Master Program in DATA SCIENCE

Certybox  offer an online Master Program in Data Science, will give you the skills required to become a successful data scientist. Through a rigorous curriculum developed by the world’s topmost experts in the field, the program covers both the foundations of data sciences, along with applied methods useful in practice. Students will learn how to collect, prepare, store, analyze, and visualize data, all at large scales.

This program follows a set structure with 10 core courses across 31 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles.

MAJOR AREAS EMPLOYING DATA SCIENCE PROFESSIONALS

  • FINANCIAL ANALYTICS- Credit Scoring, Risk based pricing, Fraud Detection and Prediction, Recovery Management, Loss Forecasting, Risk Profiling, Portfolio Stress Testing.
  • MARKETING- Segmentation, Marketing Mix Optimization, Competitor Analysis, Channel Analysis, Sales Performance Analysis, Campaign Analysis, Sales Pipeline Reporting.
  • RETAIL- Customer Analytics, Merchandising Analytics, Store Operations, Inventory Analytics, Market Spend Optimization.
  • CUSTOMER ANALYTICS- Loyalty Analytics, Customer Life Time Value, Propensity Analytics, Churn Analytics, Customer Segmentation, Cross- Sell or Up sell Models.
  • WEB ANALYTICS- Click Analytics, Customer Life cycle Analytics, Social Media Analytics, Sentiment Analytics, Online Traffic Analytics, and Conversion Analytics.
  • HUMAN RESOURCES- Recruitment Analytics, Compensation Analytics, Talent Analytics, Training Analytics, Retention Analytics, Workforce Analytics.

Learning Path

  1. Python Statistics for Data Science Course
  2. R Statistics for Data Science Course
  3. Data Science Certification Course using R
  4. Python Certification Training for Data Science
  5. Apache Spark and Scala Certification Training
  6. AI & Deep Learning with TensorFlow
  7. Tableau Training & Certification
  8. MS SQL Training Essentials
  9. Scala Essentials
  10. Mongo DB Training
  11. Data Science Master Program Capstone Project

Course Curriculum

COURSE 1- Python Statistics for Data Science Course:Understanding the Data
Introduction to Data Types 00:00:00
Numerical parameters to represent data 00:00:00
Mean 00:00:00
Mode 00:00:00
Median 00:00:00
Sensitivity 00:00:00
Information Gain 00:00:00
Entropy 00:00:00
Statistical parameters to represent data 00:00:00
Hands-On/Demo 00:00:00
Probability and its uses
Uses of probability 00:00:00
Bayesian Inference 00:00:00
Density Concepts 00:00:00
Normal Distribution Curve 00:00:00
Hands-On/Demo 00:00:00
Statistical Inference
Point Estimation 00:00:00
Confidence Margin 00:00:00
Hypothesis Testing 00:00:00
Levels of Hypothesis Testing 00:00:00
Hands-On/Demo 00:00:00
Testing the Data
Parametric Test 00:00:00
Parametric Test Types 00:00:00
Non- Parametric Test 00:00:00
Experimental Designing 00:00:00
A/B testing 00:00:00
Hands-On/Demo 00:00:00
Data Clustering
Association and Dependence 00:00:00
Causation and Correlation 00:00:00
Covariance 00:00:00
Simpson’s Paradox 00:00:00
Clustering Techniques 00:00:00
Hands-On/Demo 00:00:00
Regression Modelling
Logistic and Regression Techniques 00:00:00
Problem of Collinearity 00:00:00
WOE and IV 00:00:00
Residual Analysis 00:00:00
Heteroscedasticity 00:00:00
Homoscedasticity 00:00:00
Hands-On/Demo 00:00:00
COURSE 2-R Statistics for Data Science Course:Understanding the Data
Introduction to Data Types 00:00:00
Numerical parameters to represent data 00:00:00
Entropy 00:00:00
Statistical parameters to represent data 00:00:00
Hands-On/Demo 00:00:00
Probability and its Uses
Uses of probability 00:00:00
Need of probability 00:00:00
Bayesian Inference 00:00:00
Density Concepts 00:00:00
