Data Science Masters Program

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Data Science
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Enroll now to become a Certified Data Science expert with EDTIA Data Science Masters Program and upgrade your skills.

Course Description

Data Science Masters Program makes you experienced in tools and systems utilized by Data Science Professionals. It contains training in Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow, and Tableau.

Data Science Masters Program makes you skilled in tools and systems operated by Data Science Professionals. It possesses training in Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow, and Tableau.

A data science master's program helps you acquire skills to collect, manage and analyze data, its types, trends, and deliver the results accordingly. This advanced skill set is spread out throughout the M. Sc.

There are no prerequisites for enrollment in the Data Science Masters Program.

experienced professional working in the IT industry, an aspirant preparing to join the world of Data Science

Today, companies across multiple industries operate and rely on data scientists to develop their businesses. Data scientists are generally responsible for collecting and analyzing raw data, using data to gain insights into business processes to help achieve various goals.

Data scientists study which queries need responding to and where to locate the related data. They have business acumen and analytical skills and can mine, clean, and present data. Businesses utilize data scientists to source, manage, and analyze large amounts of unstructured data.

As companies hope to extract essential insights from big data, the demand for data scientists is on a consistent rise. Reports suggest that India is the second-highest country after the US created the requirement to recruit around 50,000 data scientists in 2020 and 2021.

Data scientists examine which questions need answering and where to discover the corresponding data. They have business acumen and analytical skills and can mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data.

What you'll learn

  • In this course, you will learn: Data Science, Python, Apache Spark & Scala, Tensorflow and Tableau

Requirements

  • There are requirements for learning this course.

Curriculam

Understand data and its types accordingly sample data and derive meaningful information from the data in different statistical parameters.

Introduction to Data Types
Numerical parameters to represent data
Mean,
Mode,
Median,
Sensitivity,
Information Gain,
Entropy,
Statistical parameters to represent data,
Estimating mean, median, and mode using PythonPython,
Calculating Information Gain and Entropy,

learn about probability, interpret & solve real-life problems using chance. You will get to know the power of possibility with Bayesian Inference.

Uses of probability,
Need of probability,
Bayesian Inference,
Density Concepts,
Normal Distribution Curve,
Calculating probability using Python,
Conditional, Joint, and Marginal Probability using PythonPython,
Plotting a Normal distribution curve

Illustrate inferences from present data and construct predictive models utilizing various inferential parameters (as a constraint)

Point Estimation,
Confidence Margin,
Hypothesis Testing,
Levels of Hypothesis Testing,
Calculating and generalizing point estimates using PythonPython,
Analysis of Confidence Intervals and Margin of Error

understand the various methods of testing the alternative hypothesis.

Parametric Test,
Parametric Test Types,
Non- Parametric Test,
Experimental Designing,
A/B testing,
Perform p test and t-tests in PythonPython,
A/B testing in PythonPython

introduction to Clustering as part of this Module which forms the basis for machine learning.

Association and Dependence,
Causation and Correlation,
Covariance,
Simpson's Paradox,
Clustering Techniques,
Correlation and Covariance in PythonPython,
Hierarchical Clustering in PythonPython,
K means Clustering in PythonPython

Discover the roots of Regression Modelling operating statistics.

Logistic and Regression Techniques,
Problem of Collinearity,
WOE and IV,
Residual Analysis,
Heteroscedasticity,
Homoscedasticity,
Perform Linear and Logistic Regression in PythonPython,
Analyze the residuals using PythonPython

learn data and its types and will accordingly sample data and derive meaningful information from the data in terms of different statistical parameters

Introduction to Data Types,
Numerical parameters to represent data,
Mean,
Mode,
Median,
Sensitivity,
Information Gain,
Entropy,
Statistical parameters to represent data,
Estimating mean, median, and mode using R,
Calculating Information Gain and Entropy

learn about probability, interpret & solve real-life problems using chance. You will get to know the power of possibility with Bayesian Inference.

Uses of probability,
Need of probability,
Bayesian Inference,
Density Concepts,
Normal Distribution Curve,
Calculating probability using R,
Conditional, Joint, and Marginal Probability using R,
Plotting a Normal distribution curve

learn to draw inferences from present data and construct predictive models using different inferential parameters (as the constraint).

