A Data Architect provides support to Data Engineers to carry out various tests with precision by using various tools and platforms. It’s very important for a Data Architect to be well versed in Data Modeling and Data Warehousing. Other additional skills required by a Data Architect are Extraction, Transformation, and Load (ETL), and should have hands-on experience with scalable tools such as Hive, Pig, and Spark.

A few of the prominent work a Database Administrator is involved in are:

  • Designing data models.

The Database Administrator (DBA), as the name suggests, operates and administers the database. The technical skills required by a DBA are SQL, scripting, database performance tuning, and system and network design. A DBA also handles the backup and recovery of databases. This job is critical as a business functions properly only when the database is stored and managed well.

Listing a few tasks a Database Administrator is involved in:

  • Database designing as per end-user requirements.

The main role of a Data analyst is to extract data and interpret the information obtained from the data for analyzing the outcome of a given problem in business. In this process, the analyst also discovers the various bottlenecks that are found in the results and provides possible solutions for the same. Extraction of information from given existing data is done using one or more standard methodologies such as data cleaning, data transformation, data visualization, and data modeling. Using these methodologies, a data analyst can make careful, data-driven decisions.

The major skills required to be a data analyst are Python


Data science, as perceived by most of the online courses and recent public discourse, has been around to help develop accurate models for prediction. The key areas within Data Science are focused on the development of models, that is, Artificial Intelligence, Machine Learning, and Deep Learning.

For fresher’s who are starting their journey in Data Science, the first thing they need to learn is the process of developing a Machine Learning model and interpreting them. So let’s try to understand how to build a machine learning model from scratch.

1. Problem definition: Any data analysis starts with setting up an…


Deep Learning is a subfield of Machine Learning based on Artificial Neural Networks. Unsupervised Pretrained Networks (UPNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks, and Recursive Neural Networks are the four major network architectures in Deep Learning. One of the main differences between Deep Learning vs Machine Learning is the way data is delivered to the system. It has more computational power making it one of the powerful and important Machine Learning subsets.

There are many Deep Learning Frameworks; however, we are going to review and list the most preferred frameworks.

TensorFlow
Developed and maintained by Google, Tensorflow is the…


Data analysis is a process of cleaning, transforming, and modeling data to discover meaningful information for better decision-making. Microsoft Excel is one of the top tools for Data Analysis. Excel has some powerful functions and formulas that improve your ability to analyze data.

So without wasting more time, let’s look at some Excel functions which are widely used for Data Analysis.

Sort & Filter

There may be cases where you want to work with a certain group of data while working on a large dataset. The sort function will come in handy then.

Shortcut keys — ALT + H +…


A Hacker is someone who spots and exploits weaknesses in computer systems or networks to gain unauthorized access. As per Cyberseek, the US alone has employed around 715,000 Cybersecurity experts.

Hackers are usually classified based on their intent of hacking a system and here’s a list of different types of Hackers and what they do.

Black Hat Hacker
Someone who violates computer security for personal gain or maliciousness.

White Hat Hacker
Someone who uses hacking skills to protect organizations from threats. They actively search for flaws that can be patched before a cybercriminal tries to penetrate the system.

Grey Hat…


Test automation has come a long way and will still be an integral part of the rapid delivery of software products and services across the business domain. Without the capability to test and release changes rapidly, an organization could face an existential crisis. We bring to you some of the key measures that can determine the success of a good automation project.

Let’s have a look -

Defect count: Number of defects detected using test automation suite.

Test misses: Number of defects leaked to later phases because of misses reported by test automation suite. The lower, the better. …


Cloud Computing started with the concept of renting out compute, storage, and networking services to developers. Serverless Computing abstracts the infrastructure from you. You need to write and deploy code to the Serverless environment. You need not worry about the underlying infrastructure, operating system, hosting and execution environment, and scaling requirements. All these are taken care of by the cloud vendor providing the Serverless service.

Let’s look at some of the benefits of Serverless Computing:

  • Lower cost

Lower cost

You need not purchase or maintain any infrastructure or…


A MANET is a mobile ad-hoc network that contains wireless links and nodes. It is an infrastructure-less network, and it can change its topology and configure itself on the fly, it can communicate via multiple hops. Whereas a Wireless Sensor Network (WSN) is a set of spatially distributed and dedicated sensors that are interlinked via the wireless medium for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location.

Let’s look at the similarities between MANET and WSN

  1. Both are infrastructure-less, distributed wireless networks

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