Normal Distribution Curve 00:00:00
Hands-On/Demo 00:00:00
Statistical Inference
Point Estimation 00:00:00
Confidence Margin 00:00:00
Hypothesis Testing 00:00:00
Levels of Hypothesis Testing 00:00:00
Hands-On/Demo 00:00:00
Testing the Data
Parametric Test 00:00:00
Parametric Test Types 00:00:00
Non- Parametric Test 00:00:00
A/B testing 00:00:00
Hands-On/Demo 00:00:00
Data Clustering
Association and Dependence 00:00:00
Causation and Correlation 00:00:00
Causation and Correlation 00:00:00
Covariance 00:00:00
Simpson’s Paradox 00:00:00
Clustering Techniques 00:00:00
Hands-On/Demo 00:00:00
Regression Modelling
Logistic and Regression Techniques 00:00:00
Problem of Collinearity 00:00:00
WOE and IV 00:00:00
Residual Analysis 00:00:00
Heteroscedasticity 00:00:00
Homoscedasticity 00:00:00
Hands-On/Demo 00:00:00
COURSE 3-Data Science Certification Course using R:Introduction to Data Science
What is Data Science? 00:00:00
What does Data Science involve? 00:00:00
Era of Data Science 00:00:00
Business Intelligence vs Data Science 00:00:00
Life cycle of Data Science 00:00:00
Tools of Data Science 00:00:00
Introduction to Big Data and Hadoop 00:00:00
Introduction to R 00:00:00
Introduction to Spark 00:00:00
Introduction to Machine Learning 00:00:00
Statistical Inference
What is Statistical Inference? 00:05:00
Terminologies of Statistics 00:00:00
Measures of Centers 00:00:00
Measures of Spread 00:00:00
Probability 00:00:00
Normal Distribution 00:00:00
Binary Distribution 00:00:00
Data Extraction, Wrangling and Exploration
Data Analysis Pipeline 00:00:00
What is Data Extraction 00:00:00
Types of data 00:00:00
Raw and Processed Data 00:00:00
Data Wrangling 00:00:00
Exploratory Data Analysis 00:00:00
Visualization of Data 00:00:00
Hands-On/Demo 00:00:00
Classification Techniques
What are Classification and its use cases? 00:00:00
What is Decision Tree? 00:00:00
Algorithm for Decision Tree Induction 00:00:00
Creating a Perfect Decision Tree 00:00:00
Confusion Matrix 00:00:00
What is Random Forest? 00:00:00
What is Navies Bayes? 00:00:00
Support Vector Machine: Classification 00:00:00
Unsupervised Learning
What is Clustering & its Use Cases? 00:00:00
What is K-means Clustering? 00:00:00
What is C-means Clustering? 00:00:00
What is Canopy Clustering? 00:00:00
What is Hierarchical Clustering? 00:00:00
Hands-On/Demo 00:00:00
Recommender Engines
What is Association Rules & its use cases? 00:00:00
What is Recommendation Engine & it’s working? 00:00:00
Types of Recommendations 00:00:00
User-Based Recommendation 00:00:00
Item-Based Recommendation 00:00:00
Difference: User-Based and Item-Based Recommendation 00:00:00
Recommendation use cases 00:00:00
Hands-On/Demo 00:00:00
Text Mining
The concepts of text-mining 00:00:00
Use cases 00:00:00
Text Mining Algorithms 00:00:00
Quantifying text 00:00:00
TF-IDF 00:00:00
Beyond TF-IDF 00:00:00
Hands-On/Demo 00:00:00
Time Series
What is Time Series data? 00:00:00
Time Series variables 00:00:00
Different components of Time Series data 00:00:00
Visualize the data to identify Time Series Components 00:00:00
Implement ARIMA model for forecasting 00:00:00
Exponential smoothing models 00:00:00
Identifying different time series scenario based on which different Exponential Smoothing model can be applied 00:00:00
Implement respective ETS model for forecasting 00:00:00
Hands-On/Demo 00:00:00
Deep Learning
Reinforced Learning 00:00:00
Reinforcement learning Process Flow 00:00:00
Reinforced Learning Use cases 00:00:00
Deep Learning 00:00:00
Reinforced Learning 00:00:00
Reinforcement learning Process Flow 00:00:00
Reinforced Learning Use cases 00:00:00
Deep Learning 00:00:00
Biological Neural Networks 00:00:00