Point Estimation,
Confidence Margin,
Hypothesis Testing,
Levels of Hypothesis Testing,
Calculating and generalizing point estimates using R,
Analysis of Confidence Intervals and Margin of Error

understand the various methods of testing the alternative hypothesis.

Parametric Test,
Parametric Test Types,
Non- Parametric Test,
A/B testing,
Perform P test and T-tests in R

get an introduction to Clustering, which forms the basis for machine learning.

Association and Dependence,
Causation and Correlation,
Covariance,
Simpson's Paradox,
Clustering Techniques,
Correlation and Covariance in R,
Hierarchical Clustering in R,
K means Clustering in R

know about the roots of Regression Modelling operating statistics.

Logistic and Regression Techniques,
Problem of Collinearity,
WOE and IV,
Residual Analysis,
Heteroscedasticity,
Homoscedasticity,
Perform Linear and Logistic Regression in R,
Analyze the residuals using R,
Calculation of WOE values using R

introduction to Data Science and see how Data Science helps to analyze large and unstructured data with various tools.

What is Data Science?
What does Data Science involve?
The era of Data Science,
Business Intelligence vs. Data Science,
The life cycle of Data Science,
Tools of Data Science,
Introduction to Big Data and Hadoop,
Introduction to R,
Introduction to Spark,
Introduction to Machine Learning

know about various statistical techniques and terminologies utilized in data analysis.

What is Statistical Inference?
Terminologies of Statistics,
Measures of Centers,
Measures of Spread,
Probability,
Normal Distribution,
Binary Distribution

Discuss the various sources available to extract data, organize the data in a structured form, examine the data, and represent the data in a graphical format.

Data Analysis Pipeline,
What is Data Extraction,
Types of Data,
Raw and Processed Data,
Data Wrangling,
Exploratory Data Analysis,
Visualization of Data,
Loading various types of the dataset in R,
Arranging the Data,
Plotting the graphs

introduction to Machine Learning, discuss the different categories of Machine Learning and execute Supervised Learning Algorithms.

What is Machine Learning?
Machine Learning Use-Cases,
Machine Learning Process Flow,
Machine Learning Categories,
Supervised Learning algorithm (Linear Regression and Logistic Regression),
Implementing the Linear Regression model in R,
Implementing the Logistic Regression model in R

learn the Supervised Learning Techniques and implement various techniques, such as Decision Trees, Random Forest Classifier, etc.

What are Classification and its use cases?
What is a Decision Tree?
Algorithm for Decision Tree Induction,
Creating a Perfect Decision Tree,
Confusion Matrix,
What is Random Forest?
What is Naive Bayes?
Support Vector Machine: Classification,
Implementing the Decision Tree model in R,
Implementing Linear Random Forest in R,
Implementing the Naive Bayes model in R,
Implementing Support Vector Machine in R

Know about Unsupervised Learning and the different types of Clustering that can be utilized to analyze the data.

What is Clustering & its use cases,
What is K-means Clustering?
What is C-means Clustering?
What is Canopy Clustering?
What is Hierarchical Clustering?
Implementing K-means Clustering in R,
Implementing C-means Clustering in R,
Implementing Hierarchical Clustering in R

know about association rules and various types of Recommender Engines.

What are Association Rules & their use cases?
Recommendation Engine & Is it working?
Types of Recommendations,
User-Based Recommendation,
Item-Based Recommendation,
Difference: User-Based and Item-Based Recommendation,
Recommendation use cases,
Implementing Association Rules in R,
Building a Recommendation Engine in R

Examine Unsupervised Machine Learning Techniques and the execution of various algorithms, for example, TF-IDF and Cosine Similarity.

The concepts of text-mining,
Use cases,
Text Mining Algorithms,
Quantifying text,
TF-IDF,
Beyond TF-IDF,
Executing the Bag of Words approach in R,
Running Sentiment Analysis on Twitter Data using R

learn about Time Series data, different components of Time Series data, Time Series modeling - Exponential Smoothing models, and ARIMA model for Time Series Forecasting.

