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Meet the people who made it to the top companies
Ayan Sengupta
System Dev Engineer
DSML Nov21 Intermediate
Trianz
Courses like DSA and DSML with Scaler stood out to me because they'd provide you with every resource possible to enhance your learning. The only thing that you'd be required to dedicate all around the course would be commitment!
Years of experience at the time of joining Scaler
4
College
Siksha 'O' Anusandhan University
Degree
B.Tech
Scaler Graduation Year
2021
Tai Rakesh Kumar
Data Engineer
DSML Feb22 Advanced
TCS
Coming from a less privileged background, the course has done wonders for me. Would recommend the Scaler program, especially DSML to engineers wanting to enter and grow in the sector of AI & ML
Years of experience at the time of joining Scaler
2
College
Gayatri Vidya Parishad College Of Engineering
Degree
B.Tech
Scaler Graduation Year
2022
Arun M V
Applied scientist
DSML Nov21 Intermediate
Qualcomm
Choosing the scaler course was the best decision I have made for my career growth.Throughout my journey with scaler, it was more like a fun way to learn and develop skills. With every session, I used to be more and more curious. It never felt like a chore to attend the classes. Even after having a tiring day, I always looked forward to learning and enjoying the scaler sessions at night.
Years of experience at the time of joining Scaler
1
College
Sri Jayachamarajendra College Of Engineering Mysore
Degree
B.Tech
Scaler Graduation Year
2021
Abhishek singh
FullStack Engineer
DSML Nov21 Beginner
Sun Life
I took assistance from Scaler, and little did I know when I enrolled in the course that not only will I thoroughly enjoy my time there, but secure my dream placement as well :)
Years of experience at the time of joining Scaler
2
College
VIT Chennai
Degree
B.Tech
Scaler Graduation Year
2021
Harsh Patel
Data Scientist
DSML Mar22 Beginner
ABB
While I don't come from a tech-savvy city like Bangalore, with Scaler's help I could dream of making a great career in Data Science
Years of experience at the time of joining Scaler
2
College
School Of Engineering And Applied Sciences Ahmedabad University
Degree
B.Tech
Scaler Graduation Year
2022
1.

What kind of Data Science projects are included as part of the course?

Projects from top companies to make you a real Data Scientist or ML Engineer.

Get hands-on experience by working with real data sets, on projects built in partnerships with top companies

All Projects
Online Security
Decide which transactions should be blocked to keep users safe.
Network Optimization
Optimize network speed by minimizing junk traffic and spammy bots.
Improve Product Design
Make the checkout experience flawless to boost sales.
Improve User Experience
Make the games and app more engaging to boost daily usage
Predict ETA
Predict when would medicine arrive at customer's addresses.
Recommendation Engine
Show personalized recommendations to improve user experience.
2.

What if I get stuck or need guidance?

Get 1:1 Mentorship from Expert Data Scientists and ML Engineers!

Speak 1:1 with your mentor to get all your queries and doubts answered, help you define your career paths, conduct mock interviews, and give you detailed feedback.

Your Mentors

Sahil Chelaramani

Ex
read more

Hitesh Hinduja

Ex
read more

Aakash Agarwal

Ex
read more

Deepak Gupta

Ex
read more

Sanjeev Singh

Ex
read more

Naga Budigam

Ex
read more
3.

Will I get Placement Assistance?

Create real-world impact with your new skillset!

Companies wish to hire data scientists and ML engineers who are not just certified and skilled but also have a deep understanding of business. We at Scaler help you achieve the best skillset and help you get job opportunities from top companies

tech-stacks
Resume Making
tech-stacks
Help with Referrals
tech-stacks
Mock Interview
tech-stacks
Career Counselling
tech-stacks
4.

Which Data Science tools would I learn?

“Git” better at predicting & manipulating data with an array of tools!

Learn 45+ tools, including Git, TensorFlow, PySpark, PyTorch, Kafka

5

Can I acquire unique skills from this Data Science Course?

Become extraordinarily well-versed with DS & ML Research.

