In Collaboration

135+

Hiring Partners

Best Pricing

70% < than other vendors

135+

Hiring Partners

Best Pricing

70% < than others

Experts

Trainers from top MNCs

Job Ready

with career support

The Data Science program will take you on a journey from learning the very basics of the programming to the advance skills of Collecting, Analysing & Managing data and presenting these methods to business scenarios. The curriculum is built on actual use-cases of more than 7500+ job positions in data science.
10+
Courses
56+
Project Hrs
235+
Learning Hrs
Prepares for Job positions
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Jr. Data Scientist
Machine Learning Developer
Data Analyst
Business Analyst
Software Developer (ML & AI)
Our top data science hiring partners
Our top data science hiring partners
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Program Highlights

Let Numbers Talk

Expert Trainers

Ex Oracle & IBM Data Scientists

235+ Hours of learning

Self-Paced & Live classes

4+ hands-on Projects

In collaboration with hiring partners

Experience Certificate

Dedicated Mentor Support

Exclusive Campus Interviews

One job offer Guarantee

Competitive Pricing

Upto 70% less than other vendors

Machine Learning Course Overview
Data Visualization (Tableau Introduction)
Deep Learning (PyTorch)
What is Data Science ?
Data Science Program Curriculum
Data Science Program – Career Building
Data Visualization (Tableau Introduction)
Program Syllabus
Python for Data Science
This course is designed for students with little to no coding experience and if you’re a coder, it will be a great refresher for important syntax and topics, those are essential to get started as a data science apprentice.

Part 1: Programming for Data Science (Python Oriented)

Topics Covered:

  • Introduction to Python Programming Language
  • Why is python most data science compatible language?
  • How is it different from other programming languages like C, C++, Java)
  • Introduction to data(Object) types in Python
  • Exploring Data structures in Python
  • Python’s applications in the general field of Programming with emphasis on Data Science.
  • Lists, Sets, Dictionaries and Tuples
  • DataFrames (Introduction to Pandas Library)
  • Strings (String Concatenation)
  • Strings (String Searching)
  • Introduction to Stacks
  • Queues and Linked Lists using inbuilt function as well as using OOP(Introduction to OOP in python)
  • Different Problems on Data Structures in Python(basic level -> intermediate level ->GATE level)( +8 problems)
  • Introduction to Recursion in Programming using Python
  • Complexity Analysis of Algorithms (Theoretical with python examples)
Python for Data Science: Part 2

Part 2: Programming for Data Science (Python Oriented)

Topics Covered:

  • Introduction to Trees
  • Complete Binary Trees (with implementations)
  • Full Binary Trees (with implementations)
  • Range Trees (with implementations)
  • KD Trees (with implementations)
  • R Trees (with implementations)
  • Introduction to Greedy Algorithms using Linear as well as Non-Linear Data Structures.
  • Problems on Greedy Algorithms.
  • Introduction to Divide and Conquer Algorithm and Master’s Theorem.
  • Problems on Divide and Conquer techniques.
  • Introduction to Dynamic Programming.
  • Problems on Dynamic Programming.
  • Introduction to Graph Algorithms and their applications.
Statistics Essentials
In this course we will explore real-life business cases using the scientific methods. We will be covering the fundamentals going all the way to learning different techniques and equip you with tools required to be successful in a modern corporate environment.

Part 1: Statistics Essentials

Topics Covered:

  • Introduction to Probability Distributions.
  • Commonly used Statistics in Data Science
  • Descriptive Statistics with Python Examples
  • Variance and Covariance
  • Correlation between Variables
  • Law of Large Numbers
  • Central Limit Theorem
Statistics Essentials: Part 2

Part 2: Statistics Essentials

Topics Covered:

  • Random Numbers
  • Pseudo Random Numbers
  • Statistical Hypothesis Testing
  • Degrees of Freedom in Statistics
  • Critical Values
  • Significance Tests
Machine Learning
Is this course we will cover machine learning and artificial intelligence. We will be covering things like regression, classification, clustering, association rule learning, reinforcement learning and many more. Moreover, for each of those branches, we will explore between 2 to 7 different algorithms, basically to create and code it in python.

