Full Stack Data Science

Learn cutting edge business analytics, Adept data into actionable insights, and be an expert with our certified full-stack data science program.
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221+

Students Empowered

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Online / Classroom

Format

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6 Months

Duration

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110+

Hiring Partners

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Program Highlights

  • Interactive live sessions
  • Experienced corporate trainer onboard
  • Both online and classroom sessions
  • Real-time industrial teaching
  • Flexible self-pacing
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Course Framework

  • 120+ hours of live online sessions
  • 24/7 access to LMS portal
  • Handling real-time projects
  • Periodical mock test
  • One to one performance assessment
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Placement Assistance

  • Resume building
  • Pre-placement training
  • Mock interviews
  • Career expo
  • shortlisting CVs with job portal collaborations

Why Full-Stack Data Science?

Full Stack Data science is the one who has worked in all of these departments including Machine Learning, Big Data, Data Visualisation, Productising Data Science Models and has sufficient knowledge of all these. Full stack data science engineering is the integration of data scientists and data science engineers.
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By 2020, the device connectivity is to reach its peak, projecting an estimate of around 30 million terabytes of data every day.

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A 10% rise in data accessibility will result in more than $65 million-additional net income for the typical Fortune 1000 company.

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By 2025, the Data Science analytics sector in India, is estimated to grow eightfold, reaching $16 billion.

Who should learn Full Stack Data Science?

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Anyone who is curious about the number, information, visualizing data and AI.

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An enthusiast with a decent background in Maths and CS.

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Professionals wanting a career uplift.

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Freshers wishing to venture into the field of Big Data through acquiring analytics skills.

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Prerequisite skills

  • Basic concepts of Maths like statistics, calculus, and probability.
  • Subtle familiarity with python/Java/R.
  • Basic knowledge about SQL.
  • Passion for developing business acumen.
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Achieved objectives

  • You Can Become A Full-Stack Data Scientist.
  • You Will Master Data Analysis, Machine And Deep Learning.
  • You Will Learn Decision Making And Control Flow.
  • You Will Grow Proficient In Neural Networking And Text Mining.
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Job opportunities

  • Business analytics professional.
  • Business intelligence professional.
  • Data scientist
  • Big data analyst
  • HR analytics professional
  • Marketing analytics professional.

Check out Introductory live session on Datascience & AI

  • Attend 5 Trail classes for FREE
  • 100% placements up-to Rs25 Lacs per annum(t&c)
  • Get a chance to work with top chief data scientists across the globe.
  • Scholarships up to Rs1,50,000. (T&C)

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  • 100% Placements up-to Rs25 Lacs per annum.
  • Get a chance to choose from 100+ MNC companies.

Campus Placements

Instructors

Data Science Introductory Classes

BASIC LEVEL

  • Introduction to Data Science
  • Business Intelligence Vs Data Analysis Vs Data Scientist
  • Data Scientist Roles.
  • Different Disciplines of Data Science
    • Machine Learning
    • Natural Language Processing
    • Deep Learning
  • Applications of Machine Learning
  • Why Machine Learning is the Future
  • What are prerequisites for Data Science.
    • Statistics
    • R language essentials for Data Science
    • Python essentials for Data Science
  • Different R language packages used for Data Science
  • Different Python modules used for Data Science

Section 1:  Basic Statistics

  • Descriptive statistics and Inferential Statistics
  • Sample and Population
  • Variables and Data types
  • Percentiles
  • Measures of Central Tendency
  • Measures of Spread
  • Skewness, Kurtosis
  • Degrees of freedom
  • Variance, Covariance, Correlation

Section 2: Exploratory Data analysis and Data Visualization in R

  • Summary Statistics
  • Data Transformations
  • Outlier Detection and Management
  • Charts and Graphs
  • One Dimensional Chart
  • Box plots
  • Bar graph
  • Histogram
  • Scatter plots
  • Multi-Dimensional Charts

Section 3:  Probability

  • Sampling Distribution
  • Standard Probability Distribution Functions
  • Bernoulli, Binomial distributions
  • Normal distributions

  • Installing R(Mac, Linux & Windows)

    Why R and importance of R in Analytics? Installation of R and R-studio

    • Data types
    • Operators
    • Loops
    • Lists, Sets
    • Vectors
    • Matrices
    • Factors
    • Functions (Built in and User defined functions)
    • Packages , libraries and their installation e Data manipulation and re-shaping
    • Data Visualization using R

