Full Stack Data Science
221+
Students Empowered
Online / Classroom
Format
6 Months
Duration
110+
Hiring Partners
Program Highlights
 Interactive live sessions
 Experienced corporate trainer onboard
 Both online and classroom sessions
 Realtime industrial teaching
 Flexible selfpacing
Course Framework
 120+ hours of live online sessions
 24/7 access to LMS portal
 Handling realtime projects
 Periodical mock test
 One to one performance assessment
Placement Assistance
 Resume building
 Preplacement training
 Mock interviews
 Career expo
 shortlisting CVs with job portal collaborations
Why FullStack Data Science?
By 2020, the device connectivity is to reach its peak, projecting an estimate of around 30 million terabytes of data every day.
A 10% rise in data accessibility will result in more than $65 millionadditional net income for the typical Fortune 1000 company.
By 2025, the Data Science analytics sector in India, is estimated to grow eightfold, reaching $16 billion.
Who should learn Full Stack Data Science?
Anyone who is curious about the number, information, visualizing data and AI.
An enthusiast with a decent background in Maths and CS.
Professionals wanting a career uplift.
Freshers wishing to venture into the field of Big Data through acquiring analytics skills.
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.
Achieved objectives
 You Can Become A FullStack 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.
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 upto 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)
Data Science Mock Interview at Geeklurn
 100% Placements upto 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
 MultiDimensional 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 Rstudio
 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 reshaping
 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
 RSquared Intuition
 Adjusted RSquared 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: KNearest Neighbours [KNN]
 KNearest Neighbour Intuition
 How to get the dataset
 KNN 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: Kmeans Clustering
 KMeans Clustering Intuition
 KMeans Random Initialization Trap
 KMeans Selecting The Number Of Clusters
 How to get the dataset
 KMeans Clustering in R
 Installation of python / Jupiter Notebook / SPYDER
 Keywords
 Python variables and Data types
 Types of Operators
 Builtin 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.
PYTHONPROGRAMMING
 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
 MultiDimensional Charts
Section 3: Data PreProcessing
 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
 Multiple Linear Regression
 Dataset + Business Problem Description
 Multiple Linear Regression in Python – Step 1
 Multiple Linear Regression in Python – Step 2
 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: Kmeans Clustering
 KMeans Clustering Intuition
 KMeans Random Initialization Trap
 KMeans Selecting The Number Of Clusters
 How to get the dataset
 KMeans 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.
PYTHONPROGRAMMING
 Installing the packages
 Installation of Tensorflow, Keras
 Keywords
 Builtin functions
 Importing and exporting data from python into various formats
 Plan of Attack (Practical Tutorials)
 Where to get the Materials
 Windows Option 1: EndtoEnd 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 preprocessing 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: QLearning Intuition
 The Bellman Equation
 Markov Decision Process
 QLearning Intuition
 QLearning Visualization
Section 4: Deep QLearning Intuition
 Plan of Attack
 Deep QLearning Intuition  Learning
 Deep QLearning Intuition  Acting
 Experience Replay
 Action Selection Policies

120 Hours of Content

6 Months Course

12 RealTime Projects
Be A Certified Full Stack Data Scientist
 Reflects your skill.
 Upgrade your business impact.
 Helps you getting hired.
Course Features
Guidance from Corporate Specialists
Resolve all theory and projects related queries from our industrial mentors.
Corporate Boost Camps
Participate in workshops and live webinars to understand technical terms and trends and make your learning reasonable.
Peer Networking
Build around a cohesive learning network with workmates, mentors, and experts to share ideas, address intraqueries and resolve projects, learning, and practical learning ambiguities.
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.
Tools Covered
Course Fee
₹60,000
Just Pay
₹5,500/month
(Prepaid)
One Time Registration Fee
₹10,000
(At the time of course registration)
Get a chance to win a scholarship up to ₹50,000 T&C Apply
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How will you benefit from this course
 Certified Full Stack Data Science Without Quitting Your Job
 CuttingEdge Curriculum Designed By Industry Experts
 Alumni Status From GeekLurn
 Career Transition With Up To 45% Salary Hike
 Hiring Opportunities From Uber, Microsoft, PWC, Genpact and More
Our Hiring Partners
Opening the doors to the best in the industry
Student Reviews
Frequently Asked Questions
We encapsulate both theories and practical sessions where you can work on live on projects subsidized with industry.
Our 24/7 LMS support program is there to help you overcome any lagging behind and compensate for the missed lessons.
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.