Data Science
What you will get?
  • 150+ Hours of Learning
  • Live Classes by Industry Mentors
  • Capstone projects
  • Mock Interview preparation

150+

Hrs of Learning

23+

Milestone

300+

Credits

6+ Months

7-10 Hours/Week

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Career
Opportunities
Python Developer, ML Engineer, Data Scientist, Data Analyst, etc
Top skills you
will learn?
Understanding Mathematical Models will help in capturing information from data. This course will help students in understanding fundamental concepts about supervised & unsupervised learning Algorithms. Developing Skills to use Python Libraries such as Numpy, Keras, Sklearn, Matplotlib & many such libraries.
Who can join
the program?
Freshers, Students doing B.E. / BTech, BSc, MSc, MTech, BCA, MCA, BCom, Development Enthusiast, Working Professionals
Minimum
Eligibility
Should know fundamentals of Computer Science

Course Curriculum

Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects, assignments, and live sessions
 
Milestone 1
Getting Started with language
10 Assessment | Credit : 4

  • Basics of Programming
  • What is a programming language?
  • Why do we need programming in Industry?
  • Practical examples (without code part)
Milestone 2
Language basics but essentials
10 Assessment | Credit : 7

  • Introduction to language
  • Data types
  • Type Conversion and typecasting
  • Keywords
  • Variables
  • Identifiers
  • Operators
Milestone 3
Program Flow and Design
10 Assessment | Credit : 8

  • Flow Control Structures
  • Iterative Control Structures (Loops)
  • Functions
  • Classes & Objects
  • Inheritance
  • Polymorphism
  • Abstraction
  • Encapsulation
Milestone 4
Object Oriented Concepts
10 Assessment | Credit : 5

  • Classes & Objects
  • Inheritance
  • Polymorphism
  • Abstraction
  • Encapsulation
Milestone 5
Production Level Essentials
10 Assessment | Credit : 3

  • Exception Handling
  • File Handling
  • Code Debugging
Milestone 6
Numpy
10 Assessment | Credit : 2

  • Numpy Introduction
  • Numerical operations using Numpy
Milestone 7
Pandas
10 Assessment | Credit : 2

  • Pandas Introduction
  • Handling datasets and processing data
Milestone 8
Stats for Machine Learning
10 Assessment | Credit : 27

  • Probability and Statistics
  • Population and Sample
  • Gaussian Normal distribution and CDF
  • Symmetric distribution and skewness
  • Standard normal variate and standardization
  • Kernel density estimation
  • Sampling distribution and Central limit theorem
  • Q-Q plot
  • Various distributions and their use
  • Chebyshev’s inequality
  • Discrete and Uniform distribution
  • Bernoulli and Binomial distribution
  • Log-Normal distribution
  • Power law distribution
  • Box-CoxTransform
  • Application of non-Gaussian distributions
  • Co-Variance
  • Pearson Correlation Coefficient
  • Spearman rank correlation coefficient
  • Correlation Vs Causation
  • Use of correlations
  • Introduction of confidence Interval
  • Computation of confidence interval
  • Hypothesis Testing
  • Resampling and Permutation Test
  • K-S test for similarity of two distributions
  • Proportional Sampling
Milestone 9
Python Essentials for Machine learning
10 Assessment | Credit : 5

  • Basics of Data Sets
  • Introduction to dimensionality reduction
  • Row and Column Vector
  • Representation of Data Set
  • Representing Data Set as a matrix
Milestone 10
Data Analysis
10 Assessment | Credit : 12

  • Factors affecting classification algorithms
  • Balanced Vs Imbalanced Datasets
  • Impact of outliers
  • Space and Run time complexity
  • K distance
  • Multiclass classification
  • Time and space complexity of K-Nearest neighbor
  • Feature Importance
  • Handling categorical and numerical features
  • Handling missing values
  • Curse of dimensionality
  • Bias-Variance tradeoff
Milestone 11
Data Preprocessing
10 Assessment | Credit : 8

  • Data Preprocessing
  • Mean of data matrix
  • Column standardization
  • Covariance of data matrix
  • MNIST Data set
  • PCA (Principle Component Analysis) for dimensionality reduction
  • Limitations of PCA
  • t-SNE for dimensionality reduction
Milestone 12
Preprocessing continued (Text Focused)
10 Assessment | Credit : 7