Introduction to Supervised and Unsupervised Learning

Section 1: Simple Linear Regression

  • Linear Regression
  • Types of Linear Regressions
  • What is slope and intercept.
  • How to Derive Simple Linear Regression coefficients.
  • What is OLS method and Gradient Descent Method
  • Dataset + Business Problem Description
  • Simple Linear Regression in R – Step 1
  • Simple Linear Regression in R – Step 2
  • Simple Linear Regression in R – Step 3
  • Simple Linear Regression in R – Step 4
  • Simple Linear Regression in R– HOMEWORK !
  • Simple Linear Regression in R– Homework Solution

Section 2:  Multiple Linear Regressions

  • Dataset + Business Problem Description
  • Multiple Linear Regression Intuition – Step 1
  • Multiple Linear Regression Intuition – Step 2
  • Multiple Linear Regression Intuition – Step 3
  • Multiple Linear Regression Intuition – Step 4
  • Multiple Linear Regression Intuition – Step 5
  • Multiple Linear Regression in R – HOMEWORK !
  • Multiple Linear Regression in R – Homework Solution

 

Section 3: Evaluating Regression Models

  • How to calculate Mean Square Error
  • R-Squared Intuition
  • Adjusted R-Squared Intuition
  • Evaluating Regression Models Performance
  • Interpreting Linear Regression Coefficients
  • Developing Accuracy testing functions.

Section 4: Classification Algorithms

  • What is a classification model.
  • different types of classification models
  • when to use what type of model
  • how to measure accuracy of a classification model

 

Section 5: Logistic Regression

  • Logistic Regression Intuition
  • How to get the dataset
  • Logistic Regression in R – Step 1
  • Logistic Regression in R – Step 2
  • Logistic Regression in R – Step 3
  • Logistic Regression in R – Step 4

Section 6: Evaluating classification models performance

  • False Positives & False Negatives
  • Confusion Matrix
  • Accuracy
  • Sensitivity, Specificity, Precision
  • Loss function

Section 7: K-Nearest Neighbours [KNN]

  • K-Nearest Neighbour Intuition
  • How to get the dataset
  • K-NN in R

Section 8: Decision Tree Classifier

  • Decision Tree Classification Intuition
  • Entropy method
  • Gini Calculation
  • Information Gain
  • How to get the dataset
  • Decision Tree Classification in R

Section 9: Decision Tree Regression

  • Decision Tree Regression Intuition
  • How to get the dataset
  • Decision Tree Regression in R

Section 10: Random Forest Classifier

  • random Forest Classification Intuition
  • how random forest works
  • How to get the dataset
  • Random Forest Classification in R

Section 11: Random Forest Regression

  • Random Forest Regression Intuition
  • How to get the dataset
  • Random Forest Regression in R

Section 12: Unsupervised Learning - Clustering Algorithms

  • What is a unsupervised learning.
  • how to use unsupervised for business problems
  • Different Clustering models.

Section 13:  K-means Clustering

  • K-Means Clustering Intuition
  • K-Means Random Initialization Trap
  • K-Means Selecting The Number Of Clusters
  • How to get the dataset
  • K-Means Clustering in R

  • Installation of python / Jupiter Notebook / SPYDER
  • Keywords
  • Python variables and Data types
  • Types of Operators
  • Built-in functions
  • String Formatting
  • Lists
  • Tuples
  • Indexing
  • Dictionaries
  • Sets
  • Importing and exporting data from python into various formats
  • Installing the packages
  • Installing how to make use of Numpy and Pandas

  • Predicting the Sales
  • Predicting the mileage of different brands of cars
  • Predicting the Breast cancer patient’s information.
  • Predicting the diabetic Patients on Health care dataset

INTERMEDIATE LEVEL

  • Refreshing the Basic level course what we learned.

