• WhiteScholars: Provides Hands-on practice with real-time projects, case studies & industry problems.
• Others: Mostly theoretical knowledge with limited practical exposure.

• WhiteScholars: Data Scientists as Trainers are industry professionals with real-world experience.
• Others: They often rely on academic faculty members with limited industry exposure.

• WhiteScholars: Offers 100% placement assistance, mock interviews, resume-building sessions, CRP Sessions & 1:1 mentorship.
• Others: Job referrals or passive support.

• WhiteScholars: Students get multiple interview opportunities based on their student performance & project completion.
• Others: May not track student performance or offer limited referrals.

• WhiteScholars:Tailored for non-programmers and freshers or beginners starting from basics.
• Others: Often skip basic fundamentals

• Regular assignments, weekly once hackathons or workshops, and career counseling session.
• Continuous skill assessments & feedback reviews to the study.

• Data Science is used for discover the patterns, build models and make predictions.The techniques used in data science are Machine Learning,Deep Learning,AI, predictive Modeling.Tools covered in the data science course are Python,Numpy,Pandas,Seaborn, R, TensorFlow, and Spark.


• Data Analytics is used to analyze historical data to generate actionable insights.The techniques used in data analytics are Data Manipulation, Data visualisation, and Data Mining.Tools covered in the data analytics course are Python, Numpy, Pandas, Excel, SQL, PowerBI, and Tableau.

• Business Analyst: Focuses on using data to improve business operations and strategies.
• Marketing Analyst: Specializes in analyzing marketing data to optimize campaigns.
• Operations Analyst: Uses data to enhance internal processes and efficiencies.
• Financial Analyst: Analyzes financial data to aid in budgeting, forecasting, and investment decisions.

• A comprehensive data analytics training program is designed to blend in-depth theoretical learning with extensive hands-on experience and robust career support. Here’s what you can expect:

• Hands-on learning is at the core of our approach. You’ll participate in 7 capstone projects—ranging from data cleaning and preprocessing to building actionable insights with real datasets. These projects are crafted to mirror real-world scenarios, allowing you to apply your newly acquired skills and build a portfolio that employers value.

• Throughout the course, you will get targeted practical assignments. You’ll dive into extracting, transforming, and loading data from sources like SQL and databases, as well as flat files.

• Learn from industry leaders working in top companies.Their hands-on industry experience ensures you receive relevant, up-to-date training that aligns with current market needs.

• Beyond learning, the program bridges the gap to employment. Access dedicated job placement assistance, career guidance, and networking opportunities through industry events and career fairs. With strong ties to a network of hiring managers, students are well-positioned to navigate a seamless transition into data-driven roles.
• By combining immersive learning, practical application, and ongoing career support, this program sets you up for success in the fast-growing field of data analytics.

• Comprehensive Learning Experience: Over 600 hours of blended training and project work, so you’re job-ready from day one.
• Practical Assignments: More than 300 hours of hands-on assignments ensure you’re comfortable solving real-world business problems.
• Data Mastery: Gain expertise in data ingestion, preprocessing, and analysis using varied data types—from NoSQL to flat files and beyond.
• Live Capstone Projects: Tackle 7+ interactive projects that let you apply new skills to scenarios you’ll encounter on the job.

• Expert Mentorship: You’ll learn from seasoned professionals—think leaders from top companies,bringing years of industry experience straight to your classroom.
• Industry Connections: Leverage our network of 100+ engaged hiring managers, opening doors to respected organizations actively seeking analytics talent.
• Current Job Market Alignment: Each aspect of the course is co-created with hiring managers, so you’re learning exactly what employers require today, giving you a genuine competitive edge.

• Hyderabad’s HITEC City continues to attract leading names in technology and analytics, including Microsoft, Amazon, Accenture, and Deloitte. This technology corridor isn’t just about gleaming office towers; it’s about unparalleled opportunity. The city has become a magnet for global and local companies, making it a fertile ground for aspiring data professionals.

• What does this mean for you? Increased demand translates to an ever-growing number of job openings for data analysts, data scientists, and BI professionals. Hyderabad routinely features among the top three Indian cities for data analytics jobs, rivaling Bengaluru and Mumbai. Whether you are just starting out or looking to move up with a few years of experience under your belt, the city offers competitive salary ranges that can accelerate your career growth.

