Unlock the Future with Our Post Graduate Programme in Data Science with Gen AI (Executive) |

• Data Collection: Web scraping, APIs, and working with datasets.
• Data Cleaning and Preprocessing: Handling missing data, encoding categorical data, data scaling.
• Exploratory Data Analysis (EDA): Visualization techniques, identifying patterns, data distributions.
• Data Wrangling with Pandas: Advanced data manipulation and aggregation.

• Statistics Essentials: Descriptive and inferential statistics, hypothesis testing, regression analysis.
• Probability Theory: Probability distributions, Bayes’ theorem, and random variables.
• Introduction to Machine Learning: Supervised vs. unsupervised learning, model evaluation metrics.
• Popular Algorithms: Linear regression, decision trees, clustering, SVM, k-nearest neighbors.

• Advanced Algorithms: Ensemble methods, Random Forest, Gradient Boosting.
• Neural Networks Fundamentals: Understanding perceptrons, activation functions, backpropagation.
• Convolutional Neural Networks (CNNs): Concepts, image classification, and applications.
• Recurrent Neural Networks (RNNs): Sequence modeling, applications in NLP.

• Generative AI Overview: Types of generative models, applications, and ethics.
• Autoencoders: Variational Autoencoders (VAEs), denoising autoencoders, applications.
• GANs: Basics, training process, and applications in image generation.
• Diffusion Models: Understanding diffusion processes, denoising diffusion probabilistic models.

• Text Preprocessing: Tokenization, stopword removal, stemming, and lemmatization.
• Traditional NLP Models: TF-IDF, word embeddings, and LSTM-based models.
• Transformers: Understanding transformer architecture, BERT, and GPT models.
• Text Generation with Generative AI: Using language models for text generation, summarization, chatbots.

• GAN Variants: Conditional GANs, CycleGANs, applications in style transfer, data augmentation.
• Diffusion and Other Emerging Models: Overview of cutting-edge generative AI models, use cases.
• Prompt Engineering: How to craft effective prompts, prompt-based fine-tuning.
• Fine-Tuning Pre-trained Models: Techniques for adapting pre-trained models to specific tasks.

• Image Generation: GANs and VAEs for synthesis, super-resolution, image-to-image translation.
• Generative AI in Audio: Text-to-speech, speech synthesis models, music generation.
• Video Generation: Deepfake technology, ethical implications, video generation tools.
• Ethics and Security: Addressing biases, privacy, and responsible AI practices.

• Healthcare Applications: Drug discovery, diagnostics, medical imaging.
• Content Creation: Automated content generation, creative AI tools for marketing.
• Gaming and Entertainment: Procedural content generation, AI-based game character design.
• Generative AI in Business: Chatbots, automated summarization, personalized marketing.

This course usually lasts between three to six months depending on the class schedule you want; whether full time or part time, you will have sufficient time to digest and have a chance to practice your skills.

Yes! Once you complete the course, you get a certificate that you may attach to your resume or update on LinkedIn. The certificate is proof that you can do data science and analytics.

We have job placement support. This includes creating a resume, interview preparations, and connecting you through our network of industry-related contacts. We will put you on the road toward your career.

All course resources are inclusive in the fees. In case you require more resources or other software for personal use, the costs will be supplementary.

You will gain hands-on experience with popular tools like Python, R, SQL, Tableau, and TensorFlow, among others.

Yes! You’ll complete a capstone project that showcases your skills. This project will be a part of your portfolio when you apply for jobs.

Absolutely! We offer both in-person and online classes, so you can choose the format that works best for you.

We keep our class sizes small, typically around 15-20 students, to ensure personalized attention and interaction.

Our approach combines lectures, hands-on labs, group discussions, and real-world projects to reinforce learning.

Yes! We provide ongoing support through our alumni network, including access to resources and job opportunities.

Yes! If you enroll as a group, we offer special discounts. Please contact us for more details.

You can easily enroll through our website or by visiting our academy. Just fill out the registration form, and our team will assist you.

We have a clear refund policy. If you withdraw within the first weeks of the course, you’ll receive a full refund.

While internships are not mandatory, we can assist you in finding internship opportunities that match your skill set.

Your assessment will be based on assignments, quizzes, participation in projects, and the final capstone project.

Yes! If you need to switch formats during the course, just let us know, and we’ll accommodate your request.

Yes! We cover foundational concepts in big data technologies such as Hadoop and Spark.

Absolutely! Many of our students come from non-technical backgrounds and successfully transition into data science careers.

Graduates have gone on to roles such as Data Analyst, Data Scientist, Machine Learning Engineer, and Business Analyst, among others.

Most of the tools we use are free or open-source, so you won’t need to purchase software for the course.

If you have an interest in data analysis, problem-solving, and technology, this course is likely a great fit for you.

Basic computer skills and a reliable internet connection are essential for online learning.

Yes! We offer demo classes so you can get a feel for our teaching style and curriculum.

We record all classes, so you can catch up on any missed material at your convenience.