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Examination & Certification Policy – inGrade

Effective as of December 1st, 2024

Internal Semester and Final Exam Policy

Registration for Exams

  • Students must register for all exams.
  • No registration fee is required for semester exams or the final exam.
  • A fee will be charged for reappearing in any semester or final exam.

Exam Eligibility

For programs with a duration of 12 months:

  • Semester 1 exam eligibility: 90 days after the batch start date.
  • Semester 2 exam eligibility: 180 days after the batch start date.
  • Semester 3 exam eligibility: 240 days after the batch start date.
  • Final exam eligibility: 270 days after the batch start date.

For programs with a duration of 6 months:

  • Semester 1 exam eligibility: 45 days after the batch start date.
  • Semester 2 exam eligibility: 90 days after the batch start date.
  • Semester 3 exam eligibility: 120 days after the batch start date.
  • Final exam eligibility: 150 days after the batch start date.
  • Exam eligibility dates may be adjusted for students who choose a different batch start date from their original batch.

Ingrade Certification Eligibility

Attendance Requirement

  • Students must maintain a minimum of 70% attendance throughout the course duration to be eligible for Ingrade certification.

Score Requirement

  • Students must achieve a minimum combined score of 50% across all internal semester and final exams to qualify for Ingrade certification.

Third-Party Certification and Examination

Third-Party Certification

  • Third-party certification fee waivers are available for students enrolled in specific Ingrade programs. Refer to program brochures or the support portal for details.
  • Students can earn third-party certifications from organizations approved or recommended by Ingrade. Ingrade reserves the right to modify the list of approved certifications.
  • These certification exams may be conducted on Ingrade’s platform, third-party organization platforms, or affiliated platforms.
  • Additional classes and preparation may be required for certain third-party certifications.
  • Certification bodies such as IBM, Microsoft, and others retain their respective trademarks and rights. Students may need to create separate accounts on their platforms, using the same user details submitted to Ingrade to ensure accurate verification.
  • In the event of exam failure or a reattempt, subsequent exams will incur fees as specified by the third-party organization, excluding taxes.

Eligibility for Third-Party Certification

  • Students must pass the internal Ingrade final exam before becoming eligible for third-party certifications.
  • Eligibility for third-party certifications is restricted to students enrolled in courses that include these certifications.

Exam Failure and Absenteeism

  • Ingrade is not responsible if a student fails any certification exam (internal or third-party). Students must prepare adequately.
  • Exam dates cannot be rescheduled once registration is complete. Students should ensure availability and preparedness before registering.
  • Failure to appear for an exam without a valid reason will require students to pay for any subsequent exam attempts.

Exam Format

  • All certification examinations will be conducted via a Computer-Based Test (CBT) format.

Certification Process Platform

  • Ingrade certification exams will be conducted using Ingrade’s platform, partner software, or trusted third-party portals.
  • Ingrade is committed to providing a seamless examination experience with superior infrastructure and technology.
  • In cases of technical or logistical challenges, Ingrade reserves the right to request students to reappear for the exam at no additional charge.

Additional Guidelines

Attendance Requirement

  • Students must have a minimum of 70% attendance in delivered lectures to appear for any semester exams.

Internal Assessments

  • Students must complete internal assessments before appearing for the corresponding semester exams. Marks from internal assessments will be carried forward if a student fails to pass the course.

AI and Remote Proctoring

  • Students must consent to AI and remote proctoring for online exams.
  • Exams will be remotely proctored with live video and audio monitoring by human proctors supported by AI technology. Sessions will be recorded.
  • Photo verification will be conducted by the proctor before the exam begins.

Ingrade is dedicated to maintaining integrity, fairness, and transparency in the certification and examination process. Our goal is to provide a valuable learning experience that equips students with essential skills for success in the tech industry.



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United Kingdom

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Office 1

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Contact us:+91 9870542463

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Industry-Leading Curriculum

Stay ahead with cutting-edge content designed to meet the demands of the tech world.

Our curriculum is created by experts in the field and is updated frequently to take into account the latest advances in technology and trends. This ensures that you have the necessary skills to compete in the modern tech world.

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Expert Instructors

Learn from top professionals who bring real-world experience to every lesson.


You will learn from experienced professionals with valuable industry insights in every lesson; even difficult concepts are explained to you in an innovative manner by explaining both basic and advanced techniques.

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Hands-on learning

Master skills with immersive, practical projects that build confidence and competence.

We believe in learning through doing. In our interactive projects and exercises, you will gain practical skills and real-world experience, preparing you to face challenges with confidence anywhere in the professional world.

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Placement-Oriented Sessions

Jump-start your career with results-oriented sessions guaranteed to get you the best jobs.