Supervised Learning and Liner Regression:

Topics Covered:

  • Supervised Learning and Liner Regression
  • Learning Objectives
  • Supervised Learning
  • Supervised Learning- Real-Life Scenario
  • Understanding the Algorithm
  • Supervised Learning Flow
  • Types of Supervised Learning – Part A
  • Types of Supervised Learning – Part B
  • Types of Classification Algorithms
  • Types of Regression Algorithms – Part A
  • Types of Regression Algorithms – Part B
  • Regression Use Case
  • Accuracy Metrics
  • Cost Function
  • Evaluating Coefficients
  • Challenges in Prediction
  • Logistic Regression – Part A
  • Logistic Regression – Part B
  • Sigmoid Probability
  • Accuracy Matrix

Feature Engineering:

Topics Covered:

  • Feature Selection
  • Regression
  • Factor Analysis
  • Factor Analysis Process
  • Principle Component Analysis (PCA)
  • First Principle Component
  • Eigenvalues and PCA
  • Linear Discriminant Analysis
  • Maximum Separable Line
  • Find Maximum Separable Line
Machine Learning: Part 2

Supervised Learning: Classification

Topics Covered:

  • Overview of Classification
  • Classification: A Supervised Learning Algorithm
  • Use Cases
  • Classification Algorithms
  • Decision Tree Classifier
  • Decision Tree: Examples
  • Decision Tree Formation
  • Choosing the Classifier
  • Overfitting of Decision Trees
  • Random Forest Classifier- Bagging and Bootstrapping
  • Decision Tree and Random Forest Classifier
  • Performance Measures: Confusion Matrix
  • Performance Measures: Cost Matrix
  • Naive Bayes Classifier
  • Steps to Calculate Posterior Probability: Part A
  • Steps to Calculate Posterior Probability: Part B
  • Support Vector Machines: Linear Separability
  • Support Vector Machines: Classification Margin
  • Linear SVM: Mathematical Representation
  • Non-linear SVMs
  • The Kernel Trick
Machine Learning: Part 3

Unsupervised Learning

Topics Covered:

  • Overview
  • Example and Applications of Unsupervised Learning
  • Clustering
  • Hierarchical Clustering
  • Hierarchical Clustering: Example
  • K-means Clustering

Time Series Modelling

Topics Covered:

  • Overview of Time Series Modelling
  • Time Series Pattern Types Part A
  • Time Series Pattern Types Part B
  • White Noise
  • Stationarity
  • Removal of Non-Stationarity
  • Time Series Models Part A
  • Time Series Models Part B
  • Time Series Models Part C

Text Mining

Topics Covered:

  • Overview of Text Mining
  • Significance of Text Mining
  • Applications of Text Mining
  • Natural Language Toolkit Library
  • Text Extraction and Pre-processing
  • NLP Process Workflow
  • Structuring Sentences
  • Rendering Syntax Trees
  • Structuring Sentences: Chunking and Chunk Parsing
  • NP and VP Chunk and Parser
  • Structuring Sentences: Chinking
Machine Learning (Hands-On)
Is this section of machine learning, we will provide the takeaway templates for all the algorithms covered, which can be downloaded to follow a hands-on approach. So overall in this section learn the live application of machine learning and artificial intelligence use cases. Which in turn will solidify the all the key practices & concepts.