Understand Artificial Neural Networks 00:00:00
Building an Artificial Neural Network 00:00:00
How ANN works 00:00:00
Important Terminologies of ANN’s 00:00:00
COURSE 4-Python Certification Training for Data Science:Introduction to Python
Overview of Python 00:00:00
The Companies using Python 00:00:00
Different Applications where Python is used 00:00:00
Discuss Python Scripts on UNIX/Windows 00:00:00
Values, Types, Variables 00:00:00
Operands and Expressions 00:00:00
Loops 00:00:00
Command Line Arguments 00:00:00
Writing to the screen 00:00:00
Hands on/Demo 00:00:00
Sequences and File Operations
Python files I/O Functions 00:00:00
Numbers 00:00:00
Strings and related operations 00:00:00
Lists and related operations 00:00:00
Dictionaries and related operations 00:00:00
Sets and related operations 00:00:00
Hands On/Demo 00:00:00
Deep Dive – Functions, OOPs, Modules, Errors and Exceptions
Functions 00:00:00
Function Parameters 00:00:00
Global Variables 00:00:00
Variable Scope and Returning Values 00:00:00
Lambda Functions 00:00:00
Object-Oriented Concepts 00:00:00
Standard Libraries 00:00:00
Modules Used in Python 00:00:00
The Import Statements 00:00:00
Module Search Path 00:00:00
Errors and Exception Handling 00:00:00
Handling Multiple Exceptions 00:00:00
Hands on/Demo 00:00:00
Introduction to NumPy, Pandas and Matplotlib
NumPy – arrays 00:00:00
Operations on arrays 00:00:00
Indexing slicing and iterating 00:00:00
Reading and writing arrays on files 00:00:00
Pandas – data structures & index operations 00:00:00
Reading and Writing data from Excel/CSV formats into Pandas 00:00:00
Grids, axes, plots 00:00:00
Markers, colours, fonts and styling 00:00:00
Types of plots – bar graphs, pie charts, histograms 00:00:00
Contour plots 00:00:00
Hands On/Demo 00:00:00
Data Manipulation
Basic Functionalities of a data object 00:00:00
Merging of Data objects 00:00:00
Concatenation of data objects 00:00:00
Types of Joins on data objects 00:00:00
Exploring a Dataset 00:00:00
Analysing a dataset 00:00:00
Hands on/Demo 00:00:00
Introduction to Machine Learning with Python
Python Revision (numpy, Pandas, scikit learn, matplotlib) 00:00:00
What is Machine Learning? 00:00:00
Machine Learning Use-Cases 00:00:00
Machine Learning Process Flow 00:00:00
Linear regression 00:00:00
Gradient descent 00:00:00
Hands On/Demo 00:00:00
Supervised Learning - I
What are Classification and its use cases? 00:00:00
What is Decision Tree? 00:00:00
Algorithm for Decision Tree Induction 00:00:00
Creating a Perfect Decision Tree 00:00:00
Confusion Matrix 00:00:00
What is Random Forest? 00:00:00
Hands on/Demo 00:00:00
Dimensionality Reduction
Introduction to Dimensionality 00:00:00
Why Dimensionality Reduction 00:00:00
PCA 00:00:00
Factor Analysis 00:00:00
Scaling dimensional model 00:00:00
LDA 00:00:00
Hands on/Demo 00:00:00
Supervised Learning - II
What is Naïve Bayes? 00:00:00
How Naïve Bayes works? 00:00:00
Implementing Naïve Bayes Classifier 00:00:00
What is Support Vector Machine? 00:00:00
Illustrate how Support Vector Machine works? 00:00:00
Hyperparameter Optimization 00:00:00
Grid Search vs Random Search 00:00:00
Implementation of Support Vector Machine for Classification 00:00:00
Hands-On/Demo 00:00:00
Unsupervised Learning
What is Clustering & its Use Cases? 00:00:00
What is K-means Clustering? 00:00:00
How does K-means algorithm work? 00:00:00
How to do optimal clustering 00:00:00
What is C-means Clustering? 00:00:00
What is Hierarchical Clustering? 00:00:00
How Hierarchical Clustering works? 00:00:00
Hands-On/Demo 00:00:00
Association Rules Mining and Recommendation Systems
What are Association Rules? 00:00:00
Association Rule Parameters 00:00:00
Calculating Association Rule Parameters 00:00:00