Whether writing that perfect resume or getting ready for an interview, we have placement-oriented sessions to get you ahead in the competition as well as tools and support in achieving your career goals.

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Flexible Learning Options

Learn on your schedule with flexible, personalized learning paths.

We present you with the opportunity to pursue self-paced and live courses - your choice of study, which allows you to select a time and manner most befitting for you. This flexibility helps align your schedule of studies with that of your job and personal responsibilities, respectively.

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Lifetime Access to Resources

You get unlimited access to a rich library of materials even after completing your course.


Enjoy unlimited access to all course materials, lecture recordings, and updates. Even after completing your program, you can revisit these resources anytime to refresh your knowledge or learn new updates.

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Community and Networking

Connect to a global community of learners and industry leaders for continued support and networking.


Join a community of learners, instructors, and industry professionals. This network offers you the space for collaboration, mentorship, and professional development-making the meaningful connections that go far beyond the classroom.

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High-Quality Projects

Build a portfolio of impactful projects that showcase your skills to employers.


Build a portfolio of impactful work speaking to your skills to employers. Our programs are full of high-impact projects, putting your expertise on show for potential employers.

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Freelance Work Training

Gain the skills and knowledge needed to succeed as freelancers.


Acquire specific training on the basics of freelance work-from managing clients and its responsibilities, up to delivering a project. Be skilled enough to succeed by yourself either in freelancing part-time or as a full-time career.

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Daniel Harris

Data Scientist

Daniel Harris is a seasoned Data Scientist with a proven track record of solving complex problems and delivering statistical solutions across industries. With many years of experience in data modeling machine learning and big Data Analysis Daniel's expertise is turning raw data into Actionable insights that drive business decisions and growth.


As a mentor and trainer, Daniel is passionate about empowering learners to explore the ever-evolving field of data science. His teaching style emphasizes clarity and application. Make even the most challenging ideas accessible and engaging. He believes in hands-on learning and ensures that students work on real projects to develop practical skills.


Daniel's professional experience spans a number of sectors. including finance Healthcare and Technology The ability to integrate industry knowledge into learning helps learners bridge the gap between theoretical concepts and real-world applications.


Under Daniel's guidance, learners gain the technical expertise and confidence needed to excel in careers in data science. His dedication to promoting growth and innovation ensures that learners leave with the tools to make a meaningful impact in the field.

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William Johnson

Python Developer

William Johnson is a Python enthusiast who loves turning ideas into practical and powerful solutions. With many years of experience in coding and troubleshooting, William has worked on a variety of projects. Many things, from web application design to automated workflows. Focused on creating easy-to-use and scalable systems.

William's development approach is pragmatic and thoughtful. He enjoys breaking complex problems down into their component parts. that can be managed and find solutions It makes the process both exciting and worthwhile. In addition to his technical skills, William is passionate about helping others learn Python. and inspires beginners to develop confidence in coding.

Having worked in areas such as automation and backend development, William brings real-world insights to his work. This ensures that his solution is not only innovative. But it is also based on actual use.

For William, Python isn't just a programming language. But it is also a tool for solving problems. Simplify the process and create an impact His approachable nature and dedication to his craft make him an inspirational figure for anyone looking to dive into the world of development.

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Jack Robinson

Machine Learning Engineer

Jack Robinson is a passionate machine learning engineer committed to building intelligent systems that solve real-world problems. With a deep love for algorithms and data, Jack has worked on a variety of projects. From building predictive models to implementing AI solutions that make processes smarter and more efficient.

Jack's strength is his ability to simplify complex machine learning concepts. Make it accessible to both technical and non-technical audiences. Whether designing recommendation mechanisms or optimizing models He ensures that every solution works and is effective.

With hands-on experience in healthcare, finance and other industries, Jack combines technical expertise with practical applications. His work often bridges the gap between research and practice. By bringing innovative ideas to life in ways that drive tangible results.

For Jack, machine learning isn't just about technology. It's also about solving meaningful problems and making a difference. His enthusiasm for the field and approachable nature make him a valuable mentor and an inspiring professional to work with.

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Emily Turner

Data Scientist

Emily Turner is a passionate and innovative Data Scientist. It succeeds in revealing hidden insights within the data. With a knack for telling stories through analysis, Emily specializes in turning raw data sets into meaningful stories that drive informed decisions.

In each lesson, her expertise in data manipulation and exploratory data analysis is evident, as well as her dedication to making learners think like data scientists. Muskan's teaching style is engaging and interactive; it makes it easy for students to connect with the material and gain practical skills.

