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1. What are the projects you mentioned above?
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 twice a month 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
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Hitesh Hinduja
Ex
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Aakash Agarwal
Ex
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Deepak Gupta
Ex
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Sanjeev Singh
Ex
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Naga Budigam
Ex
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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 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 your 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 curriculum aligned with the industry?
    Up-to-date curriculum with the fast-evolving Data Science and ML field.

    Beginner

    14 months
    Circle boundary 554a3355e2d5bff7238976da5a14a5f868191107e5574e39835f1dcbf14bb7d3.svg
    Checked circle 1cab94500f0f9b38e95828d32cd7c20f37b3822976dab01e4a1d20417022389a.svg

    Intermediate

    13 months
    Circle boundary 554a3355e2d5bff7238976da5a14a5f868191107e5574e39835f1dcbf14bb7d3.svg
    Checked circle 1cab94500f0f9b38e95828d32cd7c20f37b3822976dab01e4a1d20417022389a.svg

    Advanced

    11 months
    Circle boundary 554a3355e2d5bff7238976da5a14a5f868191107e5574e39835f1dcbf14bb7d3.svg
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    Module - 1

    Beginner Programming

    4 weeks

    Cross 1fa8266e66f20b4c0ee9d61f47e057c9d415531ea455532388884b242099d0f1.svg Not required for Intermediate
    Module - 2

    Intermediate Programming

    4 weeks

    Cross 1fa8266e66f20b4c0ee9d61f47e057c9d415531ea455532388884b242099d0f1.svg Not required for Intermediate
    Module - 3

    Data Track - Common Core

    14 weeks

    Module - 4

    Data Science and Analytics

    14.3 weeks

    Module - 5

    Data Science and Machine Learning

    20.7 weeks

    Module - 6

    ML Ops and Data Engineering

    5.3 weeks

    Module - 7

    Advanced Programming (DSA)

    11.7 weeks

    4 weeks


    • Intro to Python Programing
      • Flowcharts, Data Types, Operators
      • Conditional Statements & Loops
      • Functions & Recursion
      • Strings
      • In-built Data Structures - List, Tuple, Dictionary, Set
      • Practice

    4 weeks


    • Intro to Python Programing
      • Python Refresher
      • Advance Topics
        • Lambda Functions, List Comprehension, Functional Programming, Decorator, Args, Kwargs
      • Object Oriented Programming
      • Exception Handling, Modules, Package, Library, Built-in Modules in Python
      • Basic DSA & Problem Solving
        • Time complexity, List, 2D List, Bit Manipulation, Strings, Searching, Sorting

    14 weeks


    • Python for DSML
      • Numpy, Pandas
      • Data Visualisation using Matplotlib and Seaborn
      • Regular Expressions/Pattern Matching
      • Terminal/OS
      • Git and Github
      • File Handling
    • Probability & Statistics
      • Probability Theory and Desciptive Statistics
        • Combinatorics, Marginal Probability, Joint Probability, Conditional Probability, Bayes Theorem, Mean, Median, Mode, Percentile, IQR, Outlier
      • Probability Distributions
        • DF, PMF, CDF, PPF, Uniform, Gaussian, Bernoulli, Multinomial, Normal Distribution, Poisson, Exponential, Geometric, Log-normal distribution, Pareto/Power Law Distribution
      • Inferential Statistics
        • Confidence Interval Estimates, CLT
    • Maths Refresher for DSML
      • Coordinate Geometry
        • Point, Lines, Slope, Intercept
      • Linear Algebra
        • Vector and Matrices, Unit Vector, Dot product, Projections, Cosine Similarity, Determinant, Transpose, System of Equations
      • Linear Programming Optimisation Basics
      • Estimation Problems
    • Data Acquisition & Unstructured Data
      • Databases and SQL
        • Relational, Non-relational, ER diagrams, SQL Commands, Aggregate Functions, Joins, SubQueries, Normalisation, Scaling patterns, ACID, Dask SQL, Cloud SQL (Athena/BigQuery)
      • Data from Web API, Scraping, Data Cleaning
      • Unstructured Data - OpenCV, Image processing, Smoothening, Morphological Operations, NLTK, Text processing
    • Applied Data Science
      • Hypothesis Testing, Parametric vs non-parametric, Z-test, Chi-square, Skewness, Kurtosis, Normality test
      • Experiment Design, ANOVA, Simulations, Power of Test, A/B testing, Diff n Diff, Multi-arm bandits
      • EDA, Covariance, Correlation, Pearson, Spearman Rank, Multi-dimensional, Feature Engineering, Column normalisation, Standardisation, Covariance matrix, Missing Values, Outlier treatment
    • Data Visualization with Tableau
      • Introduction to Tableau
        • Managing Data Sources and Visualizations
        • Analyzing Data Using Statistical Tools
        • Creating Basic Charts, Dashboards & Actions
        • Introduction to Calculations
    • Product, Strategy, Business
      • Metric Design
      • How to crack Product and Strategy Rounds
      • Domain Knowledge - Banking, Finance, Marketing, Social Media, Operations, Healthcare
      • Experiment Design Advanced

