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The world is becoming more complex than ever. The amount of information generated out there is too much and what we consume is not even equivalent to a dot in the Universe.

And as a Data Science company, Analytics Vidhya understands how difficult it can be to keep up with the news in the Data Science market. So to keep you in the loop and always updated with the current affairs in the data science industry, Analytics Vidhya brings to you its new initiative where we mention the top 5 news of the recent times with verified sources.

So here are the top 5 news that you might have missed-

1. Data of 500 million LinkedIn users Breached

On 9th April 2023, Gadgets360 reports,

“LinkedIn Confirms Data Breach of 500 Million Subscribers, Personal Details Being Sold Online”

As per the news, information like email address, phone number, workplace information, full name, account IDs, links to their social media accounts, and gender details.

Reports say that the breached data is being sold on a hacker forum by an unknown user. The user has dumped data of over two million users as sample proof. The hacker is asking for a four-digit amount (in USD) in exchange for the breached data, potentially in the form of Bitcoins.

2. Streamlit Raises 35 billion in Series B funding

On 7th April 2023, TechCrunch reports

“Streamlit nabs $35M Series B to expand machine learning platform”

This round of investment was led by Sequoia with aid from previous investors Gradient Ventures and GGV Capital.

Reports confirm that Streamlit will utilize this money to scale its team, expand the platform and bring its technology to leading enterprises.

Sonya Huang, the partner at Sequoia and Streamlit board member, said: “The field of data is changing rapidly. Static dashboards are no longer the best way for data scientists to communicate insights and democratize data access. Interactive, data-rich web apps are the future. We believe Streamlit has a unique opportunity to disrupt the $25 billion Business Intelligence market with its open-source and developer-first approach—ultimately, becoming a core piece of the data science and machine learning stack for years to come. We are thrilled to partner with this exceptional team as Streamlit continues to experience wide adoption within the data science community and in the Fortune 1000.”

3. Supreme Court of India launches Artificial Intelligence portal SUPACE

On 7th April 2023, India Today reports,

“Supreme Court embraces Artificial Intelligence, CJI Bobde says won’t let AI spill over to decision-making”.

The SC intends to make use of machine learning to deal with the vast amounts of data received at the time of filing of cases leading to the piling up of whole volumes of cases.

The speeches given by the esteemed judges at the virtual event suggest that SUPACE(Supreme Court Portal for Assistance in Courts Efficiency) will help them “address bottlenecks resulting in excessive delays” and “ease pendency” but will not take decision using the tool.

The prime court of India adopting AI is a clear sign that it does not want the piling up of huge volumes of cases to continue and provide swift justice to the Indian Citizens.

You can watch the virtual launch here-

4. Most Advanced Data Center Platform Launched in India by Intel

On 8th April 2023, Gadgets Now reports,

The new 3rd Gen Intel Xeon scalable processors form the foundation of this data center platform. Intel claims to have improved the workload by an average of 46% by delivering a significant performance increase.

The platform comes with new capabilities including Intel SGX for built-in security, and Intel Crypto Acceleration and Intel DL Boost for AI acceleration.

5. Microsoft Collaborates with Thales Alenia Space for Satellite Image Processing

On 8th April 2023, The Hindu reports,

“Microsoft partners with Aerospace firm for automated satellite image processing”

Microsoft and aerospace firm Thales Alenia Space (TAS) are partnering to embed the latter’s automated image processing solution to Microsoft’s Azure Orbital platform.

With TAS’s DeeperVision, all images downlinked by Earth observation satellites can be immediately and systematically analyzed as soon as they are produced, the aerospace company said in a release.

Tom Keane, CVP of Azure Global at Microsoft says,

“Processing space satellite imagery at cloud-scale changes the game for our customers who need these AI/ML data insights to quickly make informed decisions for mission success”

End Notes

We hope this initiative helps you keep up with the current happenings in the Data Science industry. This news brought in by Analytics Vidhya aims to add value to your data science journey by helping you understand the practical implication of the data science concepts.