Learn to read relevant Research Papers in DS, ML, Deep Learning, and get proper guidance to Publish Research Paper in Global conferences

get-extra

Our top-notch Advisors hold us accountable.

person's image academy/svg/linkedin.svg
Ramit Sawhney
read more
person's image
Ramit Sawhney
Ramit Sawhney
  • Tower Research Capital / ShareChat
  • person's image academy/svg/linkedin.svg
    Pawan Kumar
    read more
    person's image
    Pawan Kumar
    Pawan Kumar
  • Head of Data Science, Uber
  • person's image academy/svg/linkedin.svg
    Bhavik Rasyara
    read more
    person's image
    Bhavik Rasyara
    Bhavik Rasyara
  • Boston Consulting Group
  • person's image academy/svg/linkedin.svg
    Yash Mimani
    read more
    person's image
    Yash Mimani
    Yash Mimani
  • McKinsey & Company
  • 7.

    Is your Data science course’s curriculum aligned with the industry?

    Up-to-date curriculum with the fast-evolving Data Science and ML field.
    Beginner
    15 Months
    Circle boundary 0822a30d92ac3930a0c28fe0e9e569145efa2e9e93349b6405a525554aaaca79.svg
    Checked circle 8d182dfbe72948215db9ddf7a22d36b0f5d91ccada0c6e3e86415486bd00e3dd.svg
    Intermediate
    11 Months
    Circle boundary 0822a30d92ac3930a0c28fe0e9e569145efa2e9e93349b6405a525554aaaca79.svg
    Checked circle 8d182dfbe72948215db9ddf7a22d36b0f5d91ccada0c6e3e86415486bd00e3dd.svg
    Advanced
    7 Months
    Circle boundary 0822a30d92ac3930a0c28fe0e9e569145efa2e9e93349b6405a525554aaaca79.svg
    Checked circle 8d182dfbe72948215db9ddf7a22d36b0f5d91ccada0c6e3e86415486bd00e3dd.svg
    Module - 1

    Beginner Module

    4 Months
    Module - 2

    Data Analysis and Visualization

    4 Months
    Module - 3

    Foundations of Machine Learning and Deep Learning

    3 Months
    Module - 4

    Specializations

    3 Months
    Module - 5

    Machine Learning Ops

    1 Month
    Module - 6

    Advanced Data Structures and Algorithms

    4 Months
    4 Months
    Tableau + Excel
    • Basic Visual Analytics
    • More Charts and Graphs, Operations on Data and Calculations in Tableau
    • Advanced Visual Analytics and Level Of Detail (LOD) Expressions
    • Geographic Visualizations, Advanced Charts, and Worksheet and Workbook Formatting
    • Introduction to Excel and Formulas
    • Pivot Tables, Charts and Statistical functions
    • Google Spreadsheets
    SQL
    • Intro to Databases & BigQuery Setup
    • Extracting data using SQL
    • Functions, Filtering and Subqueries
    • Joins
    • GROUP BY & Aggregation
    • Window Functions
    • Date and Time Functions & CTEs
    • Indexes and Partitioning
    Beginner Python
    • Flowcharts, Data Types, Operators
    • Conditional Statements & Loops
    • Functions
    • Strings
    • In-built Data Structures - List, Tuple, Dictionary, Set, Matrix Algebra, Number Systems
    4 Months
    Placement assistance for Data Analyst/Product Analyst roles via Mastery based evaluation starts after completion of this module
    Python libraries
    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API
    • Web Scraping
    • Beautifulsoup
    • Tweepy
    Probability and Applied Statistics
    • Probability
    • Bayes Theorem
    • Distributions
    • Descriptive Statistics, outlier treatment
    • Confidence Interval
    • Central limit theorem
    • Hypothesis test, AB testing
    • ANOVA
    • Correlation
    • EDA, Feature Engineering, Missing value treatment
    • Experiment Design
    • Regex, NLTK, OpenCV
    Product Analytics
    • Framework to address product sense questions
    • Diagnostics
    • Metrics, KPI
    • Product Design & Development
    • Guesstimates
    • Product Cases from Netflix, Stripe, Instagram
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    Download Curriculum
    Module - 1