What is Time Series Data?
Time Series variables,
Different components of Time Series data,
Visualize the data to determine Time Series Components,
Implement the ARIMA model for Forecasting,
Exponential smoothing models,
Identifying different time series scenarios based on which other Exponential Smoothing models can be applied,
Implement respective ETS models for Forecasting,
Visualizing and formatting Time Series data,
Plotting decomposed Time Series data plot,
Utilizing ARIMA and ETS model for Time Series Forecasting,
Forecasting for the given Period

Intro to Reinforcement Learning and Deep Learning concepts, discuss Artificial Neural networks, the building blocks for Artificial Neural Networks, and a few Artificial Neural Network terminologies.

Reinforced Learning,
Reinforcement learning Process Flow,
Reinforced Learning Use cases,
Deep Learning,
Biological Neural Networks,
Understand Artificial Neural Networks,
Building an Artificial Neural Network,
How ANN works,
Important Terminologies of ANN's

get a brief idea of it and learn the basics.

Overview,
The Companies using PythonPython,
Different Applications where it is used,
Discuss Python Scripts on UNIX/Windows,
Values, Types, Variables,
Operands and Expressions,
Conditional Statements,
Loops,
Command Line Arguments,
Writing to the screen,
Creating "Hello World" code,
Variables,
Demonstrating Conditional Statements,
Demonstrating Loops
Skills: Fundamentals of Python programming

understand various types of sequence structures, related operations, and their usage. Understand diverse ways of opening, reading, and writing to files.

Python files I/O Functions,
Numbers,
Strings and related operations,
Tuples and related operations,
Lists and related operations,
Dictionaries and related operations,
Sets and related operations,
Tuple - properties, associated processes, compared with a list,
List - properties, related operations,
Dictionary - properties, related operations,
Set - properties, related operations
Skills: File Operations using PythonPython, Working with data types of Python

learn how to create generic scripts, address errors/exceptions in code, and finally, how to extract/filter content using Regex.

Functions,
Function Parameters,
Global Variables,
Variable Scope and Returning Values,
Lambda Functions,
Object-Oriented Concepts,
Standard Libraries,
Modules Used in Python,
The Import Statements,
Module Search Path,
Package Installation Ways,
Errors and Exception Handling,
Handling Multiple Exceptions,
Functions (Syntax, Arguments, Keyword Arguments, Return Values),
Lambda ( Features, Syntax, Options, Compared with the Functions),
Sorting (Sequences, Dictionaries, Limitations of Sorting),
Errors and Exceptions (Types of Issues, Remediation),
Packages and Module (Modules, Import Options, sys Path)
Skills: Error and Exception management in PythonPython, Working with functions in Python

understand the basics of statistics, different measures and probability distributions, and the supporting libraries in these operations.

NumPy - arrays,
Operations on arrays,
Indexing, slicing and iterating,
Reading and writing arrays on files,
Pandas - data structures & index operations,
Reading and Reporting data from Excel/CSV formats into Pandas,
matplotlib library,
Grids, axes, plots,
Markers, colors, fonts, and styling,
Types of plots ( bar graphs, pie charts, histograms),
Contour plots,
NumPy library (making NumPy array, operations performed on NumPy array),
Pandas library(developing series and data frames, Importing and exporting Data),
Matplotlib (operating Scatterplot, histogram, bar graph, a pie chart to show information, Styling of Plot)
Skills: Probability Distributions in Python, Python for Data Visualization

understand in detail about Data Manipulation.

Basic Functionalities of a data object,
Merging of Data objects,
Concatenation of data objects,
Types of Joins on data objects,
Exploring a Dataset,
Analysing a dataset,
Pandas Function - Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples(),
GroupBy operations ,
Aggregation ,
Concatenation ,
Merging ,
Joining,
Skills: Python in Data Manipulation

Know the concept of Machine Learning and its types.

Revision (numpy, Pandas, scikit learn, matplotlib),
What is Machine Learning?
Machine Learning Use-Cases,
Machine Learning Process Flow,
Machine Learning Categories,
Linear regression,
Gradient descent,
Linear Regression – Boston Dataset
Skills: Machine Learning concepts, Machine Learning types, Linear Regression Implementation

Understand Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest classifiers, etc.

