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

September 7th,2023

Start Date

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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

7th September

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

Key Features

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Essential Data Science Skills

Essential Data Science Skilld

Get ready to have a GAMMA EFFECT

Enroll Now at just Rs. 2,49,999/-

Our Other Plans

Alpha Plan

Rs.4,998/-
  • Learning Video
  • Live Interactive Session
  • Live Project
  • Case Studies
  • Placement Assistance
  • Internship Opportunity
  • Learning Module: 30Hrs

Beta Plan

Rs.24,999/-
  • Everything mentioned in Alpha Plan
  • +
  • Call assistance from experts before interview
  • Personalized Mentoring
  • Industry tools training
  • Industry Specific training
  • Learning Module: 80Hrs

Course Elements

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Tools in Data Science

Anaconda

Anaconda

Python

Python

Pandas

Pandas

Numpy

Numpy

Matplotlib

Matplotlib

Power BI

Power BI

AWS

AWS

SQL

SQL

Excel

Excel

Tableau

Tableau

100+ Guided Projects

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IPL Complete Dataset

About Dataset

Now that this year's IPL is over, let's not curb our cricket love and start analyzing the whole of IPL with this latest and complete Indian Premier League dataset. It contains the match descriptions, results, winners, player of the matches, ball by ball dataset and much more. So, stop thinking and start analyzing .

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Data Analytics and Visualisation of UFC

About Dataset

Data and visual analytics is an emerging field concerned with analyzing, modeling, and visualizing complex high dimensional data. This course will introduce students to the field by covering state-of-the-art modeling, analysis and visualization techniques.

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Population and literacy stats in India

About Dataset

And this weightage is achieved after a very long period of reforms and policies. In the year 1947 when India got its independence, the literacy rate was dismal. Only 18% of the overall population was literate.

Our Clients Testimonials

Amol Ghatole

Amol Ghatole

Data Scientist, Unique Education

I was in a completely different field before taking a data science course, but the course helped me make a successful transition.

Ajit Thorat

Ajit Thorat

Director, Community for Sustainable Development

I took a data science course to improve my skills and advance my career, and it exceeded my expectations.

Vaibhav Pawar

Vaibhav Pawar

Director, COMPASS, a research and consulting firm

The data science course not only provided me with the skills and knowledge to excel in my field.

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