Emily's teaching style is rooted in curiosity and participation. She believes in empowering learners to access information with confidence and creativity. Her sessions are filled with hands-on exercises and relevant examples to help students understand complex concepts easily and clearly.

After working on various projects in industries such as retail and logistics Emily brings real-world context to her lessons. Her experience is in predictive modeling. Data visualization and enhancements provide students with practical skills that can be applied immediately to their careers.

For Emily, data science isn't just about numbers. But it's also about impact. She is dedicated to helping learners not only hone their technical skills but also develop the critical thinking needed to solve meaningful problems and create value for organizations.

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Madison King

Business Intelligence Developer

Madison King is a results-driven business intelligence developer with a talent for turning raw data into actionable insights. Her passion is creating user-friendly dashboards and reports that help organizations. Make smarter, informed decisions.

Madison's teaching methods are very practical. It focuses on helping students understand the BI development process from start to finish. From data extraction to visualization She breaks down complex tools and techniques. To ensure that her students gain confidence and hands-on experience with platforms like Power BI and Tableau.

With an extensive career in industries such as retail and healthcare, Madison has developed BI solutions that help increase operational efficiency and improve decision making. And her ability to bring real situations to her lessons makes learning engaging and relevant for students.

For Madison, business intelligence is more than just tools and numbers. It is about providing clarity and driving success. Her dedication to mentoring and approachable style enable learners to not only master BI concepts, but also develop the skills to transform data into impactful stories.

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Predictive Maintenance

Basic Data Science Skills Needed

1.Data Cleaning and Preprocessing

2.Descriptive Statistics

3.Time-Series Analysis

4.Basic Predictive Modeling

5.Data Visualization (e.g., using Matplotlib, Seaborn)

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Fraud Detection

Basic Data Science Skills Needed

1.Pattern Recognition

2.Exploratory Data Analysis (EDA)

3.Supervised Learning Techniques (e.g., Decision Trees, Logistic Regression)

4.Basic Anomaly Detection Methods

5.Data Mining Fundamentals

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Personalized Medicine

Basic Data Science Skills Needed

1.Data Integration and Cleaning

2.Descriptive and Inferential Statistics

3.Basic Machine Learning Models

4.Data Visualization (e.g., using Tableau, Python libraries)

5.Statistical Analysis in Healthcare

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Customer Churn Prediction

Basic Data Science Skills Needed

1.Data Wrangling and Cleaning

2.Customer Data Analysis

3.Basic Classification Models (e.g., Logistic Regression)

4.Data Visualization

5.Statistical Analysis

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Climate Change Analysis

Basic Data Science Skills Needed

1.Data Aggregation and Cleaning

2.Statistical Analysis

3.Geospatial Data Handling

4.Predictive Analytics for Environmental Data

5.Visualization Tools (e.g., GIS, Python libraries)

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Stock Market Prediction

Basic Data Science Skills Needed

1.Time-Series Analysis

2.Descriptive and Inferential Statistics

3.Basic Predictive Models (e.g., Linear Regression)

4.Data Cleaning and Feature Engineering

5.Data Visualization

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Self-Driving Cars

Basic Data Science Skills Needed

1.Data Preprocessing

2.Computer Vision Basics

3.Introduction to Deep Learning (e.g., CNNs)

4.Data Analysis and Fusion

5.Statistical Analysis

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Recommender Systems

Basic Data Science Skills Needed

1.Data Cleaning and Wrangling

2.Collaborative Filtering Techniques

3.Content-Based Filtering Basics

4.Basic Statistical Analysis

5.Data Visualization

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Image-to-Image Translation

Skills Needed

1.Computer Vision

2.Image Processing

3.Generative Adversarial Networks (GANs)

4.Deep Learning Frameworks (e.g., TensorFlow, PyTorch)

5.Data Augmentation

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Text-to-Image Synthesis

Skills Needed

1.Natural Language Processing (NLP)

2.GANs and Variational Autoencoders (VAEs)

3.Deep Learning Frameworks

4.Image Generation Techniques

5.Data Preprocessing

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Music Generation

Skills Needed

1.Deep Learning for Sequence Data

2.Recurrent Neural Networks (RNNs) and LSTMs

3.Audio Processing

4.Music Theory and Composition

5.Python and Libraries (e.g., TensorFlow, PyTorch, Librosa)

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Video Frame Interpolation

Skills Needed

1.Computer Vision

2.Optical Flow Estimation

3.Deep Learning Techniques

4.Video Processing Tools (e.g., OpenCV)

5.Generative Models

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Character Animation

Skills Needed

1.Animation Techniques

2.Natural Language Processing (NLP)

3.Generative Models (e.g., GANs)

