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Why Scaler Data Science & Machine Learning Program?

Scaler’s Data Science course is a program curated to help you kick-start your career in Data Science & Machine Learning. We’ll make you industry-ready through a rigorous curriculum taught by industry veterans who’ll mentor you as you headway toward growth.
Monthly 1:1 mentorship by industry experts to provide personalized guidance and support.
Learn from industry-leading experts who have built FB messenger, Uber, etc.
50+ Hands-on projects and real-world case studies enrich your learning experience.
Get Expert career guidance to help you navigate your path in data science.
Master essential tools and languages used in data science and machine learning.
Join a thriving community of learners and alumni for networking and support.
1:1 Mentorship
Monthly 1:1 mentorship by industry experts to provide personalized guidance and support.
Top Instructors
Learn from industry-leading experts who have built FB messenger, Uber, etc.
Projects and Case Studies
50+ Hands-on projects and real-world case studies enrich your learning experience.
Career Counselling
Get Expert career guidance to help you navigate your path in data science.
Tools and Languages
Master essential tools and languages used in data science and machine learning.
Learners & Alumni Network
Join a thriving community of learners and alumni for networking and support.
1.

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

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

Gain practical experience through real data sets and projects developed in collaboration with leading 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 data science related queries and doubts answered, help you define your career paths, conduct mock interviews, and give you detailed feedback.

Your Mentors

Sahil Chelaramani

Ex
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Hitesh Hinduja

Ex
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Aakash Agarwal

Ex
read more

Deepak Gupta

Ex
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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+ Data Science tools, including Git, TensorFlow, PySpark, PyTorch, and Kafka.

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
5.

Can I acquire unique skills from this Data Science Course?

Become extraordinarily well-versed in DS & ML Research.

Learn to read relevant Research Papers in Data Science, Machine Learning, and Deep Learning, and get proper guidance to Publish Research Paper at Global conferences.

get-extra

Our top-notch Advisors hold us accountable.

Our top-notch Advisors ensure our commitment to excellence. With their guidance, we maintain the highest standards in our Data Science course, ensuring your success.
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
  • 6.

    Is Scaler’s 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
    Checked circle
    Intermediate
    11 Months
    Circle boundary
    Checked circle
    Advanced
    7 Months
    Circle boundary
    Checked circle
    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
    ×

    Industry Recognized Certification.

    certificate
    7.

    Will I receive a Data Science Certification upon completing this course?

    Level up your career with Scaler’s Industry-Recognized Certification.
    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 are so amazing, you’d think they have superpowers

    Our amazing Data Science 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 what about the Scaler Data Science Course fee? Is it affordable?

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

    why-dsa
    13.

    Do you have any proof or reviews 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

    Program

    This Data Science course is designed for everyone, even if you have no coding experience. We offer a Beginner module that covers the basics of coding to get you started.
    Scaler's Data Science and Machine Learning program is considered one of the best data science courses because-
    • Covers all essential data science topics, ensuring a holistic learning experience.
    • Emphasis on hands-on projects equips students with real-world skills, setting them up for success in the field.
    • Industry experts as instructors provide invaluable insights and knowledge.
    • Scaler's industry connections and placement assistance enhance job prospects.
    • The program caters to diverse backgrounds, offering flexibility in learning for all.
    Yes, you have the flexibility to attend Scaler’s Data Science online course on a part-time basis. In case you miss a live class, you can always access the recorded sessions. You can also take a break of up to 3 months, all this within the course duration.
    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.
    Live classes are held 3 times a week, on alternate days, primarily in the late evening or night on weekdays to accommodate working software engineers. Weekend timings are flexible.
    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.
    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.
    The total Data Science course fees is ₹369,000. With EMI, this can drop as low as ~INR 8,628/month (equivalent to your monthly grocery bill!)
    Absolutely! Scaler offers a top-notch data science course designed to equip you with the skills and knowledge needed to excel in this field. Our program emphasizes hands-on learning with real-world projects and 1:1 mentorship from industry experts. We believe in providing practical experience that translates directly to the workplace. With our comprehensive curriculum and career support services, Scaler is an excellent choice for anyone looking to kickstart or advance their career in data science.

    Why Choose Scaler for Data Science?

    - Get to work on 50+ hands-on projects and real-world case studies to enrich your learning experience.
    - Get 1:1 Mentorship from Expert Data Scientists and ML Engineers!
    - Up-to-date curriculum with the fast-evolving Data Science and ML field.
    - Master essential tools and languages used in data science and machine learning.
    - Get Expert career guidance to help you navigate your path in data science.