Machine Learning: Hands-On coverage

Introduction to Statistical Learning:

  • What Is Statistical Learning?
  • Why Estimate f?
  • How Do We Estimate f?
  • The Trade-Off Between Prediction Accuracy and Model Interpretability
  • Supervised Versus Unsupervised Learning
  • Regression Versus Classification Problems
  • Assessing Model Accuracy
  • Measuring the Quality of Fit
  • The Bias-Variance Trade-Off
  • The Classification Setting


Linear Regression: Simple Linear Regression:

  • Estimating the Coefficients
  • Assessing the Accuracy of the Coefficient Estimates
  • Assessing the Accuracy of the Model
  • R^2 Statistic
  • Multiple Linear Regression
  • Estimating the Regression Coefficients
  • Relationship Between the Response and Predictors
  • Deciding on Important Variables
  • Model Fit
  • Predictions
  • Considerations in the Regression Model: Qualitative Predictors
  • Predictors with Only Two Levels
  • Qualitative Predictors with More than Two Levels
  • Extensions of the Linear Model, Non-linear Relationships
  • Outliers
  • High Leverage Points
  • Collinearity, Case Study: Marketing Plan
  • Comparison of Linear Regression with K-Nearest Neighbors

Classification: An Overview of Classification:

  • Why Not Linear Regression?
  • Logistic Regression
  • Linear Discriminant Analysis
  • A Comparison of Classification Methods
Machine Learning (Hands-On): Part 2

Machine Learning – Part 2: Hands-On Coverage

Resampling Methods:

  • Cross-Validation
  • The Bootstrap


Linear Model Selection and Regularization:

  • Subset Selection
  • Stepwise Selection
  • Cp, AIC, BIC, and Adjusted R^2
  • Shrinkage Methods (Ridge Regression, The Lasso)
  • Dimension Reduction Methods (An Overview of Principal Components Analysis, Partial Least Squares)
  • Considerations in High Dimensions

Moving Beyond Linearity:

  • Introduction to Polynomial Regression
  • Step Functions
  • Basis Functions
  • Regression Splines
  • Smoothing Splines
Machine Learning (Hands-On): Part 3

Machine Learning – Part 3: Hands-On Coverage

Tree-Based Methods:

  • The Basics of Decision Trees
  • Regression Trees
  • Classification Trees
  • Trees Versus Linear Models
  • Advantages and Disadvantages of Trees
  • Bagging, Random Forests, Boosting


Support Vector Machines:

  • Maximal Margin Classifier
  • Classification Using a Separating Hyperplane
  • Construction of the Maximal Margin Classifier
  • The Non-separable Case
  • Support Vector Classifiers
  • Details of the Support Vector Classifier
  • Classification with Non-linear Decision Boundaries
  • Kernels
  • Case Study: Heart Disease Data
  • SVMs with More than Two Classes
  • Relationship to Logistic Regression

Unsupervised Learning:

  • The Challenge of Unsupervised Learning
  • Revisit of Principal Components Analysis
  • Clustering Methods: K-Means Clustering
  • Hierarchical Clustering
Data Visualization (Tableau)
Tableau is an incredibly powerful and currently the fastest growing data visualization & analysis tool. This course will allow you to take the raw data files and turn into clear intuitive visualizations in the form of worksheets, dashboards, etc.
Apart from making your data look presentable, This course will significantly speed up the data analysis process as it make data much easier to communicate and understand.

Data Visualization: With Tableau

Tableau for Data Visualization: Certification Oriented:

  • Introduction to Tableau
  • Tableau Fundamentals- Part 1
  • Tableau Fundamentals- Part 2
  • Field Types in Tableau
  • Chart Types in Tableau- Part 1
  • Chart Types in Tableau- Part 2
  • Cosmetics in Tableau
  • Field & Chart Types in Tableau (Quiz)
  • Organize and Simplify data- Part 1
  • Organize and Simplify data- Part 2
  • Organize and Simplify data (Quiz)
  • Manage Data in Tableau
  • Combine Data in Tableau
  • Connection Options in Tableau
  • Data Connections (Quiz)
Data Visualization (Tableau): Part 2

Data Visualization – Part 2: With Tableau

Tableau for Data Visualization – Part 2: Certification Oriented:

  • Table Calculations in Tableau
  • Calculated Fields in Tableau
  • Level of Detail Calculations in Tableau
  • Calculations in Tableau (Quiz)
  • Maps in Tableau
  • Maps in Tableau (Quiz)
  • Analytics in Tableau
  • Dashboard and Stories in Tableau
  • Sample Test 1
  • Sample Test 2
Deep Learning & Natural Language Processing
In this course we will cover the very fundamentals upto the core of neural networks and deep learning. We learn about CNNs, RNNs, LSTMs and so on.
Some of the deep learning applications, we’ll have a look at speech recognition, speech generation, natural language processing and face recognition.