Recommendation Engines 00:00:00
How does Recommendation Engines work? 00:00:00
Collaborative Filtering 00:00:00
Content-Based Filtering 00:00:00
Hands-On/Demo 00:00:00
Reinforcement Learning
What is Reinforcement Learning 00:00:00
Why Reinforcement Learning 00:00:00
Elements of Reinforcement Learning 00:00:00
Exploration vs Exploitation dilemma 00:00:00
Epsilon Greedy Algorithm 00:00:00
Markov Decision Process (MDP) 00:00:00
Q values and V values 00:00:00
Q – Learning 00:00:00
α values 00:00:00
Hands-On/Demo 00:00:00
Time Series Analysis
What is Time Series Analysis? 00:00:00
Importance of TSA 00:00:00
Components of TSA 00:00:00
White Noise 00:00:00
AR model 00:00:00
MA model 00:00:00
ARMA model 00:00:00
ARIMA model 00:00:00
Stationarity 00:00:00
ACF & PACF 00:00:00
Hands on/Demo 00:00:00
Model Selection and Boosting
What is Model Selection? 00:00:00
The need for Model Selection 00:00:00
Cross-Validation 00:00:00
Cross-Validation 00:00:00
What is Boosting? 00:00:00
How Boosting Algorithms work? 00:00:00
Types of Boosting Algorithms 00:00:00
Adaptive Boosting 00:00:00
Hands on/Demo 00:00:00
COURSE -5 Apache Spark and Scala Certification Training:Introduction to Scala for Apache Spark
What is Scala? 00:00:00
Why Scala for Spark? 00:00:00
Scala in other frameworks 00:00:00
Introduction to Scala REPL 00:00:00
Basic Scala operations 00:00:00
Variable Types in Scala 00:00:00
Control Structures in Scala 00:00:00
Foreach loop, Functions and Procedures 00:00:00
Collections in Scala- Array 00:00:00
ArrayBuffer, Map, Tuples, Lists, and more 00:00:00
OOPS and Functional Programming in Scala
Getters and Setters 00:00:00
Custom Getters and Setters 00:00:00
Properties with only Getters 00:00:00
Auxiliary Constructor and Primary Constructor 00:00:00
Singletons 00:00:00
Extending a Class 00:00:00
Overriding Methods 00:00:00
Traits as Interfaces and Layered Traits 00:00:00
Programming 00:00:00
Higher Order Functions 00:00:00
Anonymous Functions, and more 00:00:00
Introduction to Big Data and Hadoop
What is Big Data? 00:00:00
Big Data Customer Scenarios 00:00:00
Limitations and Solutions of Existing Data Analytics Architecture with Uber Use Case 00:00:00
How Hadoop Solves the Big Data Problem 00:00:00
What is Hadoop? 00:00:00
Hadoop’s Key Characteristics 00:00:00
Hadoop Ecosystem and HDFS 00:00:00
Hadoop Core Components 00:00:00
Rack Awareness and Block Replication 00:00:00
HDFS Read/Write Mechanism 00:00:00
YARN and Its Advantage 00:00:00
Hadoop Cluster and Its Architecture 00:00:00
Hadoop: Different Cluster Modes 00:00:00
Data Loading using Sqoop 00:00:00
Apache Spark Framework
Big Data Analytics with Batch & Real-Time Processing 00:00:00
Why Spark is Needed? 00:00:00
What is Spark? 00:00:00
How Spark Differs from Its Competitors? 00:00:00
Spark at eBay 00:00:00
Spark at eBay 00:00:00
Spark’s Place in Hadoop Ecosystem 00:00:00
Spark Components & it’s Architecture 00:00:00
Running Programs on Scala IDE & Spark Shell 00:00:00
Spark Web UI 00:00:00
Configuring Spark Properties 00:00:00
Playing with RDDs
Challenges in Existing Computing Methods 00:00:00
Probable Solution & How RDD Solves the Problem 00:00:00
What is RDD, It’s Functions, Transformations & Actions? 00:00:00
Data Loading and Saving Through RDDs 00:00:00
Key-Value Pair RDDs and Other Pair RDDs o RDD Lineage 00:00:00
RDD Persistence 00:00:00
WordCount Program Using RDD Concepts 00:00:00
RDD Partitioning & How It Helps Achieve Parallelization 00:00:00
DataFrames and Spark SQL
Need for Spark SQL 00:00:00
What is Spark SQL? 00:00:00
Spark SQL Architecture 00:00:00
SQL Context in Spark SQL 00:00:00
Data Frames & Datasets 00:00:00
Interoperating with RDDs 00:00:00