    Data Analysis and Visualization

    4 Months
    Module - 2

    Foundations of Machine Learning and Deep Learning

    3 Months
    Module - 3

    Specializations

    3 Months
    Module - 4

    Machine Learning Ops

    1 Month
    Module - 5

    Advanced Data Structures and Algorithms

    4 Months
    4 Months
    Placement assistance for Data Analyst/Product Analyst roles via Mastery based evaluation starts after completion of this module
    Python libraries
    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API
    • Web Scraping
    • Beautifulsoup
    • Tweepy
    Probability and Applied Statistics
    • Probability
    • Bayes Theorem
    • Distributions
    • Descriptive Statistics, outlier treatment
    • Confidence Interval
    • Central limit theorem
    • Hypothesis test, AB testing
    • ANOVA
    • Correlation
    • EDA, Feature Engineering, Missing value treatment
    • Experiment Design
    • Regex, NLTK, OpenCV
    Product Analytics
    • Framework to address product sense questions
    • Diagnostics
    • Metrics, KPI
    • Product Design & Development
    • Guesstimates
    • Product Cases from Netflix, Stripe, Instagram
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    Download Curriculum
    Module - 1

    Foundations of Machine Learning and Deep Learning

    3 Months
    Module - 2

    Specializations

    3 Months
    Module - 3

    Machine Learning Ops

    1 Month
    Module - 4

    Advanced Data Structures and Algorithms

    4 Months
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    Download Curriculum
    4 Months
    Tableau + Excel
    • Basic Visual Analytics
    • More Charts and Graphs, Operations on Data and Calculations in Tableau
    • Advanced Visual Analytics and Level Of Detail (LOD) Expressions
    • Geographic Visualizations, Advanced Charts, and Worksheet and Workbook Formatting
    • Introduction to Excel and Formulas
    • Pivot Tables, Charts and Statistical functions
    • Google Spreadsheets
    SQL
    • Intro to Databases & BigQuery Setup
    • Extracting data using SQL
    • Functions, Filtering and Subqueries
    • Joins
    • GROUP BY & Aggregation
    • Window Functions
    • Date and Time Functions & CTEs
    • Indexes and Partitioning
    Beginner Python
    • Flowcharts, Data Types, Operators
    • Conditional Statements & Loops
    • Functions
    • Strings
    • In-built Data Structures - List, Tuple, Dictionary, Set, Matrix Algebra, Number Systems
    4 Months
    Placement assistance for Data Analyst/Product Analyst roles via Mastery based evaluation starts after completion of this module
    Python libraries
    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API
    • Web Scraping
    • Beautifulsoup
    • Tweepy
    Probability and Applied Statistics
    • Probability
    • Bayes Theorem
    • Distributions
    • Descriptive Statistics, outlier treatment
    • Confidence Interval
    • Central limit theorem
    • Hypothesis test, AB testing
    • ANOVA
    • Correlation
    • EDA, Feature Engineering, Missing value treatment
    • Experiment Design
    • Regex, NLTK, OpenCV
    Product Analytics
    • Framework to address product sense questions
    • Diagnostics
    • Metrics, KPI
    • Product Design & Development
    • Guesstimates
    • Product Cases from Netflix, Stripe, Instagram
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    4 Months
    Placement assistance for Data Analyst/Product Analyst roles via Mastery based evaluation starts after completion of this module
    Python libraries
    • Numpy, Pandas
    • Matplotlib
    • Seaborn
    • Data Acquisition
    • Web API
    • Web Scraping
    • Beautifulsoup
    • Tweepy
    Probability and Applied Statistics
    • Probability
    • Bayes Theorem
    • Distributions
    • Descriptive Statistics, outlier treatment
    • Confidence Interval
    • Central limit theorem
    • Hypothesis test, AB testing
    • ANOVA
    • Correlation
    • EDA, Feature Engineering, Missing value treatment
    • Experiment Design
    • Regex, NLTK, OpenCV
    Product Analytics
    • Framework to address product sense questions
    • Diagnostics
    • Metrics, KPI
    • Product Design & Development
    • Guesstimates
    • Product Cases from Netflix, Stripe, Instagram
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    3 Months
    You can move to the advanced track only after you clear the transition test
    Advanced Python
    • Python Refresher
    • Basics of Time and Space Complexity
    • OOPS
    • Functional Programming
    • Exception Handling and Modules
    Math for Machine Learning
    • Classification
    • Hyperplane
    • Halfspaces
    • Calculus
    • Optimization
    • Gradient descent
    • Principal Component Analysis