• The city’s educational ecosystem complements its industry focus. Students in Hyderabad gain not just theoretical knowledge, but access to a network of data professionals and internship opportunities, thanks to proximity to real-world innovators. This blend of academic rigor and industry exposure gives learners a genuine edge.

• Beyond its professional edge, Hyderabad boasts a strong community of analysts and data scientists who host regular meetups, workshops, and hackathons. The pleasant cost of living and cosmopolitan, culturally vibrant environment make the transition from education to employment smooth—and enjoyable.

• Data analysts are in high demand, and compensation reflects both experience and location. In Hyderabad, entry-level data analysts typically earn between ₹2–3.5 lakhs per annum, while those with a few years of experience can expect to make ₹4–6 lakhs annually. Seasoned professionals in senior data analyst roles often see salaries upwards of ₹7 lakhs per year or more.


• Nationwide, salaries are competitive as well. Across India, average entry-level salaries for data analysts start from ₹2–3 lakhs per year, and mid-level analysts generally earn between ₹4–7 lakhs. Senior analysts with extensive experience and niche expertise can command salaries that exceed ₹8 lakhs per annum, especially in larger organizations or multinational companies.


• These figures can vary based on the industry, company size, and additional skills such as expertise in Python, SQL, or advanced visualization tools—often leading to even higher earning potential.

• The demand for skilled data analysts remains exceptionally high, both in Hyderabad and throughout India. With the digital transformation sweeping every industry, organizations are seeking professionals who can turn raw data into strategic insights. Currently, there are tens of thousands of open positions nationally an indicator of incredible growth in this sector.


• Freshers as well as experienced professionals stand to benefit, as a significant share of these openings is suitable for entry level candidates, eager to kickstart their careers. Major employers including top IT firms and global consulting giants like Tech Mahindra, TCS, Wipro, Genpact, and HCL Infosystems are leading the hiring spree, making now an opportune time to enter the industry.


• Whether you’re aiming to break into your first analytics role or looking to level up, Hyderabad and India at large offer a thriving job market ready to welcome well prepared, certified data analysts.

• Course material is delivered through a user-friendly online learning portal—think of it as your very own Netflix library, but for data analytics. All modules, project files, and supplementary resources are available for download or direct access, meaning you can learn from anywhere, at any pace that fits your schedule. Whether you’re looking to binge-watch instructional videos at midnight or revisit hands-on labs over morning coffee, the platform is open 24/7 for review and practice.


• You’ll find everything organized in one place, so you can focus on mastering skills without hunting for files or handouts. Plus, with downloadable content, you’re never left behind—even if your Wi-Fi decides to take the day off.

Salary expectations in the data analytics field can vary based on experience, location, and industry demand. Typically:

• Junior Data Analyst: Professionals just starting out, with up to a few years of experience, can expect salaries ranging from ₹4 lakhs to ₹10 lakhs per annum, depending on factors such as skills, certifications, and city.


• Senior Data Analyst: With more than four years of experience, senior analysts generally earn between ₹6.5 lakhs and ₹12 lakhs per year. Compensation at this level often reflects specialized technical expertise, leadership responsibilities, and the complexity of projects handled.

Major cities like Bengaluru, Delhi, and Mumbai remain hotspots for these roles, given their thriving tech industries and higher concentration of analytics firms. Salary trends continue to rise as the demand for skilled professionals grows across sectors.

Data Analytics Job Opportunities in India

Data analytics is a rapidly growing field in India, offering a wealth of career opportunities for skilled professionals. Currently, there are nearly 100,000 open positions for data analytics roles across the country, with demand continuing to rise as industries recognize the value of data-driven insights.

The main hubs for data analytics jobs in India are:

• Bengaluru: Known as the tech capital, Bengaluru hosts nearly a quarter of the country’s analytics job openings, making it a prime destination for aspiring data professionals.


• Delhi: As a major business and technology center, Delhi is quickly becoming another hotspot for analytics roles.


• Mumbai: With its strong presence in finance and business, Mumbai also offers a growing number of opportunities for data analysts.

These cities are leading the way, but job prospects are expanding nationwide as more organizations embrace digital transformation and data-centric decision-making. Whether you’re looking to start your analytics journey or take the next step in your career, India’s vibrant job market presents abundant possibilities.