What are Classification and its use cases?
What is a Decision Tree?
Algorithm for Decision Tree Induction,
Creating a Perfect Decision Tree,
Confusion Matrix,
What is Random Forest?
Implementation of Logistic regression,
Decision tree, Random forest
Skills: Supervised Learning concepts, Implementing different types of Supervised Learning algorithms, Evaluating model output

learn about the impact of dimensions within data, perform factor analysis using PCA and compress sizes, developing an LDA model.

Introduction to Dimensionality,
Why Dimensionality Reduction,
PCA,
Factor Analysis,
Scaling dimensional model,
LDA,
PCA,
Scaling
Skills: Implementing Dimensionality Reduction Technique

Understand Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest classifiers, etc.

What is Naïve Bayes?
How Naïve Bayes works?
Implementing Naïve Bayes Classifier,
What is a Support Vector Machine?
Illustrate how Support Vector Machine works?
Hyperparameter Optimization,
Grid Search vs. Random Search,
Implementation of Support Vector Machine for Classification,
Implementation of Naïve Bayes, SVM
Skills: Supervised Learning concepts, Implementing different types of Supervised Learning algorithms, Evaluating model output

Know about Unsupervised Learning and the various types of Clustering that can be utilized to analyze the data.

What is Clustering & its Use Cases?
What is K-means Clustering?
How does the K-means algorithm work?
How to do optimal Clustering,
What is C-means Clustering?
What is Hierarchical Clustering?
How does Hierarchical Clustering work?
Implementing K-means Clustering,
Implementing Hierarchical Clustering,
Skills: Unsupervised Learning, Implementation of Clustering – various types

know Association rules and their extension towards recommendation engines with the Apriori algorithm.

What are Association Rules?
Association Rule Parameters,
Calculating Association Rule Parameters
Recommendation Engines,
How do Recommendation Engines work?
Collaborative Filtering,
Content-Based Filtering,
Apriori Algorithm,
Market Basket Analysis
Skills: Data Mining using PythonPython, Recommender Systems using PythonPython

learn about developing an intelligent learning algorithm such that the Learning becomes more and more accurate as time passes by.

What is Reinforcement Learning,
Why Reinforcement Learning,
Elements of Reinforcement Learning,
Exploration vs. Exploitation dilemma,
Epsilon Greedy Algorithm,
Markov Decision Process (MDP),
Q values and V values,
Q – Learning,
α values,
Calculating Reward,
Discounted Reward,
Calculating Optimal quantities,
Implementing Q Learning,
Setting up an Optimal Action,
Skills: Implement Reinforcement Learning using PythonPython, Developing Q Learning model in PythonPython

Know about Time Series Analysis to forecast dependent variables based on time, different models for time series modeling such that you analyze accurate time-dependent data for Forecasting.

What is Time Series Analysis?
Importance of TSA,
Components of TSA,
White Noise,
AR model,
MA model,
ARMA model,
ARIMA model,
Stationarity,
ACF & PACF,
Checking Stationarity,
Converting a non-stationary data to stationary,
Implementing Dickey-Fuller Test,
Plot ACF and PACF,
Generating the ARIMA plot,
TSA Forecasting
Skills: TSA in Python

learn about selecting one model over another and how to convert weaker algorithms into stronger ones.

What is Model Selection?
The need for Model Selection, C
ross-Validation,
What is Boosting?
How do Boosting Algorithms work?
Types of Boosting Algorithms,
Adaptive Boosting,
Cross-Validation,
AdaBoost,
Skills: Model Selection, Boosting algorithm using Python

Understand Big Data and its components, such as HDFS. Learn about the Hadoop Cluster Architecture, Introduction to Spark, and the distinction between batch processing and real-time processing.

What is Big Data?
Big Data Customer Scenarios,
Limitations and Resolutions of Existing Data Analytics Architecture with Uber Use Case
How does Hadoop Solve the Big Data Problem?
What is Hadoop? Preview,
Hadoop's Key Characteristics,
Hadoop Ecosystem and HDFS,
Hadoop Core Components,
Rack Awareness and Block Replication,
YARN and its Advantage,
Hadoop Cluster and its Architecture,
Hadoop: Different Cluster Modes,
Hadoop Terminal Commands ,
Big Data Analytics with Batch and Real-time Processing,
Why is Spark needed?
What is Spark?
How does Spark differ from other frameworks?
Spark at Yahoo!