PYTHON-PROGRAMMING

  • Installation of python / Jupiter Notebook / SPYDER
  • Keywords
  • Installing the packages
  • Numpy, Pandas, SckitLearn., Matplotlib, stats models
  • Pandas inbuilt Functions – Ndim(),axes(),vaues(),head(),tail()
  • Aggregation
  • Merging
  • Group By Operations
  • Concatenation
  • Joining – Types of joins
  • Exploring a data set
  • Creating data frames
  • Accessing data frames
  • Importing and Exporting datasets
  • Cross tabs
  • Python Conditions and If statements
  • Python While loops
  • Creating a functions

Section 1:  Advanced Statistics

  • Descriptive statistics and Inferential Statistics in Python
  • Test of Hypothesis
  • Confidence Interval

Section 2: EDA and Data Visualization in Python

  • Summary Statistics
  • Data Transformations
  • Outlier Detection and Management
  • Charts and Graphs
  • Percentiles and quartiles
  • Box plots
  • Bar graph
  • Histogram
  • Scatter plots
  • Multi-Dimensional Charts

Section 3: Data Pre-Processing

  • How to treat with Missing values
  • How to treat with Outliers
  • Recalling the knowledge of central tendency
  • What are imputation techniques
  • Data Transformation
  • Data centre and data scaling.
  • What is Standardization
  • What is Normalization
  • One Hot Encoding
  • Label Encoding

Section 1: Simple Linear Regression

  • Linear Regression
  • How to Derive Simple Linear Regression coefficients in python.
  • Dataset + Business Problem Description
  • Simple Linear Regression in Python
  • Simple Linear Regression in Python – HOME WORK !
  • Simple Linear Regression in Python – Homework Solution

Section 2:  Introduction to Linear Algebra

  • Creating a Matrix
  • Row matrix, Column Matrix, Transpose Matrix
  • Sum of Matrices and Product of Matrices
  • Inverse matrix
  • How to calculate ordinary least square method.

Section 3:  Multiple Linear Regressions with Linear Algebra

  1. Multiple Linear Regression
  • Dataset + Business Problem Description
  • Multiple Linear Regression in Python – Step 1
  • Multiple Linear Regression in Python – Step 2
  1. Variable Selection Methods
  • All possible regression techniques
  • Individual Coefficient test
  • Multicollinearity
  • Multiple Linear Regression in Python – HOMEWORK !
  • Multiple Linear Regression in Python – Homework Solution

Section 4:  Gradient Descent Method

  • Introduction to calculus
  • How to calculate Derivatives
  • Understanding Chain Rule
  • How Gradient descent will work on regression techniques
  • Types of Gradient Descent

Section 5: Logistic Regression

  • Logistic Regression Intuition
  • How to get the dataset
  • Logistic Regression in Python

Section 6: Model Validation

  • Train and Test data
  • Model selection - How to select a model
  • Resampling method - Different techniques to select a model
  • Cross validation

Section 7: Decision Tree Classifier

  • How to get the dataset
  • Decision Tree Classification in Python

Section 8: Decision Tree Regression

  • How to get the dataset
  • Decision Tree Regression in Python

Section 9: Introduction to Ensemble methods

  • Parallel Ensemble Methods
  • Sequential Ensemble Methods

Section 10: Random Forest Regression

  • Random Forest Regression Intuition
  • How to get the dataset
  • Random Forest Regression in Python

Section 11: Random Forest Classifier

  • Random Forest Classifier
  • How to get the dataset
  • Random Forest Classifier in Python

Section 12: Bagging Regression

  • Bagging Regression Intuition
  • How to get the dataset
  • Bagging Regression in Python

Section 13: Bagging Classifier

  • Bagging Classifier Intuition
  • How to get the dataset
  • Bagging Classier in Python

Section 14: Ada Boost Classifier

  • Ada Boost Classifier Intuition
  • How to get the dataset
  • How to evaluate the performance in Ada Boost

Section 15: Gradient Boost Classifier

  • Gradient Boost Classifier Intuition
  • How to get the dataset
  • How to evaluate the performance in Gradient Boost

Section 16: XGBoost Classifier

  • XGBoost Classifier Intuition
  • How to get the dataset
  • How to evaluate the performance in XGBoost

Section 17: Support Vector Machine

  • SVM Intuition
  • How to get the dataset
  • SVM in Python
  • SVM in R

Section 18: Support Vector Classifier

  • How to get the dataset
  • SVR Intuition
  • SVR in Python
  • SVR in R

Section 19: Kernel SVM

  • Kernel SVM Intuition
  • Mapping to a higher dimension
  • The Kernel Trick
  • Types of Kernel Functions
  • How to get the dataset
  • Kernel SVM in Python
  • Kernel SVM in R