    14.3 weeks


    • Tools for Data Analysts
      • Advanced Tableau
        • Mapping Geographic Data, Using stories to build dashboards, Working with Times and Dates, Creating Conditional Calculations Using Logical Functions, Creating Level of Detail (LOD) Expressions, Summarizing Data Using Table Calculations, Managing Text Strings
      • Advanced SQL
        • Nested Queries, String Functions & Pattern Matching, Mathematical Functions, Date-Time Functions
      • Excel
        • Getting Acclimated with EXCEL, Basic EXCEL Formulas & Functions , Text, Times, and Dates Data Formats, Pivot Table, Introduction to Statistics in EXCEL , Handling Data in EXCEL, Pivot Tables, Charts in Excel
      • R- Programming
        • Creating objects, Matrices, Loops, Functions in R, Data Manipulation in R, Creating frequency tables and cross tables, Building charts, Performing univariate analysis, Working with dates and time in R, Dplyr, GGplot
      • Power BI
        • ata Model: Data & Relationship View, Connecting Different Data sources, Data Modelling, Timeseries, Aggregation, and Filters, Maps, Plotting Techniques, Tables and Relation ships, Creating an Interactive Business Intelligence Report
    • Machine Learning
      • Supervised Machine Learning
        • KNN, Linear Regression, Logistic Regression, Decision Trees, Feature importance, ML Metrics
      • Unsupervised Machine Learning
        • Dimentionality Reduction & Visualization, Anomaly Detection, K-Means, PCA, t-SNE
      • Miscellaneous Machine Learning Topics
        • Text and Image vectorization using Deep-Learning, Interpretable ML, ML Life Cycle
      • NLP Concepts
      • Time Series Forecasting
        • Resampling, Autocorrelation, Forecasting, Seasonal Naive, Double/Triple Exponential (Holt) Residual Analysis, Stationarity tests, Autoregressive methods, moving average, ARIMA, SARIMA.
    • Business Awareness
      • Design of Survey
      • Metric Design
      • Big Data Frameworks
      • Business Case-studies
        • Risk, Product, Banking, Finance, E-Commerce, Social Media, Marketing, Transporation, Healthcare, Operations

    20.7 weeks


    • Essential Maths for Machine Learning
      • Linear Algebra
        • Vector and Matrices, Dot product, Projections, System of Equations, Matrix Transformation, Eigen Vectors and Values, Orthonormal Basis Vectors, SVD, PCA
      • Coordinate Geometry
        • Line, Plane, HyperPlane, Half space, Classification using plane
      • Calculus
        • Functions, Limits, Derivatives, Partial derivatives, Saddle points
    • Supervised Learning
      • Linear Regression, Gradient Descent, Multicollinearity, VIF, R-square, Heteroscedasticity, Sklearn, Polynomial Regression, Bias-Variance trade-off, Regularisation
      • Logistic Regression, Squashing function, AUC. ROC, Precision-Recall Curve, Confusion matrix, Specificity
      • KNN, Decision Trees, Ensemble learning, Bagging, Boosting, SHAP Values
      • Support Vector Machine
      • Bayesian Machine Learning
    • Unsupervised Learning
      • KMeans, Customer Segmentation, Hierarchical, DBSCAN, Anomaly Detection, Local Outlier Factor, Isolation Forest, Dimensionality Reduction, PCA, t-SNE, GMM, Information Theory, Expectation Maximisation
    • Recommender Systems
      • Collaborative/Content filtering, Propensity analysis, Cold start problem
    • Predictive Modeling & Time Series Forecasting
      • EDA, Resampling, Autocorrelation, Forecasting, Seasonal Naive, Double/Triple Exponential (Holt) Residual Analysis
      • Stationarity tests, Autoregressive methods, moving average, ARIMA, SARIMA.
    • Neural Networks
      • Neural Networks - MLP, Backpropagation, Hyperparameter Tuning, Practical Aspects of DL
      • Keras, Tensorflow, Pytorch
    • Computer Vision
      • Convolutional Neural Nets, Data Augmentation, Transfer Learning, CNN Visualisation
      • Popular CNN Architecture - Alex, VGG, ResNet, Inception, DenseNet, EfficientNet, MobileNet
      • Object Segmentation, Localisation and Detection
      • Generative Models, VAEs, GANs, Attention Models, Siamese Networks, Advanced CV.
    • Natural Language Processing
      • Text Processing and representation - Tokenization, Stemming, Lemmatization, Vector space modeling, Cosine Similarity, Euclidean Distance
      • POS tagging, Dependency parsing, Topic Modeling, Language Modeling Embeddings
      • Recurrent Neural Nets, Information Extraction, Entity Recognition, Transformers, HuggingFace, BERT, Building Chatbots
    • Reinforcement Learning and Forecasting
      • Reinforcement Learning, Q-learning, Autonomous players, RNNs and LSTMs for forecasting.

