread safety as compared to mutable objects. In a multithreaded application, a thread doesn’t have to worry about the other threads as it acts on an immutable object.

The reason here is that the thread knows that the object can’t be modified by anyone. The immutable approach in data analysis ensures that the original data set is not modified. In case a bug is identified in the code, the original data helps find a solution faster. In addition, immutability is valuable in creating safer data backups. In immutable data storage, data is safe from data corruption, deletion, and tampering.

 

Expressiveness 

Expressiveness in a programming language can be defined as the extent of ideas that can be communicated and represented in that language. If a language allows users to communicate their intent easily and detect errors early, that language can be termed as expressive. Programming languages that are expressive allow programmers to write shorter codes.

Moreover, a shorter code has less incidental complexity/ boilerplate, which makes it easier to identify errors. Talking of expressiveness, it is important to know that programming languages are English based.

When working with multilingual websites, it would be important to translate the languages to English for successful data analysis. However, there is the risk of distortion or meaning loss when applying analysis techniques to translated data. Working with professional translation companies eliminates these risks.

In addition, working in a language that they can understand makes it easy to spot errors. 

Static and dynamic typing 

These attributes of programming languages are used for error detection. They allow programmers to catch bugs and solve them before they cause havoc. The type-checking process in static typing happens at compile time.

If there is an error in the code such as invalid type arguments, missing functions or a discrepancy between the type of variable and data value assigned to it, static typing catches these bugs before the program runs the code. This means zero chances of running an erroneous code. 

On the other hand, in dynamic typing, type-checking occurs during runtime. However, it gives the programmer a chance to correct the code if it detects any bugs before the worst happens. 

 

Programming learning – Data analysts

Among the tools that data analysts require in their line of work are programming languages. Ideally, programming languages are every programmer’s defense against different types of bugs. This is because they come with characteristics that reduce the chances of writing codes that are prone to errors. These attributes include those listed above and are available in different programming languages such as Java, Python, and Scala, which are best suited for data analysts.   

 

 

Apache Superset : Empowering Business Intelligence
Saad Shaikh
| October 15, 2022

Data Science Dojo is offering Apache Superset for FREE on Azure Marketplace packaged with pre-installed SQL lab and interactive visualizations to get started. 

 

What is Business Intelligence?  

 

Business Intelligence (BI) depends on the idea of utilizing information to perform activities. It expects to give business pioneers noteworthy bits of knowledge through data handling and analytics. For instance, a business breaks down the KPIs (Key Performance Indicators) to distinguish its benefits and shortcomings. Hence, the decision-makers can conclude in which department the organization can work to increase efficiency.  

Recently two elements in BI have resulted in sensational enhancements in metrics like speed and proficiency. The two elements include:  

 

  • Automation  
  • Data Visualization  

 

Apache Superset widely focuses on the latter model which has changed the course of business insights.  

 

But what were the challenges faced by analysts before there were popular exploratory tools like Superset?  

 

Pro Tip: Join our 6-months instructor-led Data Science Bootcamp to master data science. 

 

Challenges of Data Analysts

 

Scalability, framework compatibility, and absence of business-explicit customization were a few challenges faced by data analysts. Apart from that exploring petabytes of data and visualizing it would cause the system to collapse or hang at times.  

In these circumstances, a tool having the ability to query data as per business needs and envision it in various diagrams and plots was required. Additionally, a system scalable and elastic enough to handle and explore large volumes of data would be an ideal solution.  

 

Data Analytics with Superset  

 

Apache Superset is an open-source tool that equips you with a web-based environment for interactive data analytics, visualization, and exploration. It provides a vast collection of different types of vibrant and interactive visualizations, charts, and tables. It can customize the layouts and the dynamic dashboard elements along with quick filtering, making it flexible and user-friendly. Apache Superset is extremely beneficial for businesses and researchers who want to identify key trends and patterns from raw data to aid in the decision-making process.  

 

Sales analytics - Apache superset
Video Game Sales Analytics with different visualizations

 

 

It is a powerhouse of SQL as it not only allows connection to several databases but also provides an in-browser SQL editor by the name SQL Lab  

SQL lab - Apache superset
SQL Lab: an in-browser powerful SQL editor pre-configured for faster querying

 

Key attributes  

 

  • Superset delivers an interactive UI that enriches the plots, charts, and other diagrams. You can customize your dashboard and canvas as per requirement. The hover feature and side-by-side layout make it coherent  
  • An open-source easy-to-use tool with a no-code environment. Drag and drop and one-click alterations make it more user-friendly  
  • Contains a powerful built-in SQL editor to query data from any database quickly  
  • The choice to select from various databases like Druid, Hive, MySQL, SparkSQL, etc., and the ability to connect additional databases makes Superset flexible and adaptable  
  • In-built functionality to create alerts and notifications by setting specific conditions at a particular schedule  
  • Superset provides a section about managing different users and their roles and permissions. It also has a tab for logging the ongoing events  

 

What does Data Science Dojo have for you  

 

Superset instance packaged by Data Science Dojo serves as a web-accessible no-code environment with miscellaneous analysis capabilities without the burden of installation. It has many samples of chart and dataset projects to get started. In our service users can customize dashboards and canvas as per business needs.

It comes with drag-and-drop feasibility which makes it user-friendly and easy to use. Users can create different visualizations to detect key trends in any volume of data.  