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Top 10 Data Science Prerequisites You Should Know In 2023

Data science paves an enticing career path for students and existing professionals. Be it product development, improving customer retention, or mining through data to find new business opportunities, organizations are extensively relying on data scientists to sustain, grow, and stay one step ahead of the competition. This throws light on the growing demand for data scientists. If you, too, are aspiring to become a successful data scientist, you have landed at the right place for we will talk about the top 10 data science prerequisites you should know in 2023. Have a look!


As a matter of fact, data science has a lot to do with data. In such a case, statistics turn out to be a blessing. This is for the sole reason that statistics help to dig deeper into data and gain valuable insights from them. The reality is – the more statistics you know, the more you will be able to analyze and quantify the uncertainty in a dataset.

Understanding analytical tools

Yet another important prerequisite for data science is to have a fair understanding of analytical tools. This is because a data scientist can extract valuable information from an organized data set via analytical tools. Some popular data analytical tools that you can get your hands on are – SAS, Hadoop, Spark, Hive, Pig, and R.


Data scientists are involved in procuring, cleaning, munging, and organizing data. For all of these tasks, programming comes in handy.  Statistical programming languages such as R and Python serve the purpose here. If you want to excel as a data scientist, make sure that you are well-versed in Python and R.

Machine learning (ML)

Data scientists are entrusted with yet another important business task – identifying business problems and turning them into Machine Learning tasks. When you receive datasets, you are required to use your Machine Learning skills to feed the algorithms with data. ML will process these data in real time via data-driven models and efficient algorithms.

Apache Spark

Apache Spark is just the right computation framework you need when it comes to running complicated algorithms faster. With this framework, you can save time a lot of time while processing a big sea of data. In addition to that, it also helps Data Scientists handle large, unstructured, and complex data sets in the best possible manner.

Data Visualization

Yet another important prerequisite for data science that cannot go unnoticed is data visualization, a representation of data visually, through graphs and charts. As a data scientist, you should be able to represent data graphically, using charts, graphs, maps, etc. The extensive amount of data generated each day is the very reason why we require data visualization.

Communication skills

The fact that communication skill is one of the most important non-technical skill that one should possess, no matter what the job role is, goes without saying. Even in the case of data science, communication turns out to be an important prerequisite. This is because data scientists are required to clearly translate technical findings to the other non-technical teams like Sales, Operations or Marketing Departments. They should also be able to provide meaningful insights, hence enabling the business to make wiser decisions.


Excel is one tool that is extremely important to understand, manipulate, analyze and visualize data, hence a prerequisite for data science. With Excel, it is quite easy to proceed with manipulations and computations that have to be done on the data. Having sound Excel knowledge will definitely help you become a successful data scientist.


Top 6 Data Science Jobs In The Data

This data science career is doing very well on the market. Data science is making remarkable progress in many areas of technology, economy and commerce. It’s not an exaggeration. It is no surprise that data scientists will have many job opportunities.

It is true. Multiple projections show that the demand for data scientists will rise significantly in the next five-years. It is clear that demand is far greater than supply. Data science is a highly specialized field that requires a passion for math and analytical skills. This gap is perpetuated by the insufficient supply of these skills.

Every organization in the world is now data-driven. Data-driven organizations are the First Five: Google, Amazon, Facebook, Meta, Apple, Microsoft, and Facebook. They aren’t the only ones. Nearly every company in the market uses data-driven decision-making. The data sets can be customized quickly.

Amazon keeps meticulous records of all our choices and preferences in the world of shopping. It customizes the data to only send information that is relevant to the search terms of specific customers. Both the client and the company benefit from this process. This increases the company’s profit and helps the customer by acquiring goods at lower prices than they expected.

Data sets have a wider impact than just their positive effects. Data sets have positive effects on the health sphere by making people aware about critical health issues and other health-related items. It can also have an impact on agriculture, providing valuable information to farmers about efficient production and delivery of food.

