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Data Science

Uncover hidden patterns and drive business growth with data science techniques.
  • 3 Months
  • 4.3 Rating
  • 3 live Projects
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Curriculum To Crack
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  • 19+ Modules
  • 60+ Hours of Learning
  • 15 Live Projects
  • 12+ Tools
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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
Download Curriculum

Tools & Technologies Covered

Get certificate of completion

Certificates will be conferred upon candidates who have maintained an attendance rate exceeding 90% and have attained a minimum score of 60% in the monthly assessments upon completion of the program.

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