Deep Learning: Hands-On coverage

Introduction to Statistical Learning:

  • Theoretical Understanding of Artificial Neural Networks
  • Back Propagation Mechanisms(Simple Linear Model using TensorFlow)
  • Introduction to Convolutional Neural Networks
  • Introduction to Keras
  • Ensemble Learning
  • Visual Analysis
  • Natural Language Processing
  • Reinforcement Learning
  • Transfer Learning
  • Inception Model
  • Video Classification
  • Noise Subtraction
  • Machine Translation


Natural Language Processing: Special Project (With Templates and Code)

  • Basics of the supervised learning paradigm
  • Introduction to Tensors
  • Introduction to PyTorch
  • Introduction to Keras
  • Traditional concepts of NLP
  • Computational graphs and Yada Yada
  • Finding words in a text Dataset
  • Hidden Markov model
  • Speech Tagging
Industry Project
Notice: Since this would be a real project, provided to the alumni, in collaboration with one of our project partners, for that reason the information about this project will be available at the project assignment only.

As this is the data science program, we will aim to provide you with project within the data science field, but as this will be the project provided by an actual IT organization in order to provide you real industry experience (with certificate).

The project can also be from a sub-field of Data Science like – Data visualization, Data Analysis, Ai Development, ML Development, Statistics, Data Tunnelling, Data Administration, ect.

The alumni will not be able to choose the Company for the live project. We randomly assign the project as per the availability, at the time of the experience gaining stage of the alumni during the program.

Our Experience certificate is accepted in all Tech Companies Including...

Our experience certificate is accepted by all fortune 500 companies including as follows

Our Experience certificate is accepted by
world's top companies

Which makes all the difference to polish your portfolio, and helps you land a
job that fits your needs and goals.

You will be eligible to share your Certificate in the Experience section on resumes, CVs, or other documents.

Talk to our Career Coach

Talk to our Career Coach

Have Questions?

Schedule a call with our student advisors.
From curriculum to payment plans-our experts are happy to help.

Talk to our Career Coach

Talk to our Career Coach

Recruitment process

How our external campus works

Portfolio alignment
Step 1:
After the course completion, our experts will align your profile and build a professional resume.
Interview Coaching
Step 2:
Train and get you prepared for the interview, to fill the right job roles
A Job offer Guarantee (through our Campus)
Step 4:
Regular slot in our exclusive campus until, one job offer received.
Company Outreach
Step 3:
Forward your Resume to our exclusive hiring partners.
(upto 2 interviews per week)
Portfolio alignment
Step 1:
After the course completion, our experts will align your profile and build a professional resume.
Interview Coaching
Step 2:
Train and get you prepared for the interview, to fill the right job roles
Company Outreach
Step 3:
Forward your Resume to our exclusive hiring partners.
(upto 2 interviews per week)
A Job offer Guarantee (through our Campus)
Step 4:
Regular slot in our exclusive campus until, one job offer received.
Get Started
Data Science Program

 ₹ 75,000 

55,999
Discounted only till, 28th April 2024
Our flagship Data Science Program, representing career-track learning at its most innovative. Build a job-ready portfolio, and get recruited.
Get Started
Data Science Program

 ₹ 24,000 

19,999
End of month get 10% off
Our flagship Data Science Program, representing career-track learning at its most innovative. Build a job-ready portfolio, and get recruited.