It is evident that data scientists are needed around the globe, which makes their job prospects bright. Let’s take a look at some of the most exciting data science jobs available to data scientists who want to be effective in data management within organizations.

Top 6 Data Science Jobs in the Data-driven Industry 1. Data scientists

Average Salary: US$100,000.

Also read: 14 Best Webinar Software Tools in 2023 (Ultimate Guide for Free)

2. Data architects

Average Salary: US$95,000/annum

Roles and Responsibilities This employee is responsible for developing organizational data strategies that convert business requirements into technical requirements.

3. Data engineers

Average Salary: US$110,000 an Year

Also read: The 15 Best E-Commerce Marketing Tools

4. Data analysts

Average Salary: US$70,000 an Year

Roles and Responsibilities. A data analyst must analyze real-time data using statistical techniques and tools in order to present reports to management. It is crucial to create and maintain a database and analyze and interpret current trends and patterns within those databases.

5. Data storyteller

Average Salary: US$60,000 an Year

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6. Database administrators

Average Salary: US$80,000 an Year

Roles and Responsibilities of a database administrator: The database administrator must be proficient in database software to manage data effectively and keep it up-to date for data design and development. This employee will manage the database access and prevent loss and corruption.

These are only a few of the many data science jobs available to the world. In recent years, data science has been a thriving field in many industries around the globe. In this fast-paced world, data is increasingly valuable and there are many opportunities to fill data-centric roles within reputable organizations.

What Do You Need To Know About Financial Document Management Software?

The solution is document management software. As a result of standardized and centralized digital warehouses, document management has revolutionized both small and large organizations, allowing users to easily share, edit, and control documents.

What Do You Understand by Document Management Software?

Large organizations often have different types and methods of handling documents across different sites and departments. When there is no centralized system to make sure uniformity, efforts to make and distribute them tend to be redundant.

With this management software, these efforts are consolidated, offering a single retrieval and archiving controlled document resource, which helps with easy file location and reporting. Document management facilitates interaction and collaboration by enabling role-based security privileges over the web.

The enterprise standard can be created, edited, linked together, and archived by users. There are a variety of types of documents that can be entered into operations, routing the works and delivery of certain files to key personnel, including standard operating procedures (SOPs), best practices, training materials, and regulatory content.

Quality Scenarios Using Document Management Software

Enterprise quality management software (EQMS) typically offers document management functionality. The concept of closed-loop quality management—the idea of integrating cross-functional feedback loops in the value chain—is made possible by document management software, since many EQMS functionalities require standardized documents and workflows.

Document management plays a significant role in the following processes −

Environment, Health, & Safety (EH&S)

Change management

Compliance Management

Supplier Quality Management

Employee Training

A Quality Manager’s Guide to Document Management

The document management software currently has a strong market that is used by customers and firms alike, but there are many reasons why quality organizations require more than what OneDrive, Google Docs, or many more could provide.

Due to the solution’s role as a closed-loop quality enabler, functionalities are frequently built with quality processes in mind in order to interoperate more naturally. Additionally, ISVs (independent software vendors) have developed solutions using IBM and Microsoft SharePoint technologies.

Benefits of Document Management Software

You can digitize paperwork and store it securely in an organized repository for quick retrieval when needed with document management and workflow automation. Take photos, store them, manage them, process them, share them, and track them easily.

Staff can handle daily tasks more efficiently with controlled access, task lists, and email notifications, and decision-makers can approve, reject or request more information along the way.

Collaboration and Productivity

Provide staff and faculty with the information they need by digitizing paper records and retrieving them instantly.

Save office space by storing client records electronically.

Quickly capture and process information requests, claims forms, and more with public-facing eforms.

Automate accounts payable and receivable to support financial health.

Maximize the value of your existing technology by integrating it with your client billing system.