    Eligibility

    Yes, there is an eligibility test called the Scaler entrance test for enrolling in Scaler's Data Science program.
    In Scaler's Data Science certification course, you'll acquire a wide range of skills, including:
    • Beginner skills in Tableau, Excel, SQL, and Python.
    • Data analysis and visualization using Python libraries, probability, and statistics.
    • Foundations of machine learning, deep learning, and neural networks.
    • Specializations in either machine learning or deep learning.
    • Advanced knowledge in machine learning operations, data structures, and algorithms to excel in the field.
    Scaler’s Data Science and Machine learning program is open to both freshers and working professionals. who are comfortable and confident with 10 standard aptitudes and mathematics.
    A coding background is not required to enroll in this Data Science training. 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.

    Data Science

    Data science is a field of computer science that uses various algorithms, methods, and machine learning to uncover hidden and meaningful insights in both structured and unstructured data.
    Data science can be challenging, as it requires a solid understanding of mathematics, statistics, and programming. However, with dedication and the right resources, it's accessible to those willing to learn.
    A data scientist is an expert in data science who specializes in collecting and analyzing large amounts of data from diverse sources. They use their skills in mathematics, statistics, and computer science to help organizations make informed decisions based on data analysis.
    To become a Data Scientist, follow these steps:
    • Learn the fundamentals of programming and statistics.
    • Acquire knowledge in machine learning and data analysis.
    • Build a strong portfolio of projects.
    • Pursue relevant courses.
    • Apply for Data Scientist positions.
    A Data Scientist designs new data approaches, while a Data Analyst interprets existing data. Data Scientists create innovative ways to collect and analyze data, while Data Analysts extract insights from available data.

    Job and Career

    Yes, Data Science is an excellent career choice in 2023. The field is growing rapidly, with high demand for professionals due to its continued relevance and the increasing importance of data-driven decisions.
    After completing the data science course, you can explore various job roles, including:
    • Business Analyst
    • Data Analyst
    • Data Scientist
    • Big Data Engineer
    • Data Engineer
    • Machine Learning Engineer
    • Data Architect, and many more.
    Top companies like Amazon, Google, IBM, Oracle, Deloitte, Facebook, Microsoft, Wipro, Accenture, Visa, Bank of America, and Fractal Analytics are actively hiring data scientists.
    Absolutely! Scaler goes the extra mile to ensure your success. We provide resume building, referrals, mock interviews, and career counseling to help you land your dream job with top companies.
    The average salary for a data scientist in India is ₹9.4 Lakhs per year, with a salary range of ₹3.7 Lakhs to ₹25.1 Lakhs.

    Certification

    To earn Scaler's Data Science certification, you need to successfully complete all the required course modules, assignments, and projects. You'll be assessed based on your performance throughout the program.
    Scaler's Data Science certification is a lifetime certification, meaning it doesn't expire. Once you earn it, you can proudly showcase your expertise in data science throughout your career.
    No, you won't receive a Data Science course completion certificate without a certain grade or score, but you can obtain an enrollment document.
    Scaler's Data Science certification is highly regarded in the industry. It's recognized for its comprehensive curriculum and hands-on approach, making you job-ready.

    Lectures

    If you miss a lecture, you can still watch it offline, and it won't affect your attendance.
    Yes, you can access course materials and lectures for up to 6 months after completing the course.
    If you find it challenging to balance your job or schedule with class timings, you can catch up by watching the recorded lectures as classes are held three times a week on alternate days.
    Scaler’s data science program is instructor-led, ensuring you have guidance and support throughout your learning journey.
    All the Maths required for understanding and implementing algorithms will be covered in this Data Science training (Probability, Statistics, Linear Algebra, Calculus, Coordinate Geometry).

    Community

    Scaler offers multiple support channels for students, including community Slack and WhatsApp groups for collaboration, dedicated problem-solving support on the dashboard, and Scaler support through email, chat, and phone for any concerns or queries.
    Yes, there is a Scaler community where students can interact and collaborate with each other.
    The 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.

    Opportunities

    For learners who show interest in publishing in the data science domain, we would be happy to provide mentorship and support.
    Masters and Ph.D.s are typically asked for Research-focused data science roles. Most companies do not require a Master's degree for a Data Science role.

    😎 Look who is famous!

    Scaler Data Science and Machine Learning is the talk of the town!
    We have surveyed about 100 Data Scientist to get to know what’s best, don’t wait book a class
    Program Registration
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