Meet your Trainers

Transforming careers forever

Meet your amazing Trainers

Supriya Mittra

Supriya mam is a coding whiz and has worked with some of the biggest organizations like Congnizant, TCS, KPMG, Hyundai and IBM over the span of 10 years in the industry. She is also a Data enthusiast and work as a Data Management professional in IBM.
Data Specialist at

Pankaj Chadha

Pankaj sir has 10+ years of industry experience. He has vast experience of working with MNCs like Samsung, Chase and IBM. He is a Data Specialist at IBM Group. He is well-versed with the latest IT technologies and is a perfect mentor to follow for all IT aspirants.
Data Manager at

Mohinder Jha

Mohinder sir has a Bachelor’s degree from IIT Roorkee and is one of the pioneer experts of data analysis. He has an extensive IT career spanning almost 20 years, were he worked and managed teams in companies like Infosys, HCL, Dell and work as a Data Expert at Amazon.
Data Analyst at

Supriya Mittra

Supriya mam is a coding whiz and has worked with some of the biggest organizations like Congnizant, TCS, KPMG, Hyundai and IBM over the span of 10 years in the industry. She is also a Data enthusiast and work as a Data Management professional in IBM.
Data Specialist at

Prashanth Natarajan

Prashanth sir has 10+ years of industry experience. He has vast experience of working with MNCs like Samsung, Chase and IBM. He is a Data Specialist at IBM Group. He is well-versed with the latest IT technologies and is a perfect mentor to follow for all IT aspirants.
Data Manager at

Dr. Manohar Rao

Manohar sir have been a proud Alumni of Stanford University and is an experienced consulting professional from last 15 years, having a broad understanding of solutions, industry best practices, multiple business processes or technology designs within a product/technology family.
Business Analyst at

Our amazing alumni recruitment outcomes

Naveen chaitanya

Dinesh Bansal

Karanpreet Singh

Shivangi Sharma

Shahid Shigri

Archana Paswan

Saquib Ansari

Vivek Tiwari

Shahid Shigri

Archana Paswan

Online Data Science Program FAQ

No, more struggle to compete for open job positions. Leverage our in-house Campus Recruitment, exclusive only for LearnAlumni students. Get recruited with LearnAlumni® Campus, having access to +135 recruitment partners.
It will be either LearnAlumni RFC work for one of the client or Proof of concept developed within LearnAlumni. Generally, the Project work are for 1000+ pages/100+ hours, however they will be optimized for the right amount of candidate exposure.
Yes, it can be shown as experience while applying job. You will get the experience certificate.

After your program completion, You will be forwarded in the recruitment round.
From there onwards, our career experts will build your portfolio to present in the job interview.

Then the experts will fill the gaps in your interviewing skills and set you up for job interviews for our internal LearnAlumni campus recruitment.

It includes the mandatory topics from Industry certification for candidate to provide an edge during MNC interview sessions. Eg . Walk through the Corporate Project Life Cycle from Bidding of the Project to the design and its implementation.
It will include the topics from PMP, ITIL, Six hat thinking.
We keep it real simple, all our lead trainers have minimum 10 years of experience in a fortune 500 company and at least 5 years experience as a data scientist/data engineer.
This will provide the detail insight to the MNC roles, teams, tools, secrets and their enemies.
Additionally the view point of candidate and what path they have to carve to reach his/her goals.
It is a Win Win situation for everyone.
We provide you actual live and research projects to get real-time experience, which in-turn reduce the work base cost for our client work and you earn your experience certificate to advance your career path ahead.

The course is a unique blend of self-paced class and 1-to-1 mentor support for doubt clearing.
The classes will be delivered on a weekly basis to keep a steady grow and a timeline of 3 month for optimum learning.
After which your live industry project, followed by recruitment training and processes.

You don’t need to have any prior experience in Data Science, however awareness to the usage of the Desktop/PC is required.
The good part is that you don’t have to worry about any tools as you will already be provided with the “go to” interface to get started as soon as you are enrolled.
Machine Learning Course Overview
Become a future proof
Data Expert
Data Visualization (Tableau Introduction)
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Deep Learning (PyTorch)
Become a future proof
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What is Data Science ?
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Data Science Program Curriculum
Become a future proof
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Data Science Program – Career Bulding
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