4.Audio Processing

5.Deep Learning Frameworks

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Speech Synthesis

Skills Needed

1.Text-to-Speech (TTS) Technologies

2.Deep Learning for Audio Data

3.NLP and Linguistic Processing

4.Signal Processing

5.Frameworks (e.g., Tacotron, WaveNet)

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Story Generation

Skills Needed

1.NLP and Text Generation

2.Transformers (e.g., GPT models)

3.Machine Learning

4.Data Preprocessing

5.Creative Writing Algorithms

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Medical Image Synthesis

Skills Needed

1.Medical Image Processing

2.GANs and Synthetic Data Generation

3.Deep Learning Frameworks

4.Image Segmentation

5.Privacy-Preserving Techniques (e.g., Differential Privacy)

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Fraud Detection

Skills Needed

1.Data Cleaning and Preprocessing

2.Exploratory Data Analysis (EDA)

3.Anomaly Detection Techniques

4.Supervised Learning Models

5.Pattern Recognition

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Customer Segmentation

Skills Needed

1.Data Wrangling and Cleaning

2.Clustering Techniques

3.Descriptive Statistics

4.Data Visualization Tools

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Sentiment Analysis

Skills Needed

1.Text Preprocessing

2.Natural Language Processing (NLP) Basics

3.Sentiment Classification Models

4.Data Visualization

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Churn Analysis

Skills Needed

1.Data Cleaning and Transformation

2.Predictive Modeling

3.Feature Selection

4.Statistical Analysis

5.Data Visualization

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Supply Chain Optimization

Skills Needed

1.Data Aggregation and Cleaning

2.Statistical Analysis

3.Optimization Techniques

4.Descriptive and Predictive Analytics

5.Data Visualization

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Energy Consumption Forecasting

Skills Needed

1.Time-Series Analysis Basics

2.Predictive Modeling Techniques

3.Data Cleaning and Transformation

4.Statistical Analysis

5.Data Visualization

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Healthcare Analytics

Skills Needed

1.Data Preprocessing and Integration

2.Statistical Analysis

3.Predictive Modeling

4.Exploratory Data Analysis (EDA)

5.Data Visualization

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Traffic Analysis and Optimization

Skills Needed

1.Geospatial Data Analysis

2.Data Cleaning and Processing

3.Statistical Modeling

4.Visualization of Traffic Patterns

5.Predictive Analytics

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Customer Lifetime Value (CLV) Analysis

Skills Needed

1.Data Preprocessing and Cleaning

2.Predictive Modeling (e.g., Regression, Decision Trees)

3.Customer Data Analysis

4.Statistical Analysis

5.Data Visualization

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Market Basket Analysis for Retail

Skills Needed

1.Association Rules Mining (e.g., Apriori Algorithm)

2.Data Cleaning and Transformation

3.Exploratory Data Analysis (EDA)

4.Data Visualization

5.Statistical Analysis

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Marketing Campaign Effectiveness Analysis

Skills Needed

1.Data Analysis and Interpretation

2.Statistical Analysis (e.g., A/B Testing)

3.Predictive Modeling

4.Data Visualization

5.KPI Monitoring

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Sales Forecasting and Demand Planning

Skills Needed

1.Time-Series Analysis

2.Predictive Modeling (e.g., ARIMA, Regression)

3.Data Cleaning and Preparation

4.Data Visualization

5.Statistical Analysis

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Risk Management and Fraud Detection

Skills Needed

1.Data Cleaning and Preprocessing

2.Anomaly Detection Techniques

3.Machine Learning Models (e.g., Random Forest, Neural Networks)

4.Data Visualization

5.Statistical Analysis

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Supply Chain Analytics and Vendor Management

Skills Needed

1.Data Aggregation and Cleaning

2.Predictive Modeling

3.Descriptive Statistics

4.Data Visualization

5.Optimization Techniques

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Customer Segmentation and Personalization

Skills Needed

1.Data Wrangling and Cleaning

2.Clustering Techniques (e.g., K-Means, DBSCAN)

3.Descriptive Statistics

4.Data Visualization

5.Predictive Modeling

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Business Performance Dashboard and KPI Monitoring

Skills Needed

1.Data Visualization Tools (e.g., Power BI, Tableau)

2.KPI Monitoring and Reporting

3.Data Cleaning and Integration

4.Dashboard Development

5.Statistical Analysis

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Network Vulnerability Assessment

Skills Needed

1.Knowledge of vulnerability scanning tools (e.g., Nessus, OpenVAS).

2.Understanding of network protocols and configurations.

3.Data analysis to identify and prioritize vulnerabilities.

4.Reporting and documentation for security findings.