How This Course Prepares You for Real-World Analytics

Our Data Analytics Certification Course is designed to build a strong foundation in the essential tools and techniques needed for a successful tech career. You’ll dive deep into Python programming—a must-have language in the analytics world. Beyond just the basics, we cover advanced topics like data mining, machine learning, and practical applications using popular libraries such as Pandas, NumPy, and OpenCV.

To ensure you’re truly industry-ready, our curriculum integrates hands-on projects that mirror real business challenges. You’ll gain experience working with text data through Natural Language Processing (NLP) and handling image data with computer vision techniques. This practical approach empowers you to confidently design, code, and deploy analytics solutions for a variety of IT environments.

Whether you’re aiming to strengthen your analytical skills or transition into a specialized data-driven role, this course equips you with the relevant knowledge and portfolio-worthy experience to stand out in today’s data-centric job market.

Leading Employers in India

Major players in the Indian tech landscape actively recruit data analytics professionals every year. Notable companies include Tech Mahindra, TCS, Genpact, Wipro, and HCL Infosystems, among others. These organizations consistently seek skilled data analysts to drive innovation and decision-making across various industries.

Pursuing Data Analytics After Your MBA

Absolutely—you can elevate your career trajectory by diving into data analytics post-MBA. In fact, professionals with an MBA background are uniquely positioned to bridge business acumen and analytical expertise, making them highly sought-after in today’s data-driven marketplace.

Unlocking Specialized Opportunities

• Financial Analytics: Analyzing trends, budgeting, and risk management in the finance sector.


• HR Analytics: Leveraging employee data to optimize workforce planning and performance management.


• Supply Chain Analytics: Streamlining logistics, inventory, and operations through data-backed strategies.

Enhanced Career Prospects

Companies like Deloitte, Accenture, and Ernst & Young value professionals who can transform raw data into actionable business insights. Upon completing your course, roles such as Business Analyst, Data Analyst, or Consultant in analytics-related fields become accessible. The result? Greater versatility and increased impact—whether you drive strategy for a multinational or innovate at a fast-growing startup.

• Domain-Specific Data Analytics Courses
  Our Data Analytics program doesn’t just teach you theory—it immerses you in real-world applications that matter to today’s industries. Here’s a glimpse of how we bring data analytics to life across top domains:


• Supply Chain Management
  Dive into the science of streamlining operations. In our supply chain track, you’ll learn how data analytics helps untangle complex logistics, predict material requirements, manage risk, and optimize everything from inventory to deliveries. Imagine using large-scale data to make supply chains as nimble as Amazon’s or as efficient as Toyota’s—these are the strategies you’ll master.


• Customer & Social Media Analytics
  Turn information into loyalty and growth. Our customer analytics curriculum explores segmentation, churn prediction, and lifetime value modeling. In the social media module, you’ll parse tweets and Instagram posts, decipher trends, and help brands like Nike and Netflix shape their next viral campaign—all while using Python and advanced visualization tools.


• Retail & Marketing Analysis
  Personalized shopping is the future. In retail analytics, you’ll segment shoppers, forecast demand, and optimize stock to minimize lost sales or wastage. The marketing analytics track teaches you to harness CRM data, web analytics, and campaign performance to craft strategies that drive brand loyalty and boost ROI.


• Life Sciences & Healthcare
  Data saves lives. With our healthcare analytics specialization, tackle patient data to improve outcomes, model disease progression, speed up drug discovery, and identify adverse reactions. You’ll also learn how to optimize clinical trials and streamline resource allocation, following standards set by global leaders in the health sector.


• HR, Finance, and Cybersecurity


• HR Analytics: Analyze employee retention, streamline hiring, and foster inclusive workplaces using predictive analytics and data-driven performance management.


• Finance Analytics: Sift through mountains of unstructured financial data, detect anomalies, model risk, and spot investment opportunities much like analysts in fintech giants.


• Cybersecurity Analytics: Use machine learning and data visualization to flag potential breaches, monitor networks, and keep information secure in a volatile digital landscape.


• Construction & Aviation


• Construction Analytics: Apply data-driven decision-making to project schedules, cost management, and architectural design—raising efficiency and sustainability in the built environment.
  
• Aviation Analytics: Analyze flight and customer data to optimize routes, forecast passenger demand, and fine-tune pricing—helping airlines deliver smooth travel experiences and operational excellence.

