DEV Community: Divyanshi Kulkarni

Data Science vs Business Analytics

Divyanshi Kulkarni — Thu, 16 Jul 2026 14:07:46 +0000

Data Science and Business Analytics are closely connected, but they solve different business challenges. Explore the key differences, essential skills, career paths, and the growing role of AI in shaping both fields to choose the path that's right for you. https://tinyurl.com/4zsmyxzs

USAII® is the world’s leading Artificial Intelligence certifications provider

Divyanshi Kulkarni — Tue, 14 Jul 2026 07:07:29 +0000

AI Isn't Just Changing Technology. It's Changing Careers.

Professionals who understand how to implement AI strategically will lead tomorrow's workforce.

Why choose USAII®?

✔ Industry-recognized certifications
✔ Practical AI learning
✔ Vendor-neutral curriculum
✔ Trusted in 160+ countries
✔ Designed for professionals across industries

Invest in skills that keep you competitive in the AI era.

Explore USAII® today: https://tinyurl.com/udjc4z8t

Data Literacy Lead in a Digital-First Economy

Divyanshi Kulkarni — Fri, 03 Jul 2026 13:15:11 +0000

Data literacy is no longer just a technical skill, it is a leadership advantage. Executives who can interpret data, evaluate AI-driven insights, and make informed decisions are better equipped to drive innovation and business growth.

Build the expertise to lead with confidence through the Certified Senior Data Scientist (CSDS™) certification by USDSI®.

Enroll today and elevate your data leadership. https://tinyurl.com/2vd5vadr
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PandasAI Explained

Divyanshi Kulkarni — Thu, 02 Jul 2026 07:03:43 +0000

The future of data analysis is becoming more conversational.

Imagine asking your dataset a question in plain English and getting actionable insights without writing complex code. That is exactly what AI-powered tools like PandasAI are making possible.

The demand for these skills is growing fast. According to Precedence Research, the global data analytics market is expected to reach USD 83.79 in 2026 to USD 234.92 by 2030 at a CAGR of 28.35%, driven by AI, machine learning, and real-time decision-making.

Whether you are a data scientist, analyst, engineer, or business professional, understanding how AI is transforming data workflows is no longer optional. From natural language querying and automated data visualization to practical industry applications, PandasAI is redefining how organizations interact with data.

Discover how PandasAI works, explore its key features, compare it with traditional Pandas, understand its limitations, and learn how strong data science fundamentals can help you maximize its potential.

Download the guide and discover how AI-powered analytics is shaping the future of data science.

Tech Debt And Data Agility

Divyanshi Kulkarni — Mon, 29 Jun 2026 07:32:05 +0000

Technical debt in data engineering accumulates quietly until teams are spending more time maintaining existing systems than building what comes next. Understand what it looks like and how to address it systematically. https://shorturl.at/n43ne

USDSI® Global Demand Certifications

Divyanshi Kulkarni — Sat, 27 Jun 2026 08:20:20 +0000

Data science professionals who invest in structured, globally recognized credentials are the ones organizations are prioritizing in 2026. USDSI® certifications are designed to build the expertise, credibility, and strategic advantage your career demands.

Get Certified Today → https://shorturl.at/RBGmW

What is Power BI Gateway?

Divyanshi Kulkarni — Thu, 25 Jun 2026 10:07:12 +0000

Power BI Gateway is the secure bridge that connects on-premises data sources to the Power BI cloud service, enabling real-time data access without manual uploads. Know how it works and when to use each mode https://shorturl.at/3BlRZ

What is Power BI Gateway?

Divyanshi Kulkarni — Thu, 25 Jun 2026 07:22:59 +0000

What is Power BI Gateway?

Divyanshi Kulkarni — Tue, 23 Jun 2026 10:15:31 +0000

Power BI Gateway serves as a secure bridge that facilitates communication between on-premises data sources and Microsoft Power BI cloud services. This allows organizations to keep their data safely stored within local systems while still enabling Power BI to access, refresh, and analyze that data in real time or on a defined schedule. Rather than transferring sensitive information to the cloud, the gateway securely transmits only the required data through an encrypted connection.

