Full Stack Academy | Gateway To Software Industry
Data Science
Uncover hidden patterns and drive business growth with data science techniques.
- 3 Months
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- 3 live Projects
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Curriculum To Crack High Paying Job
- 19+ Modules
- 60+ Hours of Learning
- 15 Live Projects
- 12+ Tools
Module 1
- What is Data? Big Data?. Analytics? Data Analysis ? Lifecycle , Frameworks, Modelling, Data Preparation. Applications?. What is Data Science?
Module 2
- Basics of Python, Conditional Statements, Looping Statements, Functions, Lambdas, OOP’s, Modules, Exception Handling, Reading Data in python, Numpy, Pandas, Scikit-Learn, Seaborn and Matplotlib.
Module 3
- Why Statistics? Types of Statistics? Data Collection. DESCRIPTIVE STATS : Measure of Central tendencie, Measure of Dispersion, Skewness, Kurtosis, Co-variance, Correlation, Probability Distributions. INFERENTIAL STATS: Central limit theorem, Hypothesis testing, Z-test, P-test, t-test, Chi-square test, F-test, Annova tests.
Module 4
- What is Machine Learning, Types of Learnings, Supervised Learning, Unsupervised Learning, Linear Regression, Cost Functions, Gradient Descent, Learning Rate, Evaluation metrics, Functions, Gradient Descent, Learning Rate, Evaluation metrics.
Module 5
- Regression with multiple inputs? Vectorisation? Gradient Descent on MLR, Feature Scaling, Polynomial Regression, Evaluation metrics for MLR, Regression Analysis, Bias , Variance, Curves and its types?
Module 6
- CLASSIFICATION: Logistic Regression, Decision Boundary, Cost Functions, Loss Functions, Overfitting, Underfitting, Regularization, Gradient Descent on Classification and implementation.
Module 7
- NEURAL NETWORKS : Introduction, Neurons and brain, Making Predictions, Recognising Images, Data in tensorflow, Building a simple Neural Network, Forward Propagation, Matrix Multiplication.
Module 8
- Tensorflow, Alternatives of Sigmoid and Logit functions, What are Activation Functions? How to choose functions? Softmax, Advanced Optimisation? Derivatives , Additional Layers?
Module 9
- Introduction to Decision Trees, Measure of Purity, Entropy, Information Gain, Onehot encoding, Regression Trees, RandomForest, Bagging,XgBoost, AdaBoost, Boosting techniques?
Module 10
- Introduction to Unsupervised Learning, Clustering, K-Means, Algorithm, DB- Scan, Intuition of Kmeans and DB-Scan, Gaussian, Distribution, Hierarchical Clustering, Evaluating metrics.
Module 11
- Introduction, Making Recommendations, Collaborative Filtering, Normalisation, Tensorflow of Collaborative Filtering, Ethical use.
Module 12
- Introduction to Reinforcement Learning, Examples like MarsRover, Robots, Tesla cars, State and action, Greedy Algorithm, State of reinforcement, Q-Learning.
Module 13
- Introduction, Derivatives, Representation of Neural Networks, Activation functions, Forward and Backward propagations, Epochs, Learning rate, Biases and Variances, Parameters , Hyper parameters, and Feed-Forward Networks.
Module 14
- Introduction to CNN, Computer Vision library, Edge detection, Introduction to CNN, Computer Vision library, Edge detection, Resnets, Inception Networks, MobileNet, EfficientNet, Data Augmentation, Transfer Learning, Dropout Regularization.
Module 15
- localisation, Landmark Detection, Sliding Windows, Bounding Boxes, Anchor Boxes, Multiple objects Detection, YOLO, U-NET.
Module 16
- Face Recognition, Oneshot Learning, Neural Style transfer, Cost functions, 1D and 3D generalisations.
Module 17
- Introduction to RNN? Speech Recognition, Music synthesis, Chatbots, Vanishing Gradients, GRU, LSTM, Bi-Directional RNN.
Module 18
- Chatbots, Vanishing Gradients, GRU, LSTM, Bi-Directional RNN. word2vec, Negative Sampling, Glove vectors, Sentiment Classification?
Module 19
- Introduction, Multihead attention, Sequence models, Beam Search, Error Analysis, Attention model, Trigger Word Detection.
- 19+ Modules
- 60+ Hours of Learning
- 15 Live Projects
- 12+ Tools
Tools & Technologies Covered
Get certificate of completion
Candidates will receive certificates upon completing the program if they maintain over 90% attendance and score at least 60% in monthly assessments. This recognition signifies their dedication, consistency, and academic performance, validating their commitment to learning and skill development. Meeting these criteria ensures they have successfully fulfilled the program's requirements and demonstrated their proficiency.
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