Understand the basics of Scala that are needed for programming Spark applications.

What is Scala? Preview,
Why Scala for Spark?
Scala in other Frameworks,
Introduction to Scala REPL,
Basic Scala Operations,
Variable Types in Scala,
Control Structures in Scala ,
Foreach loop, Functions, and Procedures,
Collections in Scala- Array,
ArrayBuffer, Map, Tuples, Lists, and more,
Scala REPL Detailed Demo

Know about object-oriented programming and functional programming techniques in Scala.

Functional Programming,
Higher-Order Functions,
Anonymous Functions,
Class in Scala Preview,
Getters and Setters,
Custom Getters and Setters,
Properties with only Getters,
Auxiliary Constructor and Primary Constructor,
Singletons,
Extending a Class Preview,
Overriding Methods,
Traits as Interfaces and Layered Traits,
OOPs Concepts,
Functional Programming

and learn how to develop Spark applications and perform data ingestion using Sqoop.

Spark's Place in Hadoop Ecosystem,
Spark Components & its Architecture Preview,
Spark Deployment Modes,
Introduction to Spark Shell,
Writing your first Spark Job Using SBT,
Submitting Spark Job,
Spark Web UI,
Data Ingestion using Sqoop Preview,
Building and Running Spark Application,
Spark Application Web UI,
Configuring Spark Properties,
Data ingestion using Sqoop

Get an insight into Spark - RDDs and other RDD-related manipulations for implementing business logic (Transformations, Actions, and Functions performed on RDD).

Challenges in Existing Computing Methods
Probable Resolution & How RDD Solves the Issue
What is RDD, Its Operations, Transformations & Actions Preview
Data Loading and Saving Through RDDs Preview
Key-Value Pair RDDs
Other Pair RDDs, Two Pair RDDs
RDD Lineage
RDD Persistence
WordCount Program Using RDD Concepts
RDD Partitioning & How It Assists Attain Parallelization
Passing Functions to Spark
Loading data in RDDs
Saving data through RDDs
RDD Transformations
RDD Actions and Functions
RDD Partitions
WordCount through RDDs

learn about SparkSQL, which is used to process structured data with SQL queries, data-frames and datasets in Spark SQL, and different kinds of SQL operations performed on the data-frames.

Need for Spark SQL,
What is Spark SQL?
Spark SQL Architecture,
SQL Context in Spark SQL,
User-Defined Functions,
Data Frames & Datasets,
Interoperating with RDDs,
JSON and Parquet File Formats,
Loading Data through Different Sources,
Spark – Hive Integration,
Spark SQL – Creating Data Frames,
Loading and Transforming Data through Different Sources,
Stock Market Analysis,
Spark-Hive Integration

Discover why machine learning is required, various Machine Learning techniques/algorithms, and Spark MLlib.

Why Machine Learning?
What is Machine Learning?
Where is Machine Learning Used?
Face Detection: USE CASE,
Different Types of Machine Learning Techniques,
Introduction to MLlib,
Features of MLlib and MLlib Tools,
Various ML algorithms supported by MLlib

Implement various algorithms supported by MLlib, such as Linear Regression, Decision Tree, Random Forest, etc.

Supervised Learning ( Linear Regression, Logistic Regression, Decision Tree, Random Forest Preview)
Unsupervised Learning ( K-Means Clustering & How It Works with MLlib Preview)
Analysis of US Election Data operating MLlib (K-Means)
Machine Learning MLlib,
K- Means Clustering,
Linear Regression,
Logistic Regression,
Decision Tree,
Random Forest

Learn about Kafka Cluster and how to configure different types of Kafka Cluster. Get introduced to Apache Flume, its architecture, and its integration with Apache Kafka for event processing.