Section 20: Naive Bayes Classifier

  • Recalling the knowledge of Probability
  • Bayes Theorem
  • Naive Bayes Intuition
  • How to get the dataset
  • Naive Bayes in Python
  • Naive Bayes in R

Unsupervised Learning

Section 21:  K-means Clustering

  • K-Means Clustering Intuition
  • K-Means Random Initialization Trap
  • K-Means Selecting The Number Of Clusters
  • How to get the dataset
  • K-Means Clustering in Python

Section 22: Hierarchical Clustering

  • Hierarchical Clustering Intuition
  • Hierarchical Clustering How Dendrograms Work
  • Hierarchical Clustering Using Dendrograms
  • How to get the dataset
  • HC in Python – Step 1
  • HC in Python – Step 2
  • HC in Python – Step 3

Section 23: Dimensional Reduction Techniques

  • What is a dimension?
  • Why we should reduce dimensionality.
  • Different techniques to reduce

Section 24: Principal Compound Analysis

  • PCA in Python – Step 1
  • PCA in Python – Step 2
  • PCA in Python – Step 3
  • PCA in R – Step 1
  • PCA in R – Step 2
  • PCA in R – Step 3

Section 25: Introduction to Natural Language processing

  • Introduction to NLP
  • What is NLP and its importance.
  • What we can do with NLP

Section 26: Introduction to Deep Learning

Artificial Neural Networks

  • Gradient Descent
  • Stochastic Gradient Descent
  • Back propagation
  • How to get the dataset
  • Business Problem Description
  • ANN in R– Step 1
  • ANN in R – Step 2
  • ANN in R – Step 3

  • Predicting the salaries based on positions of employees
  • Predicting the car seats price in an industry
  • Predicting the bank notes fake or authentic
  • Predicting the petrol consumption on Highways

ADVANCED LEVEL

  • Refreshing the Basic level course what we learned.
  • Refreshing the Intermediate level course what we learned.

 PYTHON-PROGRAMMING

  • Installing the packages
  • Installation of Tensorflow, Keras
  • Keywords
  • Built-in functions
  • Importing and exporting data from python into various formats
  • Plan of Attack (Practical Tutorials)
  • Where to get the Materials
  • Windows Option 1: End-to-End installation steps
  • Windows Option 2 - Part B: Installing PyTorch
  • Mac or Linux: Installing Anaconda
  • Mac or Linux: Installing PyTorch and Keras

Section 1: Installation and introduction

  • Introduction to NLP
  • What is nlp and its importance.
  • What we can do with nlp
  • Introduction to spam engines.
  • Introduction to sentiment analyzers.
  • Implementation of text pre-processing using NLTK
  • Word tokenization
  • Sentence tokenization
  • Parts of speech tagging
  • lemmatization
  • Removing stop words
  • Building word clouds

Section 2: Feature engineering techniques from text

  • Feature extraction techniques and importance
  • Word Existence feature
  • Word Frequency feature
  • Word proportion feature
  • Lexical feature
  • Lexical diversification feature
  • Term frequency and inverse document frequency
  • NLP vs Machine Learning
  • How to get the dataset
  • How NLP is connecting with super vised Learning techniques
  • Natural Language Processing in Python – Step 1
  • Natural Language Processing in Python – Step 2
  • Natural Language Processing in Python – Step 3
  • Natural Language Processing in Python – Step 4
  • Natural Language Processing in Python – Step 5

Section 3: Association Rule Mining

  • What are recommendation systems
  • Different ARM algorithms.
  • How ARM used for recommendations.

Section 4: Apriori algorithm

  • Apriori Intuition
  • How to get the dataset
  • How NLP is connecting with Unsupervised learning.
  • Apriori in R – Step 1
  • Apriori in R – Step 2
  • Apriori in R – Step 3
  • Apriori in Python – Step 1
  • Apriori in Python – Step 2

Section 1: Artificial Neural Networks

  • Artificial Neural networks Intuition
  • Plan of Attack
  • The Neuron
  • The Activation Function
  • Types of Activation Function
  • Exploring Sigmoid and Relu functions
  • Understand Cost functions
  • How do Neural Networks work?
  • How do Neural Networks learn?
  • Gradient Descent
  • Stochastic Gradient Descent
  • Back propagation
  • How to get the dataset
  • Business Problem Description
  • ANN in Python – Step 1
  • ANN in Python – Step 2
  • ANN in Python – Step 3
  • ANN in Python – Step 4