Compliance, Safety, and Security

Protect client privacy by restricting unauthorized viewing of confidential documents.

Demonstrate compliance with HIPAA, Sarbanes-Oxley, and GDPR.

Protect data, documents, and online communications from cyber threats with state-of-the-art encryption.

Enforce retention schedules automatically.

IT and Other Future-Readiness

Develop a user-friendly solution that meets the needs of multiple departments without adding to IT’s workload.

Prepare for natural disasters with a fail-safe disaster recovery plan

Document Management Trends

Among the top qualities banking stakeholders prefer in their DMS are ease of use, flexibility, and a straightforward architecture that can be integrated with legacy infrastructure. Using the latest DMS is a great way for non-banking and banks financial institutions to differentiate themselves from their competition. The banking industry has devoted a greater amount of attention to DMSs, which has led to various trends, including:

Cybersecurity Control Software

In order to compete in the market, DMS offerings should offer safeguards against cyberattacks on the mainframes of banking institutions. A number of document cybersecurity control providers have recognized that this market need exists and have developed dedicated features of cybersecurity specifically designed for banking DMSs.

A key cybersecurity feature of these systems is the ability to protect or permit specific file types and extensions. To do this, the file header is compared against a list of all uploaded files.

Various methods of incorporating multiple factors of authentication are available. MFA tokens should be tailored to the bank’s policies in order to be a popular choice for them.

Cloud-Based Collaboration

The business case for DMS has drastically changed, with most institutions now favoring it. As a result of the cloud’s ability to remove size and scalability limitations, a bank’s business can grow without limit.

Merged conglomerates can create opportunities for development and portfolio expansion through cloud-based collaboration. Due to the elimination of a closed network, partners in far-flung countries can now collaborate on document exchange and transaction tracking without the need to access a closed network.

Enterprise Process Automation

The program observes human handling in order to help the program components learn how to perform these business processes. Data scientists can also use automation to mine enterprise DMS for relevant information. Data mining can be performed by low-level data engineers with the right DMS equipped with AI enhancements, enabling small businesses to get the same capabilities as larger businesses.


Some document management software offers a wide variety of features and benefits, while others are tailored specifically for certain tasks. Consider how you carry out your business when selecting the best option for your business.

5 Bar Tricks You Can Do With Science

As Kurt Vonnegut wrote in his classic Cat’s Cradle, “science is magic that works.” The same can be applied to these seemingly magical bar tricks, which are not really tricks but based in simple physics and science. They were shown to me by an affable Englishman named Tim Shaw, who hosts a show called “None of the Above,” debuting tonight (March 24) on the National Geographic Channel at 9 p.m. ET. In the show, Shaw will conduct a series of outrageous feats like landing a helicopter on eggs–without breaking a single one–to illustrate “how cool science can be,” he said. (In this example, the science-based explanation is that the shape of the eggs is surprisingly strong, and when many eggs are put together, their collective strength is greater than you might imagine.) Here are some tricks that Shaw demonstrated for PopSci last week.

1. Upside-Down Wine Bottle Trick

Shaw pours the contents of a wine bottle into a bowl, leaving a small amount in the bottom of the bottle. Then he puts the bottle into the microwave for nearly 3 minutes. What will happen when he puts the bottle, top-down, into the bowl?

The answer: it will suck up nearly all of the wine.

How does it work? The microwave turns much of the water and alcohol in the wine into steam and vapor, causing the pressure in the bottle to rise. Although the bottle isn’t sealed, the opening is small enough so that most of it remains within the container. Once he takes it out and places it upside-down in the wine, however, a seal is formed and the bottle and gases within quickly cool. As the water condenses, going from a gas to a liquid, the pressure in the bottle drops. That causes surrounding atmospheric pressure to push the wine from the bowl up into the bottle, Shaw explains.

Wine bottle trick

TV presenter Tim Shaw demonstrating a trick wherein a wine bottle is made to suck up wine.