For businesses that rely on on-premises databases such as SQL Server, Oracle, SAP, SharePoint, or Excel files stored on local servers, Power BI Gateway is an essential component. It ensures that reports and dashboards published to the Power BI Service remain consistently up to date, eliminating the need for manual data uploads.

Two types of Power BI Gateways are available:

1.Standard Mode (Enterprise Gateway): This mode is designed for organizational use and supports multiple users and data sources, along with scheduled refreshes, Direct Query, and Live Connection. It also integrates seamlessly with Microsoft services such as Power Apps, Power Automate, Azure Analysis Services, and Microsoft Fabric.

2.Personal Mode: Intended for individual users, this gateway is primarily used for the scheduled refresh of personal reports. It is straightforward to install but does not provide the advanced collaboration and enterprise management capabilities available in Standard Mode.

Utilizing Power BI Gateway offers a range of significant benefits, including enhanced security, automated data refreshes, centralized gateway management, and seamless integration between cloud-based analytics and on-premises systems. It eliminates the need for manual dataset updates, ensuring that business users consistently have access to the most current information for informed decision-making.

In today's data-driven environment, Power BI Gateway plays a critical role in enabling secure hybrid analytics. It effectively connects local business data with cloud-powered business intelligence, empowering organizations to leverage advanced visualization, reporting, and AI-driven insights without compromising data security or regulatory compliance.

Best Data Science Courses that You Should Check in 2026

Divyanshi Kulkarni — Sat, 20 Jun 2026 08:31:52 +0000

USDSI® offers a structured, three-tier data science certification pathway, CDSP™, CLDS™, and CSDS™, built to take professionals from foundational skills to enterprise-level AI and data leadership. Whichever stage of your career you are at, there is a program designed to help you build the right skills and credibility.

Explore the Pathways https://shorturl.at/x0LvK

Why Data Storytelling Matters More Than Ever in the Generative AI Era

Divyanshi Kulkarni — Thu, 18 Jun 2026 11:35:02 +0000

When Generative AI can process data, report, and create graphs in seconds, what skill do students actually need anymore? It is not the technical knowledge that is the answer. It is the capacity to explain a story behind the data. One of the things GenAI can help with, yet never creates.

Despite all the hype, the PwC Survey 2025 discovered that the number of employees who use GenAI tools daily is only 14% because having access to AI and knowing how to use its output are two very different things. The latter is the very gap that this blog is all about. How students can start developing this skill today: we deconstruct what data storytelling is, why Gen AI is more important than ever, and why it is becoming essential.
What Data Storytelling Actually Is (And isn’t)

It is not about creating graphs. Actual data storytelling is at the crossroads of three things:

●Data literacy: The ability to interpret the information given by the numbers.
●Narrative structure: Shaping those numbers into a beginning, middle, and end
●Visual communication: Displaying them to a real audience.

Imagine a person describing the facts about climate data to a school board. Raw statistics will not sway anybody. But a story with context, urgency, and a clear "so what?" just might. It is the difference between discovering an insight and describing it to take action, which is what data storytelling is. In a Gen AI world, when anybody can receive a summary of the data, not many can frame it.

The Gen AI Paradox: More Data, Less Understanding

This is the uncomfortable fact: AI systems such as ChatGPT, Gemini, and Copilot haven't made data skills less important. They've made them more urgent.

●No longer does a student have to analyze charts, summaries, and reports produced by AI.
●There exists data overwhelm; however, more information is creating worse decisions than better ones.
●Gen AI is an information generator, not a sense maker; the difference between them is overwhelming.

Those students who become aware of this gap and teach themselves to fill it will become essential partners of AI. The ones who don't risk being replaced by it.

Why Data Storytelling Is the Skill Gen AI Can't Fully Replace
Generative AI can work with data and create insights comprehensively and fast, yet data storytelling is a source of fundamental human skills.