Our program ensures you don’t just understand analytics—you become the go-to problem-solver in any industry, equipped to turn data into actionable insights, just like the world’s best organizations.

Data analytics plays a transformative role in supply chain management by bringing clarity and agility to complex processes. By analyzing large volumes of data from various sources—such as manufacturing outputs, shipping timelines, market trends, and even fluctuating input costs—data analysts can identify patterns and inefficiencies that might otherwise remain hidden.

Here’s how data analytics drives smarter supply chain decisions:

• Demand Forecasting: By leveraging statistical models and real-time data, businesses can accurately predict future demand, reducing the risks of overstocking or stockouts.
• Inventory Optimization: Data-driven insights assist companies in maintaining optimal inventory levels, minimizing carrying costs while ensuring product availability.
• Risk Mitigation: Analytics uncover potential disruptions—like supplier delays or changes in regulations—enabling proactive risk management and contingency planning.
• Process Automation: Advanced analytics and machine learning streamline everything from procurement to delivery, improving response times and reducing human error.
• Cost Reduction: By highlighting inefficiencies in transportation, warehousing, and logistics, data analytics empowers organizations to make cost-effective choices.

Global brands like Amazon and UPS famously rely on sophisticated data analytics platforms to manage their sprawling supply chain networks, continually adjusting to changes in demand, shipping conditions, and supply availability. As a result, supply chains become more resilient, efficient, and responsive to the fast-paced demands of today’s marketplace.

If you’re interested in pursuing a career in this field, developing data analytics skills can open doors to dynamic roles across multiple industries.

Real-World Applications: Data Analytics in HR

Data analytics isn’t just transforming industries like finance and marketing—it’s making a powerful impact in Human Resources as well. Here’s how organizations are leveraging analytics in key HR functions:

• Recruitment
  Gone are the days of poring over hundreds of resumes by hand. Today, leading companies like Google and Unilever use data-driven tools to streamline recruitment. By analyzing historical hiring data, candidate profiles, and even social media activity, HR teams can zero in on applicants who truly align with the job requirements and company culture. This data-backed approach not only saves time but helps organizations build stronger, more effective teams from day one.
• Performance Management
  Performance reviews don’t have to be once-a-year, anxiety-inducing rituals. With analytics, HR departments can now track employee performance in real time. Dashboards visualize key metrics, helping managers identify top performers, monitor progress toward goals, and spotlight opportunities for upskilling. Whether it’s sales results, project delivery times, or customer feedback, data provides a clear, objective lens for continuous improvement.
• Promoting Diversity & Inclusion
  Creating a diverse, equitable workplace is more than just the right thing to do—it’s smart business. Industry leaders like Salesforce use analytics to break down workforce demographics, monitor hiring patterns, and uncover hidden biases in the talent pipeline. HR teams can then develop targeted initiatives to close gaps, ensuring equal opportunity and a more inclusive culture.

By integrating data analytics into HR, organizations unlock smarter hiring, better employee development, and a workplace where everyone has a chance to thrive.

Impact of Data Analytics in Life Sciences and Healthcare

Data analytics is driving remarkable progress in life sciences and healthcare, opening doors to innovations that directly improve patient care and advance medical research.

• Enhanced Patient Outcomes: By leveraging tools like predictive modeling, healthcare professionals can anticipate disease progression and customize treatment plans. This data-driven approach ensures patients receive timely, effective interventions tailored to their unique needs.
• Streamlined Clinical Trials: Analytics are reshaping how clinical trials are managed by identifying optimal trial designs, selecting appropriate patient groups, and tracking real-time responses. This reduces both costs and time to market for new therapies, benefiting pharmaceutical research and, ultimately, patients.
• Informed Decision-Making: Access to large volumes of patient data helps healthcare teams make evidence-based choices, leading to better diagnosis, treatment, and ongoing care.
• Improved Drug Development: Data analysis helps researchers pinpoint promising drug candidates, monitor side effects, and adapt quickly to findings—accelerating innovation in drug discovery.

Overall, data analytics is empowering life sciences and healthcare professionals to deliver superior patient outcomes, make smarter decisions, and drive breakthrough innovations that shape the future of healthcare.