2. Create A Plasma Ball In Your Microwave

Shaw lights a match inside a microwave oven, and his assistant places a wine glass over it. He turns on the microwave. Almost immediately, a ball of plasma appears. Shaw says that this can reach a high temperature, enough to crack the wine glass. After several failed attempts he gets it to work, as you can see in the video, but it doesn’t break the wine glass. Probably for the best. (Note: I wouldn’t recommend trying this at home.)

How it works: the microwaves (produced by the appliance’s magnetron) cause the gases released by the burning match to become weakly ionized, or charged, creating plasma. Some electrons in the gases absorb the microwaves, achieving a higher energy level. But they don’t stay there for long, and as they come back down to a lower energy level, they release light and heat.

3. Gaseous Whiskey

Shaw puts some whiskey in a 20-ounce plastic bottle, and then pumps it up a few times with a bicycle pump. The connection between the pump and the bottle must be air-tight, which Shaw achieves with a rubber stopper. Once he releases the seal, a haze of whiskey and water appears inside the bottle. This whiskey fog can then be inhaled. (It may be okay to do this once or twice, but inhaling alcohol in general is dangerous.)

How it works: the gradual build-up of pressure causes a small amount of the alcohol to vaporize and become a gas; the pressure overcomes the intermolecular forces that typically make alcohol a liquid at room temperature, and allows it to make the phase transition to a gas. When the pressure drops again, the alcohol condenses to form small visible droplets.

4. Transfer Water Into A Glass

You’ve got a bowl shallowly filled with water, an empty glass, a lime wedge, and a book of matches. Now get the water from the bowl into the glass (without pouring it in–that’s cheating).

The solution? Place the lime wedge in the middle of the bowl. Light a match and stick it upright (flame up) in the lime wedge. Place the glass over it. Watch the water get sucked into the glass as the match is gradually extinguished.

How it works: As the match burns, it consumes the oxygen in the glass. While this creates some water and carbon dioxide, it reduces the pressure, sucking in the water.

5. Break the Bottom Out of a Bottle

Shaw fills a beer bottle almost all the way to the top with water, leaving about a one-inch gap. He then firmly hits the top of it with the butt of his palm. The bottom pops off, and the water goes with it.

I had previously thought this was an urban legend, but apparently it is not. One key to doing this is holding the bottle firmly in the non-striking hand, preventing it from moving downward as much as possible. This creates a shock wave of pressure that moves through the liquid, breaking the bottle its weakest point, the bottom.

One important note: this doesn’t work with carbonated beverages like beer, Shaw said. Instead, water or flat beer are ideal. Bubbles in the liquid interfere with the movement of the shock wave, he said.

In related news, here’s why hitting the top of somebody’s beer bottle causes it to foam over.

Data Science Roles In Telecom Industry


Big Data and Cloud Platform

In the early years, telecommunications data storage was hampered by a variety of problems such as unwieldy numbers, a lack of computing power, prohibitive costs. But with the new technologies, the dimension of problems has changed.

The areas of use of Technology are:

· Cloud Platform enabling Data storage expenses to drop every day. (Azure, AWS)

· Computer processing power is increasing exponentially (Quantum Computing)

· Analytics software and tools are cheap and sometimes free (Knime, Python)

In earlier days, the data stores were expensive, and data was stored in siloed – separated and often incompatible – data stores. This was creating barriers to make use of an enormous volume and variety of information. Business Intelligence (BI) vendors like IBM, Oracle, SAS, Tibco, and QlikTech are breaking down these walls between data storage and this provides a lot of jobs for telecom data scientists.

Data Scientist roles in Telecom Sector 1. Network Optimization

When a network is down, underutilized, overtaxed, or nearing maximum capacity, the costs add up

In the past, telecom companies have handled this problem by putting caption data and developing tiered pricing models.

But now, using real-time and predictive analytics, companies analyze subscriber behavior and create individual network usage policies.