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Phishing Simulation

Skills Needed

1.Familiarity with phishing simulation tools (e.g., GoPhish, Cofense).

2.Data analysis to interpret employee responses.

3.Knowledge of phishing tactics and techniques.

4.Communication skills for training and feedback.

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Incident Response Plan Development

Skills Needed

1.Incident management frameworks (e.g., NIST, ISO 27001).

2.Risk assessment and prioritization.

3.Data tracking and timeline creation for incidents.

4.Scenario modeling to anticipate potential threats.

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Penetration Testing

Skills Needed

1.Proficiency in penetration testing tools (e.g., Metasploit, Burp Suite).

2.Understanding of ethical hacking methodologies.

3.Knowledge of operating systems and application vulnerabilities.

4.Report generation and remediation planning.

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Malware Analysis

Skills Needed

1.Expertise in malware analysis tools (e.g., IDA Pro, Wireshark).

2.Knowledge of dynamic and static analysis techniques.

3.Proficiency in reverse engineering.

4.Threat intelligence and pattern recognition.

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Secure Web Application Development

Skills Needed

1.Secure coding practices (e.g., input validation, encryption).

2.Familiarity with security testing tools (e.g., OWASP ZAP, SonarQube).

3.Knowledge of application security frameworks (e.g., OWASP).

4.Understanding of regulatory compliance (e.g., GDPR, PCI DSS).

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Cybersecurity Awareness Training Program

Skills Needed

1.Behavioral analytics to measure training effectiveness.

2.Knowledge of common cyber threats (e.g., phishing, malware).

3.Communication skills for delivering engaging training sessions.

4.Use of training platforms (e.g., KnowBe4, Infosec IQ).

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Data Loss Prevention Strategy

Skills Needed

1.Familiarity with DLP tools (e.g., Symantec DLP, Forcepoint).

2.Data classification and encryption techniques.

3.Understanding of compliance standards (e.g., HIPAA, GDPR).

4.Risk assessment and policy development.

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Chloe Walker

Data Engineer

Chloe Walker is a meticulous data engineer who specializes in building robust pipelines and scalable systems that help data flow smoothly. With a passion for problem-solving and attention to detail, Chloe ensures that the data-driven core of every project is strong.


Chloe's teaching philosophy focuses on practicality and clarity. She believes in empowering learners with hands-on experiences. It guides them through the complexities of data architecture engineering with real-world examples and simple explanations. Her focus is on helping students understand how to design systems that work efficiently in real-time environments.


With extensive experience in e-commerce, fintech, and other industries, Chloe has worked on projects involving large data sets. cloud technology and stream data in real time Her ability to translate complex technical settings into actionable insights gives learners the tools and confidence they need to excel.


For Chloe, data engineering is about creating solutions to drive impact. Her accessible style and deep technical knowledge make her an inspirational consultant. This ensures that learners leave their sessions ready to tackle engineering challenges with confidence.

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Samuel Davis

Data Scientist

Samuel Davis is a Data Scientist passionate about solving complex problems and turning data into actionable insights. With a strong foundation in statistics and machine learning, Samuel enjoys tackling challenges that require analytical rigor and creativity.

Samuel's teaching methods are highly interactive. The focus is on promoting a deeper understanding of the "why" behind each method. He believes teaching data science is about building confidence. And his lessons are designed to encourage curiosity and critical thinking through hands-on projects and case studies.


With professional experience in industries such as telecommunications and energy. Samuel brings real-world knowledge to his work. His ability to connect technical concepts with practical applications equips learners with skills they can put to immediate use.

For Samuel, data science is more than a career. But it is a way to make a difference. His approachable demeanor and commitment to student success inspire learners to explore, create, and excel in their data-driven journey.

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Lily Evans

Data Science Instructor

Lily Evans is a passionate educator and data enthusiast who thrives on helping learners uncover the magic of data science. With a knack for breaking down complex topics into simple, relatable concepts, Lily ensures her students not only understand the material but truly enjoy the process of learning.

Lily’s approach to teaching is hands-on and practical. She emphasizes problem-solving and encourages her students to explore real-world datasets, fostering curiosity and critical thinking. Her interactive sessions are designed to make students feel empowered and confident in their abilities to tackle data-driven challenges.


With professional experience in industries like e-commerce and marketing analytics, Lily brings valuable insights to her teaching. She loves sharing stories of how data has transformed business strategies, making her lessons relevant and engaging.

For Lily, teaching is about more than imparting knowledge—it’s about building confidence and sparking a love for exploration. Her approachable style and dedication to her students ensure they leave her sessions with the skills and mindset to excel in their data science journeys.

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