Data Analytics Techniques in Marketing Analytics

Modern marketing analytics leverages a robust toolkit to turn massive volumes of raw data into strategic gold. Some of the most impactful techniques include:

• Machine Learning Algorithms: These models identify patterns in customer data, predict future purchasing behaviors, and segment audiences for precisely targeted campaigns.
• Predictive Analytics: By analyzing historical data, these methods forecast outcomes such as customer churn, campaign performance, or even product demand, helping teams make informed decisions before launching their next marketing effort.
• Data Visualization Tools: Interactive dashboards and visual reports (using solutions like Tableau and Power BI) make complex data easy to digest, helping marketers spot trends, track campaign metrics, and communicate results across teams.

Professionals also utilize insights from diverse data streams—ranging from social media and web analytics to CRM platforms—to craft deeply personalized campaigns. By combining these advanced analytics techniques, marketers can fine-tune their strategies, engage customers more effectively, and achieve measurable improvements in campaign performance.

Data Analytics in Social Media & Web Analytics

Understanding how data analytics shapes social media and web analytics can be a real game changer for marketers and businesses alike. Social platforms like Facebook, Twitter, and Instagram churn out an avalanche of data every single day. The trick lies in making sense of this information and turning it into actionable insights.

With the right data analytics techniques, organizations can:

• Track and measure campaign performance: From reach and engagement to conversions, analytics tools let you know exactly what’s working and what needs a tweak.
• Analyze customer sentiment: By examining language, tone, and patterns in comments, reviews, and posts, businesses can gauge how customers really feel about products and services.
• Spot emerging trends: Whether it’s a viral challenge or a new competitor, analytics helps businesses stay ahead by highlighting what’s trending in real time.
• Refine customer targeting: Demographic and behavioral data allow marketers to tailor campaigns with laser-like precision, increasing relevance and ROI.
• Enhance product development: Feedback and suggestions surfaced through social analytics often spark improvements or entirely new offerings.

Ultimately, data analytics empowers businesses to listen more attentively, respond more effectively, and build stronger, more loyal customer bases. It’s not just about numbers—it’s about understanding the story behind the data and using it to drive better decisions.

How Data Analytics Elevates Customer Analytics

Understanding your customers is at the heart of successful business strategies—and data analytics is the engine behind that insight. By systematically collecting and exploring large volumes of customer data, businesses can uncover patterns and preferences that were previously hidden.

Here’s how data analytics transforms customer analytics and empowers smarter business decisions:

• Deeper Customer Insights: Techniques such as segmentation allow companies to group their customers based on shared traits, behaviors, or purchasing history, revealing valuable opportunities for tailored offerings and personalized marketing.
• Predictive Power: With predictive modeling, organizations can anticipate customer needs, forecast purchasing patterns, and even identify those at risk of churning, enabling proactive engagement.
• Value Assessment: Calculating metrics like customer lifetime value helps prioritize high-impact individuals, so resources are focused on building lasting, profitable relationships.
• Acting on Feedback: Mining and analyzing customer feedback—whether from surveys, reviews, or social media—enables businesses to refine products, enhance service, and boost overall satisfaction.

From online retailers using Amazon’s recommendation algorithms, to airlines leveraging sentiment analysis of social media posts, these approaches help shape data-driven decisions. By harnessing these capabilities, companies aren’t just reacting to customer behavior—they’re anticipating it, resulting in stronger loyalty and measurable growth.

How Data Analytics Transforms the Construction and Architecture Industry

Harnessing the power of data analytics has become a game changer for professionals in construction and architecture. With the ability to collect and interpret data from an array of sources—like project timelines, budgets, and resource allocation—teams can now make smarter, data-driven decisions at every stage.

Key Benefits:

• Boosted Project Efficiency: By analyzing patterns in scheduling and resource use, project managers can spot bottlenecks early, streamline workflows, and keep everything moving on time and within budget.
• Cost Reduction: Data insights help identify areas where resources are being underutilized or wasted, making it easier to cut unnecessary expenses and maximize returns.
• Enhanced Design Quality: Architects are using analytics to refine and optimize building designs, ensuring projects are both high-performing and sustainable—think of how big firms like Skanska and Foster + Partners leverage data to push boundaries.
• Compliance and Risk Management: By continuously monitoring compliance metrics and performance data, companies can proactively address issues, stay aligned with industry standards, and avoid costly setbacks.

