Advanced Certificate
Course in Data Science

Kick-start your journey in Data Science & Machine Learning with India's best selling advanced certification Programme from TalentServe through Hands on Industry Projects, Cutting Edge Curriculum, Industry Mentorship.

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Data Science Course Image

1 Lac

Learners

7 - 9 Months

Recommended 8-10 hrs/week

June 15,2023

Start Date

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Data Scientists in 2022

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Advanced Certification Course in Data Science

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Get Placed with our 100% Job Guarantee Program

Data Science refers to the process of extracting valuable information from various structured or unstructured data, e.g., data mining.

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Book Our Free Demo Classes

15 LPA

Average CTC

312

User Enrolled

15th June

Batch Starts

7 Months

Program Duration

Online

Mode

Learning Path

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Modules of Data Science Certification

Introduction to Python

  • Features, the advantages of Python over other programming languages · Python installation – Windows, Mac & Linux distribution for Anaconda Python

  • Deploying Python IDE

  • Basic Python commands, data types, variables, keywords and More

Basic Steps

  • Built-in data types in Python

  • Learn classes, modules, Str(String),

  • Basic operators, comparison, arithmetic, slicing and slice operator, logical etc

  • Loop and control statements while, for, if, break, else, continue

OOPs

  • OOP concepts in Python · How to write OOP concepts program in Python Connecting to a database · Classes and objects in Python · OOPs paradigm, important concepts in OOP like polymorphism, inheritance, encapsulation · Python functions, return types and parameters · Lambda expressions

Numpy

    NumPy for mathematical computing
  • Introduction to arrays and matrices

  • Broadcasting of array math, indexing of array

  • Standard deviation, conditional probability, correlation and covariance.

Data Visualization

    Matplotlib for data visualization
  • How to plot graph and chart with Python

  • Various aspects of line, scatter, bar, histogram,etc

  • Seaborn

Pandas

  • Pandas for data analysis and machine learning

  • Introduction to Python dataframes

  • Importing data

  • Various data operations like selecting, filtering, sorting, viewing, joining, combining

Exceptions and Errors

    Exception Handling
  • Introduction to Exception Handling

  • Scenarios in Exception Handling with its execution

  • Arithmetic exception

  • RAISE of Exception

Introduction to Artificial Intelligence and Machine Learning

    By the end of this lesson, you will be able to:
  • Define Artificial Intelligence (AI) and understand its relationship with data

  • Understand machine learning approach

  • Identify the applications of machine learning

  • Identify the applications of machine learning

Data Wrangling and Manipulation

    By the end of this lesson, you will be able to:
  • Demonstrate data import and exploration using Python

  • Demonstrate different data wrangling techniques and their significance

  • Perform data manipulation in python

Supervised Learning

    By the end of this lesson, you will be able to:
  • Understand the different types of supervised learning

  • Build various regression models

Supervised Learning-Classification

    By the end of this lesson, you will be able to:
  • Understand classification as part of supervised learning

  • Demonstrate different classification techniques in Python

  • Evaluate classification models

Unsupervised learning

    By the end of this lesson, you will be able to:
  • Explain the mechanism of unsupervised learning

  • Practice different clustering techniques in Python

Machine Learning Pipeline Building

  • Machine learning automation using ML Pipelines

Decision Tree Analysis and Ensemble Learning

    By the end of this lesson, you will be able to:
  • Decision Tree algorithms

  • Explain ensemble learning

  • Models in ensemble learning

  • Evaluate the performance of bagging and boosting models

AI and Deep learning introduction

  • What is AI and Deep learning
  • Brief History of AI
  • Recap: SL, UL and RL
  • Deep learning : successes last decade
  • Demo & discussion: Self driving car object detection
  • Applications of Deep learning
  • Challenges of Deep learning
  • Fullcycle of a deep learning project
  • Key Takeaways
  • Knowledge Check

Artificial Neural Network

  • Biological Neuron Vs Perceptron
  • Shallow neural network
  • Training a Perceptron
  • Backpropagation
  • ole of Activation functions & backpropagation
  • Demo code: Backpropagation (Assisted)
  • Demo code: Activation Function (Unassisted)
  • Optimization
  • Regularization
  • Dropout layer
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project (MNIST Image Classification)

Deep Neural Network & Tools

  • Deep Neural Network : why and applications
  • Designing a Deep neural network
  • How to choose your loss function?
  • Tools for Deep learning models
  • Keras and its Elements
  • Tensorflow and Its ecosystem

Deep Neural Net optimization, tuning, interpretability

  • Optimization algorithms
  • SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
  • Batch normalization
  • Demo Code: Batch Normalization (Assisted)
  • Exploding and vanishing gradients
  • Hyperparameter tuning
  • Interpretability
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Hyperparameter Tunning With Keras Tuner

Convolutional Neural Net

  • Success and history
  • CNN Network design and architecture
  • Demo code: CNN Image Classification (Assisted)
  • Deep convolutional models
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Image Classification

Recurrent Neural Networks

  • Sequence data
  • Sense of time
  • RNN introduction
  • LSTM
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Stock Price Forecasting and Sentiment Analysis using LSTM

Overfit and underfit

  • Explore several common regularization techniques, and use them to improve on a classification model.