Need for Kafka,
What is Kafka? Preview,
Core Concepts of Kafka,
Kafka Architecture,
Where is Kafka Used?
Understanding the Components of the Kafka Cluster,
Configuring Kafka Cluster,
Kafka Producer and Consumer Java API,
Need for Apache Flume,
What is Apache Flume?
Basic Flume Architecture,
Flume Sources,
Flume Sinks,
Flume Channels,
Flume Configuration,
Integrating Apache Flume and Apache Kafka,
Configuring Single Node Single Broker Cluster,
Configuring Single Node Multi Broker Cluster,
Producing and consuming messages,
Flume Commands,
Setting up Flume Agent,
Streaming Twitter Data into HDFS

know about the various streaming data sources such as Kafka and flume. Create a spark streaming application.

Apache Spark Streaming: Data Sources
Streaming Data Source Overview Preview
Apache Flume and Apache Kafka Data Sources
Example: Using a Kafka Direct Data Source
Perform Twitter Sentimental Analysis Using Spark Streaming
Different Streaming Data Sources

understand the concepts of Deep Learning and know how it varies from machine learning.

What is Deep Learning?
Curse of Dimensionality,
Machine Learning vs. Deep Learning,
Use cases of Deep Learning,
Human Brain vs. Neural Network,
What is Perceptron?
Learning Rate,
Epoch,
Batch Size,
Activation Function,
Single Layer Perceptron

you should be able to get yourself introduced to TensorFlow 2. x. You will install and validate TensorFlow 2. x by creating a Simple Neural Network to indicate handwritten digits and operating Multi-Layer Perceptron to improvise the model's accuracy.

Introduction to TensorFlow 2. x,
Installing TensorFlow 2. x,
Defining Sequence model layers,
Activation Function,
Layer Types,
Model Compilation,
Model Optimizer,
Model Loss Function,
Model Training,
Digit Classification utilizing Simple Neural Network in TensorFlow 2. x,
Improving the model,
Adding Hidden Layer,
Adding Dropout,
Using Adam Optimizer,

comprehend how and why CNN came into existence after MLP and learn about Convolutional Neural Network (CNN) by studying the theory behind how CNN is utilized to predict 'X' or 'O'. Use CNN VGG-16 utilizing TensorFlow 2 and predict whether the given image is of a 'cat' or a 'dog' and save and load a model's weight.

Image Classification Example,
What is Convolution,
Convolutional Layer Network,
Convolutional Layer,
Filtering,
ReLU Layer,
Pooling,
Data Flattening,
Fully Connected Layer,
Predicting a cat or a dog,
Saving and Loading a Model,
Face Detection using OpenCV

understand the concept and working of RCNN and figure out why it was developed in the first place.

Regional-CNN,
Selective Search Algorithm,
Bounding Box Regression,
SVM in RCNN,
Pre-trained Model,
Model Accuracy ,
Model Inference Time ,
Model Size Comparison,
Transfer Learning,
Object Detection – Evaluation,
mAP,
IoU,
RCNN – Speed Bottleneck,
Fast R-CNN,
RoI Pooling,
Fast R-CNN – Speed Bottleneck,
Faster R-CNN,
Feature Pyramid Network (FPN),
Regional Proposal Network (RPN),
Mask R-CNN

understand what a Boltzmann Machine is and its implementation. You will also learn what an Autoencoder is, its various types, and how it works.

What is Boltzmann Machine (BM)?
Identify the issues with BM,
Why did RBM come into the picture?
Step by step implementation of RBM,
Distribution of Boltzmann Machine,
Understanding Autoencoders,
Architecture of Autoencoders,
Brief on types of Autoencoders,
Applications of Autoencoders

understand the generative adversarial model and how it works by implementing a step-by-step Generative Adversarial Network.

Which Face is Fake?
Understanding GAN,
What is Generative Adversarial Network?
How does GAN work?
Step by step Generative Adversarial Network implementation,
Types of GAN,
Recent Advances: GAN

classify each emotion shown in the facial expression into various categories by developing a CNN model to recognize the images' facial expressions and predict the uploaded image's facial expressions. During the project implementation, utilize OpenCV and Haar Cascade File to check the emotion.

Where do we use Emotion and Gender Detection?
How does it work?
Emotion Detection architecture
Face/Emotion detection using Haar Cascades
Implementation on Colab

Understand to differentiate between Feed Forward Network and Recurrent neural network (RNN) and comprehend how RNN works. Understand and learn about GRU and execute Sentiment Analysis using RNN and GRU.