  • Preprocessing DataSet
  • Data Cleaning
  • Convert text to vector
  • Bag of words
  • Stemming
  • tf-IDF
  • Word2Vec
Milestone 13
Python Visualizations
10 Assessment | Credit : 2

  • MatPlotLib Introduction
  • Plotting various types of graphs such as scatter plot, line plot, histogram, etc
  • I2C, on-chip EEPROM
  • Watchdog timer, etc. Case Study of MC
Milestone 14
Visualization Practicals
10 Assessment | Credit : 11

  • Introduction to Iris dataset
  • 2D scatter plots
  • 3D scatter plots
  • Pair Plots
  • Histograms and Probability density function (PDF)
  • Univariate Analysis using PDF
  • Mean, Median, Variance and standard deviation
  • Cumulative distribution function (CDF)
  • Percentiles and Quantiles
  • Box Plots with whiskers
  • Violin Plots
Milestone 15
Supervised Machine Learning Algorithms
10 Assessment | Credit : 13

  • Naive Bayes algorithm for classification
  • Logistic Regression
  • Linear Regression
  • Gradient descent algorithm
  • Support Vector Machine
  • Decision tree algorithm for classification
  • Ensembles
  • Random Forest
  • Gradient boosting
  • XGboot and AdaBoost
  • Classification & Regression in Machine learning
  • K-Nearest Neighbour
  • Time and space complexity of K-Nearest neighbor
Milestone 16
Unsupervised Machine Learning Algorithm
10 Assessment | Credit : 4

  • Clustering algorithms
  • K means algorithm
  • Agglomerative clustering
  • Density-based clustering (DBSCAN)
Milestone 17
Model Performance Metrics
10 Assessment | Credit : 7

  • Accuracy measure of classification algorithm
  • Accuracy
  • Confusion matrix
  • ROC and AUC curve
  • Log-Loss
  • R-squared coefficient of determination
  • Median absolute deviation (MAD
Milestone 18
Working with different types of datasets
10 Assessment | Credit : 8

  • Feature Engineering
  • Moving window for time series
  • Fourier decomposition
  • Image histogram
  • Relational data
  • Graph data
  • Feature binning
  • Feature slicing
Milestone 19
Basic of Deep Learning
10 Assessment | Credit : 8

  • Neural network and Deep Learning
  • History of neural network and comparison with biological neuron
  • Multilayer perceptron
  • Training a single layer model
  • Training MLP model
  • Back Propagation
  • Activation function
  • Vanishing gradient problem
Milestone 20
Components of Deep Learning
10 Assessment | Credit : 12

  • Deep layer perceptron
  • Drop Outs
  • Regularization
  • RELU
  • OptimizerHill-descent 2D
  • OptimerHill-descent 3D
  • SGD
  • Adam optimizer algorithm
  • Softmax for multiclass classification
  • Tensor Flow and Keras
  • GPU vs CPU
  • Google collaboratory
Milestone 21
Deep Learning Algorithms
10 Assessment | Credit : 11

  • Convolutional Networks
  • Understanding Visual cortex
  • Edge detection in images
  • Padding and strides
  • Convolutional layer
  • Max Pooling
  • ImageNet data sets
  • AlexNet
  • VGGNet
  • Mini Project: Cats Vs Dogs
  • Given an image of an animal identify whether it is an image of Dog OR Cat OR None
Milestone 22
Advanced Deep Learning Algorithms
10 Assessment | Credit : 6

  • Recurrent Neural Networks
  • Training RNN model by backpropagation
  • Types of RNN
  • LSTM
  • Deep RNN
  • Bidirectional RNN
Milestone 23
MLOps
10 Assessment | Credit : 3

  • APIs
  • Docker Containers
  • Hosting

Industry Projects and Case Studies

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Develop projects and applications

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Python Certificate

Course Completion Certificate

You will be awarded a Course Completion Certificate only if you pass with a minimum grade of 60% and a Certificate of Excellence if you secure 90% and above.

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Internships

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