When the network goes down, every department (sales, marketing, customer service) can observe the effects, locate the customers affected, andimmediately implement efforts to address the issue.

When a customer suddenly abandons a shopping cart, customer service representatives can soothe concerns in a subsequent call, text, oremail.

Building360-degree profile of Network using CDRs, Alarms, Network Manuals, TemIP, etc. gives a better overview of the network health.

Not only does this make happy customers, but it also improves efficiencies and maximizes revenue streams.

Telecoms also have the option to combine their knowledge of network performance with internal data (e.g., customer usage or marketing initiatives) and external data (e.g., seasonal trends) to redirect resources (e.g., offers or capital investments) towards network hotspots.

2. Customer Personalization

Like all the industries, Telecom has much more scope to personalize the services such as value-added services, data packs, apps to recommend based on following the behavioral patterns of customers. Sophisticated 360-degree profiles of customers assembled from all below help to build personalized recommendations for customers.

Customer Behaviour

voice, SMS, and data usage patterns

video choices

customer care history

social media activity

past purchase patterns

website visits, duration, browsing, and search patterns.

Customer Demographics

age, address, and gender

type and number of devices used.

service usage

geographic location

This allows telecom companies to offer personalized services or products at every step of the purchasing process. Businesses can tailor messages to appear on the right channels (e.g., mobile, web, call center, in-store), in the right areas, and in the right words and images.

Customer Segmentation, Sentiment analysis, Recommendation engines for more apt products for the customers are the illustrative areas where Data scientists can help for improvements.

3. Customer Retention

Due to customer dissatisfaction in any of the areas such as poor connection/network quality, poor services, high cost of services, call drops, competitors, less personalization, customer churn. This means they jump from network to network in search of bargains. This is one of the biggest challenges confronting a telecom company. It is far more costly to acquire new customers than to cater to existing ones.

To prevent churn, data scientists are employing both real-time and predictive analytics to:

Combinevariables (e.g., calls made, minutes used, number of texts sent, average bill amount, the average return per user i.e.ARPU) to predict the likelihoodof change.

Know when a customer visits a competitor’s website changes his/her SIM or swaps devices.

Use sentiment analysis of social media to detect changes in opinion.

Target specific customer segments with personalized promotions based on historical behavior.

React to retains customers as soon as the change is noted.

Predictive models, clustering would be the ways to predict the prospective churners.

Implemented Solution Approach

Using big data and python, I have developed the solution to find the upcoming network failure before it takes place. The critical success factor defined were:

· Identify and prioritize the cells with call drop issues based on rules provided by the operator.

· Based on rules specified, provide relevant indicative information to network engineers that might have caused the issue in the particular cell.

· Provide a 360-degree view of network KPIs to the network engineer.

· Build a knowledge management database that can capture the actions taken to resolve the problem and

· Update the CRs as good and bad, based on effectiveness in resolving the network issue

As a huge data was getting created, the database used was Hadoop -Big Insights.

Data transformation scripts were in spark.

And the neural network was the ML technique used to find out the system parameters when historically alarms (the indication of network failure) in the system got generated.

This information was fed as a threshold and once in the real scenario the parameters start approaching the threshold, the internal alert for those cell sites get generated for the Network engineer to focus on as preventive analytics.

Once the network engineer identifies the problem and solves it, it gets documented in the knowledge repository for future reference.

And when exactly a similar situation occurs, network the engineer will not get notification of internal alert but also steps to solve which is build using knowledge repository.


The reduction in process time, dropped call rate, the volume of (transient) issues handled by engineer, mean time to solve the problem, cost, people and increase in Revenue, customers, customer satisfaction, efficiency, and productivity of network engineers are the main area of any industry which Data scientists would be of help.

Various data generation sources under Telecom sectors are booming areas for Data Scientists to innovate, explore, value add, and help the provider to provide data-driven AI/ML solutions by preventive analytics, process improvements, optimizations, predictive analytics.


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