Issues with Feed Forward Network,
Recurrent Neural Network (RNN),
Architecture of RNN,
Calculation in RNN,
Backpropagation and Loss calculation,
Applications of RNN,
Vanishing Gradient,
Exploding Gradient,
What is GRU?
Components of GRU,
Update gate,
Reset gate,
Current memory content,
Final memory at current time step

comprehend the architecture of LSTM and the significance of gates in LSTM. Distinguish between sequence-based models and increase the model's efficiency utilizing BPTT.

What is LSTM?
Structure of LSTM,
Forget Gate,
Input Gate,
Output Gate,
LSTM architecture,
Types of Sequence-Based Model,
Sequence Prediction,
Sequence Classification,
Sequence Generation,
Types of LSTM,
Vanilla LSTM,
Stacked LSTM,
CNN LSTM,
Bidirectional LSTM,
How to increase the efficiency of the model?
Backpropagation through time,
Workflow of BPTT

Know to implement Auto Image is captioning utilizing pre-trained model Inception V3 and LSTM for text processing.

Auto Image Captioning,
COCO dataset,
Pre-trained model,
Inception V3 model,
The architecture of Inception V3,
Modify the last layer of a pre-trained model,
Freeze model,
CNN for image processing,
LSTM or text processing

Get a brief idea of the Data Visualization and Tableau Prep Builder tool.

Data Visualization,
Business Intelligence tools,
Introduction to Tableau,
Tableau Architecture,
Tableau Server Architecture,
VizQL,
Introduction to Tableau Prep,
Tableau Prep Builder User Interface,
Data Preparation techniques utilizing Tableau Prep Builder tool,
Create a simple data flow utilizing the Tableau Prep Builder tool,
Group and Replace feature using Tableau Prep Builder tool,
Pivoting data using the Tableau Prep Builder tool,
Aggregate data utilizing the Tableau Prep Builder tool,
Perform Unions and Joins utilizing the Tableau Prep Builder tool

get a brief idea of Tableau UI components and various ways to establish a data connection.

Features of Tableau Desktop,
Connect to data from File and Database,
Types of Connections,
Joins and Unions,
Data Blending,
Tableau Desktop User Interface,
Basic project( Make a workbook and publish it on Tableau Online),
Joins using Tableau Desktop,
Data Blending feature within Tableau,
Create a Workbook and post it over Tableau Online,
Save a workbook in different formats

comprehend the significance of Visual Analytics and explore the different charts, features, and techniques used for Visualization.

Visual Analytics,
Basic Charts (Bar Chart, Line Chart, and Pie Chart),
Hierarchies,
Data Granularity,
Highlighting,
Sorting,
Filtering,
Grouping,
Sets,
Basic Charts in Tableau,
Display Hierarchies, Data Granularity, and Highlighting features in Tableau,
Perform Sorting, Filtering, and Grouping techniques in Tableau,
Sets in Tableau

Comprehend basic calculations such as Numeric, String Manipulation, Date Function, Logical and Aggregate.

Types of Calculations,
Built-in Functions - Number, String, Date, Logical, and Aggregate,
Operators and Syntax Conventions,
Table Calculations,
Level Of Detail (LOD) Calculations,
Using R within Tableau for Calculations,
Demonstrate calculations using Built-in Functions in Tableau,
Conduct Quick Table and Level Of Detail (LOD) calculations in Tableau,
Installing R and establishing a connection with R within Tableau

Learn Visual Analytics in a more granular manner. It covers different advanced techniques for analyzing data, including Forecasting, Trend Lines, Reference Lines, Clustering, and Parameterized concepts.

Parameters,
Tooltips,
Trend lines,
Reference lines,
Forecasting,
Clustering,
Demonstrate Parameters in Calculations,
Perform Data Visualization utilizing Trend lines, Forecasting, and Clustering feature in Tableau,
Project 1- Domain: Media & Entertainment Industry

Know advanced analytical scenarios using Level Of Detail expressions.