    Introduction to Neural Networks and Machine Learning
    • Introduction to Classical Machine Learning
    • Linear Regression
    • Polynomial, Bias-Variance, Regularisation
    • Cross Validation
    • Logistic Regression-2
    • Perceptron and Softmax Classification
    • Introduction to Clustering, k-Means
    • K-means ++, Hierarchical
    3 Months each
    You can pursue the Deep Learning specialisation after completing the Machine Learning specialisation or vice versa
    Machine Learning
    Machine Learning 1: Supervised
    • MLE, MAP, Confidence Interval
    • Classification Metrics
    • Imbalanced Data
    • Decision Trees
    • Bagging
    • Naive Bayes
    • SVM
    Machine Learning 2: Unsupervised and Recommender systems
    • Intro to Clustering, k-Means
    • K-means ++, Hierarchical
    • GMM
    • Anomaly/Outlier/Novelty Detection
    • PCA, t-SNE
    • Recommender Systems
    • Time Series Analysis
    And/Or
    Deep Learning
    Neural Networks
    • Perceptrons
    • Neural Networks
    • Hidden Layers
    • Tensorflow
    • Keras
    • Forward and Back Propagation
    • Multilayer Perceptrons (MLP)
    • Callbacks
    • Tensorboard
    • Optimization
    • Hyperparameter tuning
    Computer vision
    • Convolutional Neural Nets
    • Data Augmentation
    • Transfer Learning
    • CNN
    • CNN hyperparameters tuning & BackPropagation
    • CNN Visualization
    • Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
    • Object Segmentation, Localisation, and Detection
    • Generative Models, GANs
    • Attention Models
    • Siamese Networks
    • Advanced CV
    Natural Language Processing
    • Text Processing and Representation
    • Tokenization, Stemming, Lemmatization
    • Vector space modelling, Cosine Similarity, Euclidean Distance
    • POS tagging, Dependency parsing
    • Topic Modeling, Language Modeling
    • Embeddings
    • Recurrent Neural Nets
    • Information Extraction
    • LSTM
    • Attention
    • Named Entity Recognition
    • Transformers
    • HuggingFace
    • BERT
    1 Month
    After completion of this module Placement assistance for Data Scientist (ML/DS) roles via Mastery based evaluation will start
    Machine Learning Ops
    • Streamlit
    • Flask
    • Containerisation, Docker
    • Experiment Tracking
    • MLFlow
    • CI/CD
    • GitHub Actions
    • ML System Design
    • AWS Sagemaker, AWS Data Wrangler, AWS Pipeline
    • Apache Spark
    • Spark MLlib
    4 Months
    The recorded lectures of Advanced Programming will be shared along with Teaching Assistant support (no live sessions)
    Advanced Data Structures and Algorithms
    • Linked Lists
    • Stacks & Queues
    • Trees
    • Tries & Heaps
    Download Curriculum
    8.

    Why do I need to learn DSA and Big Data?

    To help you do effective problem solving & make smarter business decisions

    Moreover, DSA and Big Data are essential skills in your toolkit to shine in your roles as a Data Scientist or ML Engineer.

    why-dsa
    9.

    Can I try a demo class?

    “Knowing us before growing with us” is our motto.

    Attend a free class and get a feel of how your life with Scaler look like, understand our teaching patterns

    10.

    Who will teach me all this?

    Only the best! Instructors so amazing, you’d think they have superpowers

    Our amazing instructors take live classes and resolve all your doubts on the go. We have the best pack from the industry

    Your Mentors

    Srikanth Varma

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    Ajay Shenoy

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    Harshit Tyagi

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    Anant Mittal

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    Mohit Uniyal

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    Mudit Goel

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    Prashant K Tiwari

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    Sameer Shah

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    Nitish Jaipuria

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    Shan Mehrotra

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    Sundaravaradhan

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    Amit Singh

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    Mohit Kukkarl

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    Rahul Aggarwal

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    Suraaj Hasija

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    Suransh Chopra

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    Thanish Batcha

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    Vishwath parthasarathy

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    affordable-course
    11.