    5.3 weeks


    • ML Ops
      • Project scoping, Experiment tracking using MLFlow/W&B, Scripting (Flask/FastAPI/Streamlit), Testing, Versioning, Docker, CI/CD pipelines, AWS lambda, Monitoring using AWS Kibana, Drift
    • ETL Pipeline
      • Big Data and Distributed Systems
        • Hadoop, Map-reduce, Spark, PySpark, SparkSQL, Spark ML, Orchestration using Airflow, Serving using Flask
      • Big Data and Distributed Systems
        • Data Governance - Data quality, Data Dictionary, Data Access Management, Data storage and recovery procedure, Handling stereotypes and Social biases in data

    11.7 weeks


    • Advanced Data Structures & Algorithms
      • DP
      • Stack
      • Queue
      • Linked List
      • Trees
      • Graphs
      • Heaps
    Download Curriculum

    4 weeks


    • Intro to Python Programing
      • Flowcharts, Data Types, Operators
      • Conditional Statements & Loops
      • Functions & Recursion
      • Strings
      • In-built Data Structures - List, Tuple, Dictionary, Set
      • Practice

    4 weeks


    • Intro to Python Programing
      • Python Refresher
      • Advance Topics
        • Lambda Functions, List Comprehension, Functional Programming, Decorator, Args, Kwargs
      • Object Oriented Programming
      • Exception Handling, Modules, Package, Library, Built-in Modules in Python
      • Basic DSA & Problem Solving
        • Time complexity, List, 2D List, Bit Manipulation, Strings, Searching, Sorting

    14 weeks


    • Python for DSML
      • Numpy, Pandas
      • Data Visualisation using Matplotlib and Seaborn
      • Regular Expressions/Pattern Matching
      • Terminal/OS
      • Git and Github
      • File Handling
    • Probability & Statistics
      • Probability Theory and Desciptive Statistics
        • Combinatorics, Marginal Probability, Joint Probability, Conditional Probability, Bayes Theorem, Mean, Median, Mode, Percentile, IQR, Outlier
      • Probability Distributions
        • DF, PMF, CDF, PPF, Uniform, Gaussian, Bernoulli, Multinomial, Normal Distribution, Poisson, Exponential, Geometric, Log-normal distribution, Pareto/Power Law Distribution
      • Inferential Statistics
        • Confidence Interval Estimates, CLT
    • Maths Refresher for DSML
      • Coordinate Geometry
        • Point, Lines, Slope, Intercept
      • Linear Algebra
        • Vector and Matrices, Unit Vector, Dot product, Projections, Cosine Similarity, Determinant, Transpose, System of Equations
      • Linear Programming Optimisation Basics
      • Estimation Problems
    • Data Acquisition & Unstructured Data
      • Databases and SQL
        • Relational, Non-relational, ER diagrams, SQL Commands, Aggregate Functions, Joins, SubQueries, Normalisation, Scaling patterns, ACID, Dask SQL, Cloud SQL (Athena/BigQuery)
      • Data from Web API, Scraping, Data Cleaning
      • Unstructured Data - OpenCV, Image processing, Smoothening, Morphological Operations, NLTK, Text processing
    • Applied Data Science
      • Hypothesis Testing, Parametric vs non-parametric, Z-test, Chi-square, Skewness, Kurtosis, Normality test
      • Experiment Design, ANOVA, Simulations, Power of Test, A/B testing, Diff n Diff, Multi-arm bandits
      • EDA, Covariance, Correlation, Pearson, Spearman Rank, Multi-dimensional, Feature Engineering, Column normalisation, Standardisation, Covariance matrix, Missing Values, Outlier treatment
    • Data Visualization with Tableau
      • Introduction to Tableau
        • Managing Data Sources and Visualizations
        • Analyzing Data Using Statistical Tools
        • Creating Basic Charts, Dashboards & Actions
        • Introduction to Calculations
    • Product, Strategy, Business
      • Metric Design
      • How to crack Product and Strategy Rounds
      • Domain Knowledge - Banking, Finance, Marketing, Social Media, Operations, Healthcare
      • Experiment Design Advanced

