Description
Course Name: Certificate in Data Analysis Deep Learning
Course Id: CDADL/Q1001.
Eligibility: 10th Grade (high school) or Equivalent.
Duration: Two Month.
Objective: This course is designed to provide comprehensive knowledge of data analysis techniques integrated with deep learning concepts. It covers data preprocessing, neural networks, model training, evaluation, and deployment of deep learning algorithms to extract insights from complex datasets.
How to Enroll and Get Certified in Your Chosen Course:
Step 1: Choose the course you wish to get certified in.
Step 2: Click on the “Enroll Now” button.
Step 3: Proceed with the enrollment process.
Step 4: Enter your billing details and continue to course fee payment.
Step 5: You will be redirected to the payment gateway. Pay the course and exam fee using one of the following methods:
Debit/Credit Card, Wallet, Paytm, Net Banking, UPI, or Google Pay.
Step 6: After successful payment, you will receive your study material login ID and password via email within 48 hours of fee payment.
Step 7: Once you complete the course, take the online examination.
Step 8: Upon passing the examination, you will receive:
• A soft copy (scanned) of your certificate via email within 7 days of examination.
• A hard copy (original with official seal and signature) sent to your address within 45 day of declaration of result.
Step 9: After certification, you will be offered job opportunities aligned with your area of interest.
Online Examination Detail:
Duration- 60 minutes.
No. of Questions- 30. (Multiple Choice Questions).
Maximum Marks- 100, Passing Marks- 40%.
There is no negative marking in this module.
| Marking System: | ||||||
| S.No. | No. of Questions | Marks Each Question | Total Marks | |||
| 1 | 10 | 5 | 50 | |||
| 2 | 5 | 4 | 20 | |||
| 3 | 5 | 3 | 15 | |||
| 4 | 5 | 2 | 10 | |||
| 5 | 5 | 1 | 5 | |||
| 30 | 100 | |||||
| How Students will be Graded: | ||||||
| S.No. | Marks | Grade | ||||
| 1 | 91-100 | O (Outstanding) | ||||
| 2 | 81-90 | A+ (Excellent) | ||||
| 3 | 71-80 | A (Very Good) | ||||
| 4 | 61-70 | B (Good) | ||||
| 5 | 51-60 | C (Average) | ||||
| 6 | 40-50 | P (Pass) | ||||
| 7 | 0-40 | F (Fail) | ||||
Key Benefits of Certification- Earning a professional certification not only validates your skills but also enhances your employability. Here are the major benefits you gain:
Practical, Job-Ready Skills – Our certifications are designed to equip you with real-world, hands-on skills that match current industry demands — helping you become employment-ready from day one.
Lifetime Validity – Your certification is valid for a lifetime — no renewals or expirations. It serves as a permanent proof of your skills and training.
Lifetime Certificate Verification – Employers and institutions can verify your certification anytime through a secure and reliable verification system — adding credibility to your qualifications.
Industry-Aligned Certification –All certifications are developed in consultation with industry experts to ensure that what you learn is current, relevant, and aligned with market needs.
Preferred by Employers – Candidates from ISO-certified institutes are often prioritized by recruiters due to their exposure to standardized, high-quality training.
Free Job Assistance Based on Your Career Interests – Receive personalized job assistance and career guidance in your preferred domain, helping you land the right role faster.
Syllabus:-
Introduction to Data Analysis and Deep Learning: Fundamentals of data analysis, Importance of data in AI and deep learning, Overview of machine learning vs. deep learning, Types of data (structured, unstructured, semi-structured), Data collection and preprocessing, Basics of statistics for data analysis, Introduction to Python/R for data analysis, Key libraries for data science (NumPy, Pandas, Matplotlib), Overview of deep learning applications, Ethical considerations in AI and data science.
Data Preprocessing and Cleaning: Handling missing data, Data normalization and standardization, Feature engineering techniques, Handling categorical data (one-hot encoding, label encoding), Removing duplicate and irrelevant data, Dealing with imbalanced datasets, Outlier detection and treatment, Exploratory Data Analysis (EDA) techniques, Data transformation and scaling, Automating data cleaning processes.
Exploratory Data Analysis (EDA) and Visualization: Understanding data distributions, Descriptive statistics (mean, median, mode, variance, standard deviation), Data visualization techniques (histograms, scatter plots, bar charts), Correlation and covariance analysis, Pair plots and box plots, Feature importance and selection, Identifying patterns in data, Dimensionality reduction techniques (PCA, t-SNE), Interactive data visualization tools (Tableau, Power BI, Seaborn), Storytelling with data.
Fundamentals of Machine Learning: Supervised vs. unsupervised learning, Regression models (linear, polynomial, logistic), Classification models (decision trees, SVM, Naïve Bayes, k-NN), Clustering algorithms (k-means, hierarchical clustering, DBSCAN), Evaluation metrics (accuracy, precision, recall, F1-score), Cross-validation techniques, Bias-variance tradeoff, Overfitting and underfitting, Introduction to ensemble methods (random forests, boosting), Feature selection techniques.
Introduction to Neural Networks: Basics of artificial neural networks (ANNs), Neurons, activation functions and perceptron models, Feedforward neural networks (FNN), Backpropagation and gradient descent, Loss functions and optimization techniques, Hyperparameter tuning in neural networks, Introduction to deep learning frameworks (TensorFlow, PyTorch, Keras), Building a simple neural network model, Understanding model evaluation and tuning, Common challenges in training neural networks.
Deep Learning Architectures: Convolutional Neural Networks (CNNs) and their applications, Recurrent Neural Networks (RNNs) for sequential data, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), Autoencoders for dimensionality reduction, Generative Adversarial Networks (GANs) for synthetic data generation, Transfer learning and pre-trained models, Attention mechanisms and Transformer models, Reinforcement learning basics, Object detection and segmentation techniques, Deploying deep learning models in real-world applications.
Job Opportunities after Certificate in Data Analysis & Deep Learning
Graduates of this program gain expertise in data analysis, machine learning, deep learning, neural networks, and AI model development. This equips them for roles in IT, artificial intelligence, finance, healthcare, business intelligence, and research.
Key Career Options: Data Scientist, Machine Learning Engineer, Deep Learning Engineer, Data Analyst, AI Researcher, Data Engineer, Business Intelligence Analyst, Quantitative Analyst (Quant), NLP Engineer, Computer Vision Engineer.
Salary Range (India):
Entry-level: ₹4–10 LPA
Mid-level: ₹10–20 LPA
Senior-level: ₹20–50 LPA
Industries Hiring Graduates: Technology & AI, E-commerce, Finance & Banking, Healthcare, Manufacturing, Automotive & Robotics, Consulting & Research, AI Startups.
Skills Developed: Python & R programming, machine learning, deep learning, neural networks, statistical analysis, data visualization (Tableau, Power BI, Matplotlib), big data technologies (Hadoop, Spark), algorithm development, predictive modeling, cloud computing, and data-driven decision-making.
Graduates can advance to senior roles such as Senior Data Scientist, AI Researcher, Senior Machine Learning Engineer, Deep Learning Specialist, or Quantitative Analyst. Additional certifications in Deep Learning, AWS Machine Learning, Google ML Engineering, or advanced data science programs can significantly enhance career growth and earning potential.