Transfer Learning

  • Deriving representations from a previous network to extract meaningful features from new samples

Working with Generative Adversarial Networks

  • Introduction to Generative Adversarial Networks

  • Generative vs. Discriminative Algorithms

  • Architectural Overview

  • Basic building block – generator

  • Basic building block – discriminator

  • Types of GANs

  • Introduction to Deep Convolutional GANs (DCGAN)

Pytorch

  • Image classification in ANN and CNN

Modules of Data Science Certification

1. Module

Introduction to Python

  • Features, the advantages of Python over other programming languages · Python installation – Windows, Mac & Linux distribution for Anaconda Python

  • Deploying Python IDE

  • Basic Python commands, data types, variables, keywords and More

2. Module

Basic Steps

  • Built-in data types in Python

  • Learn classes, modules, Str(String),

  • Basic operators, comparison, arithmetic, slicing and slice operator, logical etc

  • Loop and control statements while, for, if, break, else, continue

3. Module

OOPs

  • OOP concepts in Python · How to write OOP concepts program in Python Connecting to a database · Classes and objects in Python · OOPs paradigm, important concepts in OOP like polymorphism, inheritance, encapsulation · Python functions, return types and parameters · Lambda expressions

4. Module

Numpy

    NumPy for mathematical computing
  • Introduction to arrays and matrices

  • Broadcasting of array math, indexing of array

  • Standard deviation, conditional probability, correlation and covariance.

5. Module

Data Visualization

    Matplotlib for data visualization
  • How to plot graph and chart with Python

  • Various aspects of line, scatter, bar, histogram,etc

  • Seaborn

6. Module

Pandas

  • Pandas for data analysis and machine learning

  • Introduction to Python dataframes

  • Importing data

  • Various data operations like selecting, filtering, sorting, viewing, joining, combining

7. Module

Exceptions and Errors

    Exception Handling
  • Introduction to Exception Handling

  • Scenarios in Exception Handling with its execution

  • Arithmetic exception

  • RAISE of Exception

8. Module

Introduction to Artificial Intelligence and Machine Learning

    By the end of this lesson, you will be able to:
  • Define Artificial Intelligence (AI) and understand its relationship with data

  • Understand machine learning approach

  • Identify the applications of machine learning

  • Identify the applications of machine learning

9. Module

Data Wrangling and Manipulation

    By the end of this lesson, you will be able to:
  • Demonstrate data import and exploration using Python

  • Demonstrate different data wrangling techniques and their significance

  • Perform data manipulation in python

10. Module

Supervised Learning

    By the end of this lesson, you will be able to:
  • Understand the different types of supervised learning

  • Build various regression models

11. Module

Supervised Learning-Classification

    By the end of this lesson, you will be able to:
  • Understand classification as part of supervised learning

  • Demonstrate different classification techniques in Python

  • Evaluate classification models

12. Module

Unsupervised learning

    By the end of this lesson, you will be able to:
  • Explain the mechanism of unsupervised learning

  • Practice different clustering techniques in Python

13. Module

Machine Learning Pipeline Building

  • Machine learning automation using ML Pipelines

14. Module

Decision Tree Analysis and Ensemble Learning

    By the end of this lesson, you will be able to:
  • Decision Tree algorithms

  • Explain ensemble learning

  • Models in ensemble learning

  • Evaluate the performance of bagging and boosting models

15. Module

AI and Deep learning introduction

  • What is AI and Deep learning
  • Brief History of AI
  • Recap: SL, UL and RL
  • Deep learning : successes last decade
  • Demo & discussion: Self driving car object detection
  • Applications of Deep learning
  • Challenges of Deep learning
  • Fullcycle of a deep learning project
  • Key Takeaways
  • Knowledge Check

16. Module

Artificial Neural Network

  • Biological Neuron Vs Perceptron
  • Shallow neural network
  • Training a Perceptron
  • Backpropagation
  • ole of Activation functions & backpropagation
  • Demo code: Backpropagation (Assisted)
  • Demo code: Activation Function (Unassisted)
  • Optimization
  • Regularization
  • Dropout layer
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project (MNIST Image Classification)

17. Module

Deep Neural Network & Tools

  • Deep Neural Network : why and applications
  • Designing a Deep neural network
  • How to choose your loss function?
  • Tools for Deep learning models
  • Keras and its Elements
  • Tensorflow and Its ecosystem

18. Module

Deep Neural Net optimization, tuning, interpretability

  • Optimization algorithms
  • SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
  • Batch normalization
  • Demo Code: Batch Normalization (Assisted)
  • Exploding and vanishing gradients
  • Hyperparameter tuning
  • Interpretability
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Hyperparameter Tunning With Keras Tuner

19. Module

Convolutional Neural Net

  • Success and history
  • CNN Network design and architecture
  • Demo code: CNN Image Classification (Assisted)
  • Deep convolutional models
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Image Classification

20. Module

Recurrent Neural Networks

  • Sequence data
  • Sense of time
  • RNN introduction
  • LSTM
  • Key Takeaways
  • Knowledge Check
  • Lesson-end Project: Stock Price Forecasting and Sentiment Analysis using LSTM

21. Module

Overfit and underfit

  • Explore several common regularization techniques, and use them to improve on a classification model.

22. Module

Transfer Learning

  • Deriving representations from a previous network to extract meaningful features from new samples

23. Module

Working with Generative Adversarial Networks

  • Introduction to Generative Adversarial Networks

  • Generative vs. Discriminative Algorithms

  • Architectural Overview

  • Basic building block – generator

  • Basic building block – discriminator

  • Types of GANs

  • Introduction to Deep Convolutional GANs (DCGAN)

24. Module

Pytorch

  • Image classification in ANN and CNN

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Tools in Data Science

Anaconda

Anaconda

Python

Python

Pandas

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Numpy

Numpy

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Matplotlib

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