    14.3 weeks


    • Tools for Data Analysts
      • Advanced Tableau
        • Mapping Geographic Data, Using stories to build dashboards, Working with Times and Dates, Creating Conditional Calculations Using Logical Functions, Creating Level of Detail (LOD) Expressions, Summarizing Data Using Table Calculations, Managing Text Strings
      • Advanced SQL
        • Nested Queries, String Functions & Pattern Matching, Mathematical Functions, Date-Time Functions
      • Excel
        • Getting Acclimated with EXCEL, Basic EXCEL Formulas & Functions , Text, Times, and Dates Data Formats, Pivot Table, Introduction to Statistics in EXCEL , Handling Data in EXCEL, Pivot Tables, Charts in Excel
      • R- Programming
        • Creating objects, Matrices, Loops, Functions in R, Data Manipulation in R, Creating frequency tables and cross tables, Building charts, Performing univariate analysis, Working with dates and time in R, Dplyr, GGplot
      • Power BI
        • ata Model: Data & Relationship View, Connecting Different Data sources, Data Modelling, Timeseries, Aggregation, and Filters, Maps, Plotting Techniques, Tables and Relation ships, Creating an Interactive Business Intelligence Report
    • Machine Learning
      • Supervised Machine Learning
        • KNN, Linear Regression, Logistic Regression, Decision Trees, Feature importance, ML Metrics
      • Unsupervised Machine Learning
        • Dimentionality Reduction & Visualization, Anomaly Detection, K-Means, PCA, t-SNE
      • Miscellaneous Machine Learning Topics
        • Text and Image vectorization using Deep-Learning, Interpretable ML, ML Life Cycle
      • NLP Concepts
      • Time Series Forecasting
        • Resampling, Autocorrelation, Forecasting, Seasonal Naive, Double/Triple Exponential (Holt) Residual Analysis, Stationarity tests, Autoregressive methods, moving average, ARIMA, SARIMA.
    • Business Awareness
      • Design of Survey
      • Metric Design
      • Big Data Frameworks
      • Business Case-studies
        • Risk, Product, Banking, Finance, E-Commerce, Social Media, Marketing, Transporation, Healthcare, Operations

    20.7 weeks


    • Essential Maths for Machine Learning
      • Linear Algebra
        • Vector and Matrices, Dot product, Projections, System of Equations, Matrix Transformation, Eigen Vectors and Values, Orthonormal Basis Vectors, SVD, PCA
      • Coordinate Geometry
        • Line, Plane, HyperPlane, Half space, Classification using plane
      • Calculus
        • Functions, Limits, Derivatives, Partial derivatives, Saddle points
    • Supervised Learning
      • Linear Regression, Gradient Descent, Multicollinearity, VIF, R-square, Heteroscedasticity, Sklearn, Polynomial Regression, Bias-Variance trade-off, Regularisation
      • Logistic Regression, Squashing function, AUC. ROC, Precision-Recall Curve, Confusion matrix, Specificity
      • KNN, Decision Trees, Ensemble learning, Bagging, Boosting, SHAP Values
      • Support Vector Machine
      • Bayesian Machine Learning
    • Unsupervised Learning
      • KMeans, Customer Segmentation, Hierarchical, DBSCAN, Anomaly Detection, Local Outlier Factor, Isolation Forest, Dimensionality Reduction, PCA, t-SNE, GMM, Information Theory, Expectation Maximisation
    • Recommender Systems
      • Collaborative/Content filtering, Propensity analysis, Cold start problem
    • Predictive Modeling & Time Series Forecasting
      • EDA, Resampling, Autocorrelation, Forecasting, Seasonal Naive, Double/Triple Exponential (Holt) Residual Analysis
      • Stationarity tests, Autoregressive methods, moving average, ARIMA, SARIMA.
    • Neural Networks
      • Neural Networks - MLP, Backpropagation, Hyperparameter Tuning, Practical Aspects of DL
      • Keras, Tensorflow, Pytorch
    • Computer Vision
      • Convolutional Neural Nets, Data Augmentation, Transfer Learning, CNN Visualisation
      • Popular CNN Architecture - Alex, VGG, ResNet, Inception, DenseNet, EfficientNet, MobileNet
      • Object Segmentation, Localisation and Detection
      • Generative Models, VAEs, GANs, Attention Models, Siamese Networks, Advanced CV.
    • Natural Language Processing
      • Text Processing and representation - Tokenization, Stemming, Lemmatization, Vector space modeling, Cosine Similarity, Euclidean Distance
      • POS tagging, Dependency parsing, Topic Modeling, Language Modeling Embeddings
      • Recurrent Neural Nets, Information Extraction, Entity Recognition, Transformers, HuggingFace, BERT, Building Chatbots
    • Reinforcement Learning and Forecasting
      • Reinforcement Learning, Q-learning, Autonomous players, RNNs and LSTMs for forecasting.