Section 2: Deep Neural Networks works with NLP

  • Pre- Processing of text data
  • Applying Term Frequency and Inverse Document Frequency method.
  • Developing a simple neural network
  • Converting to Deep neural network

Section 3: Convolutional Neural Networks

  • What are convolutional neural networks?
  • Step 1 – Convolution Operation
  • Step 1(b) – ReLU Layer
  • CNN in Python – Step 1
  • Step 2 - Pooling
  • Step 3 - Flattening
  • Step 4 - Full Connection
  • Step 5 - Summary

Section 4: Recurrent Neural Networks

    • What you’ll need for RNN
    • Plan of attack
    • The idea behind Recurrent Neural Networks
    • The Vanishing Gradient Problem
    • LSTMs
    • Building a RNN
    • How to get the dataset
    • Building a RNN - Step 1
    • Building a RNN - Step 2
    • Building a RNN - Step 3
    • Building a RNN - Step 4
    • Building a RNN - Step 5

Section 1: Introduction

  • Why AI?
  • Introduction
  • How to get data from practice
  • Some Additional Resources!!

Section 2: Reinforcement Learning

  • What is reinforcement learning?
  • Fundamentals of Reinforcement Learning
  • Elements of Reinforcement learning.
  • Q values and V values

 Section 3: Q-Learning Intuition

  • The Bellman Equation
  • Markov Decision Process
  • Q-Learning Intuition
  • Q-Learning Visualization

 Section 4: Deep Q-Learning Intuition

  • Plan of Attack
  • Deep Q-Learning Intuition - Learning
  • Deep Q-Learning Intuition - Acting
  • Experience Replay
  • Action Selection Policies

  • 120 Hours of Content

  • 6 Months Course

  • 12 Real-Time Projects

Be A Certified Full Stack Data Scientist

Learn Full Stack Data Science and get certified from TOP industries & Universities which :
  • Reflects your skill.
  • Upgrade your business impact.
  • Helps you getting hired.
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Course Features

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Guidance from Corporate Specialists

Resolve all theory and projects related queries from our industrial mentors.

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Corporate Boost Camps

Participate in workshops and live webinars to understand technical terms and trends and make your learning reasonable.

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Peer Networking

Build around a cohesive learning network with workmates, mentors, and experts to share ideas, address intra-queries and resolve projects, learning, and practical learning ambiguities.

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Placement Assurance

We make you industry ready as you get equipped with all the requisites through our extensive and one of its kind learning programs.

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Course Fee

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Student Reviews

Srinivasan
Srinivasan
Cisco
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GeekLurn's Live projects have helped me to possess a real view of business hierarchy. I am grateful to the extremely talented mentors to wave me through pictorial concepts.
Aravind
Aravind
Mindfire
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I was afraid if I could learn data science without having proficiency in coding. But GeekLurn has the advantage of fundamental ground learning where you can start from the scratch irrespective of your background. I am working as a Senior Data Scientist at a reputed firm in Bangalore.
Vidhi Jain
Vidhi Jain
Oracle
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I had an enlightening experience with GeekLurn and was surprised to see such dedication from career mentorship who not only helped me shape my cv but guided me through the entire interview process with thorough sincerity.

Frequently Asked Questions

Yes, we have weekend batches available for working professionals.

We encapsulate both theories and practical sessions where you can work on live on projects subsidized with industry.

Yes, you can. In online instructor-led training, industry experts will train you with a group of our course learners for 120 hours over online conferencing software like Zoom & Webinar. Online Classes will happen every day from Monday to Friday.

Yes, you can pursue data science with fundamental coding ideas like C and C++.

Our 24/7 LMS support program is there to help you overcome any lagging behind and compensate for the missed lessons.

Yes, you can enroll with us without any programming code as we will be guiding you through the basics of coding.

The courses come with a span of 120 hours timeline. It will depend on your convenient schedule and the type of batch you are enrolled into.

Yes, absolutely!. We make sure your experience of learning with us is worth it
by assuring you proper carrier assistance. We indulge in
1. Career expo
2. Resume building
3. Pre-placement training
4. Mock interviews
5. shortlisting CVs with job portal collaborations

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