JSON and Parquet File Formats 00:00:00
Loading Data through Different Sources 00:00:00
Machine Learning using Spark MLlib
What is Machine Learning? 00:00:00
Where is Machine Learning Used? 00:00:00
Different Types of Machine Learning Techniques 00:00:00
Face Detection: USE CASE 00:00:00
Understanding MLlib 00:00:00
Features of Spark MLlib and MLlib Tools 00:00:00
Various ML algorithms supported by Spark MLlib 00:00:00
K-Means Clustering & How It Works with MLlib 00:00:00
Analysis on US Election Data: K-Means Spark MLlib USE CASE 00:00:00
Understanding Apache Kafka and Kafka Cluster
Need for Kafka 00:00:00
What is Kafka? 00:00:00
Core Concepts of Kafka 00:00:00
Kafka Architecture 00:00:00
Where is Kafka Used? 00:00:00
Understanding the Components of Kafka Cluster 00:00:00
Configuring Kafka Cluster 00:00:00
Producer and Consumer 00:00:00
Capturing Data with Apache Flume and Integration with Kafka
Need of Apache Flume 00:00:00
What is Apache Flume 00:00:00
Basic Flume Architecture 00:00:00
Flume Sources 00:00:00
Flume Sinks 00:00:00
Flume Channels 00:00:00
Flume Configuration 00:00:00
Integrating Apache Flume and Apache Kafka 00:00:00
Apache Spark Streaming
Drawbacks in Existing Computing Methods 00:00:00
Why Streaming is Necessary? 00:00:00
What is Spark Streaming? 00:00:00
Spark Streaming Features 00:00:00
Spark Streaming Workflow 00:00:00
How Uber Uses Streaming Data 00:00:00
Streaming Context & DStreams 00:00:00
Transformations on DStreams 00:00:00
WordCount Program using Spark Streaming 00:00:00
Describe Windowed Operators and Why it is Useful 00:00:00
Important Windowed Operators 00:00:00
Slice, Window and ReduceByWindow Operators 00:00:00
Stateful Operators 00:00:00
Perform Twitter Sentimental Analysis Using Spark Streaming 00:00:00
COURSE -6 AI & Deep Learning with TensorFlow:Introduction to Deep Learning
Deep Learning: A revolution in Artificial Intelligence 00:00:00
Limitations of Machine Learning 00:00:00
What is Deep Learning? 00:00:00
Advantage of Deep Learning over Machine learning 00:00:00
3 Reasons to go for Deep Learning 00:00:00
Real-Life use cases of Deep Learning 00:00:00
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfiting and Overfiting, Optimization 00:00:00
Hands-On/Demo 00:00:00
Understanding Neural Networks with TensorFlow
How Deep Learning Works? 00:00:00
Activation Functions 00:00:00
Illustrate Perceptron 00:00:00
Training a Perceptron 00:00:00
Important Parameters of Perceptron 00:00:00
Important Parameters of Perceptron 00:00:00
What is TensorFlow? 00:00:00
TensorFlow code-basics 00:00:00
Graph Visualization 00:00:00
Constants, Placeholders, Variables 00:00:00
Creating a Model 00:00:00
Step by Step – Use-Case Implementation 00:00:00
Deep dive into Neural Networks with TensorFlow
Understand limitations of a Single Perceptron 00:00:00
Understand Neural Networks in Detail 00:00:00
Illustrate Multi-Layer Perceptron 00:00:00
Back propagation – Learning Algorithm 00:00:00
Understand Backpropagation – Using Neural Network Example 00:00:00
MLP Digit-Classifier using TensorFlow 00:00:00
TensorBoard 00:00:00
Master Deep Networks
Why Deep Networks 00:00:00
Why Deep Networks give better accuracy? 00:00:00
Use-Case Implementation on SONAR dataset 00:00:00
Understand How Deep Network Works? 00:00:00
How Backpropagation Works? 00:00:00
Illustrate Forward pass, Backward pass 00:00:00
Different variants of Gradient Descent 00:00:00
Types of Deep Networks 00:00:00
Convolutional Neural Networks (CNN)
Introduction to CNNs 00:00:00
CNNs Application 00:00:00
Architecture of a CNN 00:00:00
Convolution and Pooling layers in a CNN 00:00:00
Understanding and Visualizing a CNN 00:00:00
Hands-On 00:00:00
Recurrent Neural Networks (RNN)