 

What is included in this offer:  

 

  • A VM configured with a web-accessible Superset application  
  • Many sample charts and datasets to get started  
  • In-browser optimized SQL editor called SQL Lab  
  • User access and roles manager  
  • Alert and report feature  
  • Feasibility of drag and drop  
  • In-build functionality of event logging  

 

Our instance supports the following major databases:  

 

  • Druid  
  • Hive  
  • SparkSQL  
  • MySQL  
  • PostgreSQL  
  • Presto  
  • Oracle  
  • SQLite  
  • Trino  
  • Apart from these any data engine that has Python DB-API driver and a SQL Alchemy dialect can be connected  

 

Conclusion  

 

Efficient resource requirement for exploring and visualizing large volumes of data was one of the areas of concern when working on traditional desktop environments. The other area of concern includes the ad-hoc SQL querying of data from different database connections. With our Superset instance, both concerns are put to rest.

When coupled with Microsoft cloud services and processing speed, it outperforms its traditional counterparts since data-intensive computations aren’t performed locally but in the cloud. It has a lightweight semantic layer and is designed as a cloud-native architecture.  

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Superset instance dedicated specifically to Data Science & Analytics on Azure Marketplace. Now hurry up and avail this offer by Data Science Dojo, your ideal companion in your journey to learn data science!  

 

Click on the button below to head over to the Azure Marketplace and deploy Apache Superset for FREE by clicking on “Get it now”. 

 

Superset

 

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 

 

 

 

 

 

 

 

Albar Wahab
| June 22, 2022

Look into data science myths in this blog. The field of Data is an ever-growing field and often you’ll come across buzzwords surrounding it. Being a trendy field, sometimes you will come across statements about it that might be confusing or entirely a myth. Let us bust these myths, and ensure your doubts are clarified!

What is Data Science?

In simple words, data science involves using models and algorithms to extract knowledge from data available in various forms. The data could be large or small or could be structured such as a table or unstructured such as a document containing text and images containing spatial information. The role of the data scientist is to analyze this data and extract information from the data which can be used to make data-driven decisions.

data science myths, data science compass
The Flawed Data Science Compass

Myths

Now, let us dive into some of the myths:

1. Data Science is all about building machine learning and deep learning models

Although building models is a key aspect, it does not define the entirety of the role of a Data Scientist. A lot of work goes on before you proceed with building these models. There is a common saying in this field that is “Garbage in, garbage out.” Real-life data is rarely available in a clean and processed form, and a lot of effort goes into pre-processing this data to make it useful for building models. Up to 70% of the time can be consumed in this process.

This entire pipeline can be split up into multiple stages including acquiring, cleaning, and pre-processing data, visualization, analyzing, and understanding it, and only then are you able to build useful models with your data. If you are building machine learning models using the readily available libraries, your code for your model might end up being less than 10 lines! So, it is not a complex part of your pipeline.

2. Only people with a programming or mathematical background can become Data Scientists

Another myth surrounding is that only people coming from certain backgrounds can pursue a career in it, which is not the case at all! Data science is a handy tool that can help a business enhance its performance in almost every field.

For example, human resources is a field that might be distant from statistics and programming, but it has a very good implementation of data science as a use case. IBM, by collecting employee data, has built an internal AI system that can predict when an employee might quit using machine learning. A person with domain knowledge about the human resource field will be the best fit for building this model.

Regardless of your background, you can learn it online with our top-rated courses from scratch. Join one of our top-rated programs including Data Science Bootcamp and Python for Data Science and get started!

Join our Data Science Bootcamp today to start your career in the world of data. 

3. Data Analysts, Data Engineers, and Data Scientists all perform the same tasks

Data Analysts and Data Scientists roles have overlapping responsibilities. Data analysts carry out descriptive analytics, collecting current data and making informed decisions using it. For example, a data analyst might notice a drop in sales and will try to uncover the underlying cause using the collected company data. Data Scientists also make these informed business decisions. However, they involve using statistics and machine learning to predict the future!

Data Scientists use the same collection of data but use it to make predictive models that can predict future decisions and guide the company on the right actions to take before something happens. Data engineers on the other hand build and maintain data infrastructures and data systems. They’re responsible for setting up data warehouses and building databases where the collected data is stored.

4. Large data results in more accurate models

This myth might be partially wrong but partially right as well. Large data does not necessarily translate to higher accuracy of your model. More often, the performance of your model depends on how well you carry out the cleaning of your dataset and extraction of the features. After a certain point, the performance of your model will start to converge regardless of how much you increase the size of your dataset.

As per the saying “garbage in, garbage out”, if the data you have provided for the model is noisy and not properly processed, likely, the accuracy of the model will also be poor. Therefore, to enhance the accuracy of your models, you must ensure that the quality of the data you are providing is up to the mark. Only a greater quantity of relevant data will positively impact your model’s accuracy!

5. Data collection is the easiest part of data science

When learning how to build machine learning models, you would often go to open data sources and download a CSV or Excel file with a click of a button. However, data is not that readily available in the real world and you might need to go to extreme lengths to acquire it.

Once acquired, it will not be formatted and in an unstructured form and you will have to pre-process it to make it structured or meaningful. It can be a difficult, challenging, and time-consuming task to source, collect and pre-process data. However, this is an important part because you cannot build a model without any data!

Data comes from numerous sources and is usually collected over a period by using automation or manual resources. For example, for building a health profile of a patient, data about their visits will be recorded. Telemetry data from their health device such as sensors can be collected and so on. This is just the case for one user. A hospital might have thousands of patients they deal with every day. Think about all the data!

Please share with us some of the myths that you might have encountered in your data science journey.

Want to upgrade your data science skillset? checkout our Python for Data Science training. 

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