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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
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+ 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.

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
Intermediate
11 Months
Advanced
7 Months
Module - 1

Beginner Module

5 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
5 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
Python
  • Flowcharts, Data Types, Operators
  • Conditional Statements & Loops
  • Functions
  • Strings
  • In-built Data Structures - List, Tuple, Dictionary, Set, Matrix Algebra, Number Systems
  • Python Refresher
  • Basics of Time and Space Complexity
  • OOPS
  • Functional Programming
  • Exception Handling and Modules
4 Months
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
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
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
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
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
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
5 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
Python
  • Flowcharts, Data Types, Operators
  • Conditional Statements & Loops
  • Functions
  • Strings
  • In-built Data Structures - List, Tuple, Dictionary, Set, Matrix Algebra, Number Systems
  • Python Refresher
  • Basics of Time and Space Complexity
  • OOPS
  • Functional Programming
  • Exception Handling and Modules
4 Months
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
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
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
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
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
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
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Industry Recognized Certification.

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6.

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

Level up your career with Scaler’s Industry-Recognized Certification.
7.

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

8.

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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9.

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.
10.

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
11.

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…
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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.
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 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 2024. 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.
At Scaler, we are committed to supporting our students in their career journeys through extensive placement support and our network of 900+ partner companies. While we do not provide job guarantees, we offer valuable resources and training to improve job prospects.
Our students benefit from personalized career guidance, regular mentorship, interview preparation assistance, resume building support, and mock interviews conducted by industry experts. The active Scaler community, with over 40,000 members, provides networking opportunities and continuous support.
Notably, our DSML alumni have secured a median salary hike of 110% and medium CTC of INR 18 lakhs per annum.
Take a look at the Scaler Career Assessment Report audited by B2K Analytics for more insights.

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.
We are providing certificates to all the learners after the end of the program
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 whatsapp groups for collaboration, dedicated problem-solving support on the dashboard, and Scaler support through 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.

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Program Registration
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