    Great, but can I afford Scaler Data Science Course?

    Consider it a short-term investment for your long-term career growth!

    Invest in your career and future, enroll with super affordable EMI options starting at Rs 8,628/- Try the course for the first 2 weeks - full money-back guarantee if you choose to withdraw.

    EMI Options
    You can find both no-cost EMI & standard interest EMI from our NBFC partners. See below a summary of their best plans (more details available at the time of payment)
    Total Amount
    Upfront Downpayment
    Amount split over EMI
    Duration (Months)
    Monthly Payments
    No Cost Emi
    ₹369,000
    ₹35,000
    ₹334,000
    6
    9
    12
    18
    24
    ₹55,667
    ₹37,111
    ₹27,833
    ₹18,556
    ₹13,917
    Standard Emi
    ₹369,000
    ₹35,000
    ₹334,000
    36
    60
    ₹12,339
    ₹8,628
    Delivered via our EMI partners - Liquiloans, Eduvanz, EarlySalary, Avanse & Credit Fair
    You can also choose to avail EMI options from your credit card providers.
    12.

    Can I connect with other top Data Scientists & ML Engineers?

    Network with alumni and peers from top companies

    Access job opportunities from 600+ partner employers and exchange job opportunities with 20k+ strong student community that will make you say Scaler Forever!

    why-dsa
    13.

    Do you have any proof that your course works?

    Our Proven Track Record shows that we walk the talk
    Sumit Kumar

    Sumit Kumar

    A big shout out to my mentor Chandra Bhan Giri. I will always be grateful to you for your support and guidance. It would be impossible to count all the ways that you’ve helped me in my career.
    Dolly Vaishnav

    Dolly Vaishnav

    …The biggest shoutout to my mentor Krunal Parmar for constantly pushing & guiding me throughout the journey. He is the best mentor I could ever get…
    phone
    Ready to become a data science and machine learning expert? Book a live class with Srikanth Varma and start your journney!

    Scaler Data Science Training FAQ’s

    Coding background is not required to enroll in this Data Science course. You can start from the Beginner module in which we will cover the basics of coding.

    In fact, prior knowledge in Data Science or ML is also not needed. We will cover all the relevant topics from scratch.

    The only prerequisite is that you should have a basic understanding of 9th and 10th-grade school maths - just the basics, nothing advanced. Still, we will cover these topics in class, but some prior knowledge would be helpful.
    All the Maths required for understanding and implementing algorithms will be covered in this Data Science training (Probability, Statistics, Linear Algebra, Calculus, Coordinate Geometry).
    For learners who show interest in publishing in the data science domain, we would be happy to provide mentorship and support.
    While designing the Scaler Data Science course, we did not put any limit on the duration. We included each and every concept that is important for making you a strong Data Scientist and ML Engineer. The course turned out to be 15 months long with more hands-on experience.

    Notice that the course is quite rigorous; each week you will have 3 Live lectures of 2.5 hours each, homework assignments, business case project, and discussion sessions. This allows us to cover the entire depth and breadth of Data Science & Machine Learning, as much as is required for you to succeed in the role.
    25 data science projects will be assigned in homework and others will be covered in class.

    In each case, we will focus on a particular portion that is relevant to the topic being covered that week.

    Each case study is discussed in class, at the end of the week (Business Case Discussion Session)

    Submissions from all students are posted on an internal Discussion Forum where you can read how other students have solved that particular problem.

    Students are allowed to drop 25% of the total case studies, depending on their interest area. So they can focus the remaining time on other cases of their interest.

    As students practice working under deadlines, their speed and productivity will hopefully start improving.
    Scaler community has people working worldwide. The bottleneck is in getting a visa sponsorship. Many companies based in India offer opportunities for their high-performing employees to work on international data science projects and relocate. Some international companies also hire directly in India and ask to relocate for jobs. However, with the surge in WFH, this trend may be ebbing. However, you can continue applying for remote data science jobs based outside India via LinkedIn.
    Masters and PhD are typically asked for Research focused data science roles. Most companies do not require a Masters' degree for a Data Science role.

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