Introduction to RNN Model 00:00:00
Application use cases of RNN 00:00:00
Modelling sequences 00:00:00
Training RNNs with Backpropagation 00:00:00
Long Short-Term memory (LSTM) 00:00:00
Recursive Neural Tensor Network Theory 00:00:00
Recurrent Neural Network Model 00:00:00
Hands-On 00:00:00
Restricted Boltzmann Machine (RBM) and Autoencoders
Restricted Boltzmann Machine 00:00:00
Applications of RBM 00:00:00
Collaborative Filtering with RBM 00:00:00
Introduction to Autoencoders 00:00:00
Autoencoders applications 00:00:00
Understanding Autoencoders 00:00:00
Hands-On 00:00:00
Keras API
Define Keras 00:00:00
How to compose Models in Keras 00:00:00
Sequential Composition 00:00:00
Functional Composition 00:00:00
Predefined Neural Network Layers 00:00:00
What is Batch Normalization 00:00:00
Saving and Loading a model with Keras 00:00:00
Customizing the Training Process 00:00:00
Using TensorBoard with Keras 00:00:00
Use-Case Implementation with Keras 00:00:00
Hands-On 00:00:00
TFLearn API
Define TFLearn 00:00:00
Composing Models in TFLearn 00:00:00
Sequential Composition 00:00:00
Functional Composition 00:00:00
Predefined Neural Network Layers 00:00:00
What is Batch Normalization 00:00:00
Saving and Loading a model with TFLearn 00:00:00
Customizing the Training Process 00:00:00
Using TensorBoard with TFLearn 00:00:00
Use-Case Implementation with TFLearn 00:00:00
Hands-On 00:00:00
In-Class Project
How to approach a project? 00:00:00
Hands-On project implementation 00:00:00
What Industry expects? 00:00:00
Industry insights for the Machine Learning domain 00:00:00
QA and Doubt Clearing Session 00:00:00
COURSE -7 Tableau Training & Certification:Introduction to Data Visualization
Data Visualization 00:00:00
Introducing Tableau 10.0 00:00:00
Establishing Connection 00:00:00
Joins and Union 00:00:00
Data Blending 00:00:00
Hands On 00:00:00
Visual Analytics
Managing Extracts 00:00:00
Managing Metadata 00:00:00
Visual Analytics 00:00:00
Data Granularity using Marks Card 00:00:00
Highlighting 00:00:00
Introduction to basic graphs 00:00:00
Hands On 00:00:00
Visual Analytics in depth I
Sorting. 00:00:00
Filtering. 00:00:00
Grouping 00:00:00
Graphical Visualization 00:00:00
Hands On 00:00:00
Visual Analytics in depth II
Sets 00:00:00
Forecasting 00:00:00
Clustering 00:00:00
Trend Lines. 00:00:00
Reference Lines. 00:00:00
Parameters 00:00:00
Hands On 00:00:00
Dashboard and Stories
Introduction to Dashboard. 00:00:00
Creating a Dashboard Layout. 00:00:00
Designing Dashboard for Devices. 00:00:00
Dashboard Interaction – Using Action. 00:00:00
Introduction to Story Point. 00:00:00
Hands On 00:00:00
Mapping
Introduction to Maps. 00:00:00
Editing Unrecognized Locations. 00:00:00
Custom Geocoding. 00:00:00
Polygon Maps. 00:00:00
Web Mapping Services. 00:00:00
Background Images. 00:00:00
Hands On 00:00:00
Calculation
Introduction to Calculation : Number Functions, String Functions , Date Functions, Logical Functions, Aggregate Functions. 00:00:00
Introduction to Table Calculation. 00:00:00
Introduction to LOD expression : Fixed LOD , Included LOD, Excluded LOD 00:00:00
Hands On 00:00:00
Charts
Box and Whisker’s Plots 00:00:00
Gantt Charts 00:00:00
Waterfall Charts 00:00:00
Pareto Charts 00:00:00
Control Charts 00:00:00
Funnel Charts 00:00:00
Hands On 00:00:00
Integrating Tableau with R and Hadoop
Introduction to Big Data 00:00:00
Introduction to Hadoop 00:00:00
Introduction to R 00:00:00
Integration among R and Hadoop 00:00:00
Calculating measure using R 00:00:00
Integrating Tableau with R 00:00:00
Integrated Visualization using Tableau 00:00:00
Hands On 00:00:00
Data Science Master Program CAPSTONE Project

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