Ultimately, data analytics empowers professionals to transform raw project information into actionable insights—fueling greater innovation and reliability across the field.

Data Analytics in Cybersecurity

Data analytics is an essential pillar in modern cybersecurity strategies, helping organizations stay one step ahead of sophisticated threats. By applying techniques like machine learning, statistical modeling, and advanced data visualization, cybersecurity professionals can sift through vast amounts of information—from network logs and firewall records to alerts from Security Information and Event Management (SIEM) tools.

Here’s how data analytics powers smarter security:

• Threat Detection: Data analysts monitor real-time patterns and flag unusual activities across networks, often spotting issues that would slip past traditional defenses.
• Incident Prevention: Predictive analytics use historical data to forecast potential attacks before they happen, enabling teams to reinforce vulnerable points.
• Rapid Response: When a breach occurs, analytics help trace its origin, scope, and impact, guiding rapid decision-making to minimize damage.

Whether analyzing anomalies with tools like Splunk or leveraging machine learning to automate alert responses, data analytics transforms raw security data into actionable insights—arming organizations to protect their digital assets more effectively.

Missed a session? No worries! Every class is recorded and uploaded to our student portal, so you can revisit the lesson at your own convenience. Whether life throws a curveball or you just need a quick refresher, these recordings are there to help you catch up without missing a beat. This way, your learning stays flexible, and you remain on track with the rest of the cohort.

Opting for a data analytics program that’s backed by NASSCOM and recognized by the Government of India offers you more than just a certificate to hang on the wall—it’s a mark of trust and credibility that hiring managers notice right away.

Here’s what you gain when you pursue this kind of program:

• Unmatched Recognition: Your credentials stand out with industry and government approval, making your profile more attractive to top employers.
• Comprehensive Learning Hours: Dive deep with over 180 hours of expertly crafted online and classroom sessions designed to give you a well-rounded foundation.
• Real-World Projects: Develop practical expertise by working on capstone projects that reflect the challenges you’ll face on the job.
• Career Support: Enjoy career guidance, job placement assistance, and exclusive connections to industry events and job fairs—paving the way for a smoother transition into data analytics roles.
• Exclusive Incentives: Qualify for additional incentives and career platforms, such as access to the Talent Connect Portal and participation in government-sponsored assessments and career programs.
• Lifelong Industry Network: Stay ahead via ongoing access to resources like boot camps, networking events, and career development support to help you evolve as the industry does.

When your training is shaped by key industry leaders and recognized by the government, you’re not just learning—you’re building a future-proof career in analytics.

Expertise in Data Analytics

• Data Collection and Acquisition
• Data Cleaning and Preparation
• Exploratory Data Analysis (EDA)
• Statistical Analysis
• Data Visualization
• Predictive Analytics
• Business Intelligence (BI)
• Data Warehousing
• SQL and NoSQL Databases
• Data Mining Techniques
• Machine Learning Basics
• Big Data Technologies
• Reporting and Dashboarding

Dive Deeper: What Sets Our Program Apart

Our curriculum goes beyond the basics—placing special emphasis on advanced concepts that prepare you for real-world challenges:

• Hypothesis Testing: Master the four core hypothesis tests and their strategic applications.
• Comprehensive Machine Learning: Explore data mining, unsupervised learning, recommendation engines, and network analytics.
• Text Mining & NLP: Develop skills in text mining and natural language processing, including hands-on work with Naive Bayes, Perceptron, and multilayer perceptrons.
• Programming Proficiency: Gain practical experience in both Python and R, strengthening your coding skills for industry demands.
• Practical Data Ingestion: Learn to source and manage data from SQL, NoSQL, flat files, and more.
• Data Preprocessing: Dedicate significant time to understanding and applying detailed data preprocessing techniques.

Real-World Applications

Data analytics is not just about crunching numbers—it’s about delivering actionable insights across multiple domains:

• Marketing Analytics: Leverage machine learning, predictive analytics, and data visualization to inform data-driven strategies and measure campaign effectiveness.
• Cyber Security Analytics: Apply big data analytics and machine learning to detect, prevent, and respond to cyber threats, utilizing tools like SIEM, network logs, and intrusion detection systems.

Our approach blends foundational knowledge with practical assignments and capstone projects, ensuring you’re industry-ready, whether your interests lie in finance, marketing, cyber security, or beyond.