    5.3 weeks


    • ML Ops
      • Project scoping, Experiment tracking using MLFlow/W&B, Scripting (Flask/FastAPI/Streamlit), Testing, Versioning, Docker, CI/CD pipelines, AWS lambda, Monitoring using AWS Kibana, Drift
    • ETL Pipeline
      • Big Data and Distributed Systems
        • Hadoop, Map-reduce, Spark, PySpark, SparkSQL, Spark ML, Orchestration using Airflow, Serving using Flask
      • Big Data and Distributed Systems
        • Data Governance - Data quality, Data Dictionary, Data Access Management, Data storage and recovery procedure, Handling stereotypes and Social biases in data

    11.7 weeks


    • Advanced Data Structures & Algorithms
      • DP
      • Stack
      • Queue
      • Linked List
      • Trees
      • Graphs
      • 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
    Ex
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    Ajay Shenoy
    Ex
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    Harshit Tyagi
    Ex
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    Anant Mittal
    Ex
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    Mohit Uniyal
    Ex
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    Mudit Goel
    Ex
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    Nandini Menon
    Ex
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    Prashant K Tiwari
    Ex
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    Sameer Shah
    Ex
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    Nitish Jaipuria
    Ex
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    Shan Mehrotra
    Ex
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    Sundaravaradhan
    Ex
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    Amit Singh
    Ex
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    Mohit Kukkarl
    Ex
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    Rahul Aggarwal
    Ex
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    Suraaj Hasija
    Ex
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    Suransh Chopra
    Ex
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    Thanish Batcha
    Ex
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    Vishwath parthasarathy
    Ex
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    affordable-course
    11. Great, but can I afford this 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 9,811/- 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
    Reduced Tution Fees
    Upfront Downpayment
    Amount split over EMI
    Duration (Months)
    Monthly Payments
    No Cost Emi
    ₹299,000
    ₹299,000
    ₹35,000
    ₹264,000
    6
    9
    12
    18
    19
    ₹44,000
    ₹29,333
    ₹22,000
    ₹14,667
    ₹13,895
    Standard Emi
    ₹299,000
    ₹299,000
    ₹35,000
    ₹264,000
    24
    36
    ₹13,436
    ₹9,811
    Delievered via our EMI partners - ZestMoney, Propelled, Eduvanz, Liquiloans
    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
    99.99% convinced? Take the .01 leap by attending a demo class with our star instructor Mudit Goel

    Frequently Asked Questions

    Coding background is not required to enroll in this 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 pre-requisitie is that you should have 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 (Probability, Statistics, Linear Algebra, Calculus, Coordinate Geometry).
    For learners who show interest in publishing, we would be happy to provide mentorship and support.
    While designing the Scaler DSML 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 8 months long, equivalent to 2 semesters (1 year) of college but 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 & ML, as much as is required for you to succeed in the role.
    25 projects will be assigned in homework and other 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 projects and relocate. Some international companies also hire directly in India and ask to relocate for job. However, with the surge in WFH, this trend may be ebbing. However, you can continue applying for remote jobs based outside India via LinkedIn.
    Masters and PhD is typically asked for Research focused roles. Most companies do not require a Masters' degree for Data Science role.
    😎 Look who is famous!
    Scaler Data Science and Machine Learning is the talk of the town!
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