●Contextual Judgment: It takes human knowledge as to what data are significant to a certain audience and at what point. AI recognizes trends, but it does not really understand priorities or context.
●Ethical Framing: In any piece of data, there are always decisions regarding what is emphasized and what is not. These are responsible decisions. Artificial intelligence can create insights, but not possess their influence.
●Audience Empathy: Emotional intelligence is needed to reduce complex data into simple, relevant information. It is possible to simulate empathy with the help of AI, but a real human connection is the distinction.
How Students Can Build Data Storytelling Skills in the Gen AI Era
Students have to be taught to judge and use insights generated by generative AI, as it can take place quite fast but requires meaningful interpretation. The concept of data storytelling concerns the process of providing clarity, context, and direction to the AI-generated outputs.

●Enhance information literacy, and challenge the origin, patterns, and suppositions of information.
●Ask “So what?” after every insight.
●Get used to transforming data into an identifiable start, middle, and conclusion.
●Rework the same information for other audiences.
●Apply GenAI as a draft, but not a rule.
●Focus on decisions and actions, not just descriptions.

Learners who learn to integrate critical thinking and AI technologies become leaders of thought, but not consumers.

What Educators Must Do to Prepare Students for an AI-Driven World
Memorization is not so important in an AI world; instead, interpretation of information is important. Teachers should not rely on artificial intelligence but teach students how to think.

●Turn classroom assignments into generative AI usage.
●Students should be asked to review and improve AI outputs.
●Educate on moral framing and good use of data.
●Focus on awareness and clarity of communication.
●Encourage intelligence and thought rather than volume.
●Promote problem-solving with real-world data.

The focus of learning must evolve from the mere provision of information to the facilitation of meaningful insight.

Conclusion: In the Age of Generative AI, Meaning Is the Real Competitive Advantage Reports, graphs, and summaries can be created in seconds using generative AI. But it cannot comprehend context, consequences, and human priorities.

It is not the ability to access information but the ability to interpret it that is the real benefit nowadays. Data storytelling converts raw data into meaning, purpose, and activity. With an AI-driven world, clarity, better questions, and meaning will be necessary.

FAQs

1. How will data storytelling affect careers in 2026?
In 2026, employers value professionals who can turn Generative AI outputs into clear business decisions. Data storytelling helps candidates stand out beyond technical skills.

2. Are universities teaching Gen AI and data storytelling in 2026?
Many institutions are integrating Gen AI tools, but the focus is shifting toward critical thinking and interpreting AI-generated insights.

3. Can data storytelling reduce GenAI misinformation?
Yes. Strong data storytelling skills help verify context, question AI outputs, and prevent the spread of misleading information.

How Machine Learning and NLP Power Sentiment Analysis | Infographic

Divyanshi Kulkarni — Fri, 12 Jun 2026 06:44:20 +0000

Sentiment analysis has become one of the most practical applications of machine learning and natural language processing in business today. By combining ML, NLP, and large language models, organizations can now move beyond simple positive or negative classification to understand context, sarcasm, intent, and even multilingual feedback at scale.

The process follows a clear pipeline, starting with data collection from reviews, surveys, social media, and customer interactions, followed by language processing to analyze words, context, and meaning, then ML application to detect patterns and classify sentiment, ultimately generating insights into trends, preferences, and concerns.

The business impact is significant. According to Business Research Insights 2026, 75% of retail companies are already leveraging sentiment analysis tools for consumer insights, and Precedence Research projects the global sentiment analytics market will reach USD 6.44 billion in 2026, growing at a 12.78% CAGR.

For businesses, the benefits extend across faster insight generation, better customer understanding, improved decision-making, competitive advantage, enhanced customer experience, and stronger reputation monitoring.

As sentiment analysis becomes core to how organizations understand their customers, professionals who can apply ML and NLP skills in this space are increasingly valuable.

Know more in detail https://shorturl.at/h8dyU