Case 1 - Count Customers by Order,
Case 2 - Profit per Business Day,
Case 3 - Comparative Sales,
Case 4- Profit Vs. Target,
Case 5- Discovering the second-order date,
Case 6- Cohort Analysis,
All the use cases are Hands-on intensive

you will gain an understanding of Geographic Visualization in Tableau.

Introduction to Geographic Visualizations,
Manually assigning Geographical Locations,
Types of Maps,
Spatial Files,
Custom Geocoding,
Polygon Maps,
Web Map Services,
Background Images,
Make a Map and assign Geographic locations to the fields,
Illustrate how to make a Map from a Spatial file,
Learn how to create a Filled Map, Symbol Map, and a Density Map,
Perform Custom Geocoding in Maps,
Build a Polygon Map,
Establish a connection with the WMS Server

learn to plot various advanced graphs in Tableau Desktop.

Box and Whisker's Plot,
Bullet Chart,
Bar in Bar Chart,
Gantt Chart,
Waterfall Chart,
Pareto Chart,
Control Chart,
Funnel Chart,
Bump Chart,
Step and Jump Lines,
Word Cloud,
Doughnut Chart,
All the above charts have Hands-on

learn to build Dashboards and Stories within Tableau.

Introduction to Dashboards,
The Dashboard Interface,
Dashboard Objects,
Building a Dashboard,
Dashboard Layouts and Formatting,
Interactive Dashboards with actions,
Designing Dashboards for devices,
Story Points,
Explain how to add objects to a Dashboard,
Create a simple Dashboard (utilizing Layouts and Formatting features),
Create Interactive Dashboards using actions,
Understand to build a Dashboard for devices utilizing Device Designer,
Build Stories with Dashboards,
In-class Project 2- Domain: Retail Industry

discover effective ways of creating Dashboards with minimum time investment.

Tableau Tips and Tricks,
Choosing the correct type of Chart,
Format Style,
Data Visualization best practices,
Prepare for Tableau Interview,
Hands-on experience with various tips and tricks with Tableau,
In-class Industry Grade Major Project-Domain: Transportation Industry

discover to publish data, interact, modify, and secure the published data on Tableau Online.

Publishing Workbooks to Tableau Online,
Interacting with Content on Tableau Online,
Data Management through Tableau Catalog,
AI-Powered features in Tableau Online (Ask and Explain Data),
Understand Scheduling,
Managing Permissions on Tableau Online,
Data Security with Filters in Tableau Online,
Publishing Workbooks to Tableau Online,
Interacting with Content on Tableau Online,
Governing permissions on Tableau Online,
Data security using User-based and Row-level filters

know to create Tableau reports for different industrial scenarios and broadcast them on Tableau Online.

Project Statement: You are recruited as a freelancer for a Retail store that supplies Furniture, Office Supplies, and Technology products to customers all over Europe. You have been asked to build interactive dashboards that can achieve insights into the earnings for orders over the years.

FAQ

The average salary for a data analyst is $100,500 per year

One of the significant benefits of working with a data science company is its proven ability to help your business make informed decisions based on structured predictive data analysis.

To better understand the Data Science Masters Program Certification Training, one must learn as per the curriculum.

Like any other field, with proper guidance Data Science can become an easy field to learn about, and one can build a career in the area. However, as it is vast, it is easy for a beginner to get lost and lose sight, making the learning experience challenging and frustrating.

Data Science helps companies efficiently comprehend gigantic data from numerous sources and derive valuable insights to make smarter data-driven decisions. Data Science is widely used in various industry domains, including marketing, healthcare, finance, banking, policy work, etc.

You need to know various programming languages, such as Python, Perl, C/C++, SQL, and Java, with PythonPython being the most common coding language required in data science roles.

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Training Course Features

Assessments
Assessments

Every certification training session is followed by a quiz to assess your course learning.

Mock Tests
Mock Tests

The Mock Tests Are Arranged To Help You Prepare For The Certification Examination.

Lifetime Access
Lifetime Access

A lifetime access to LMS is provided where presentations, quizzes, installation guides & class recordings are available.

24x7 Expert Support
24x7 Expert Support

A 24x7 online support team is available to resolve all your technical queries, through a ticket-based tracking system.

Forum
Forum

For our learners, we have a community forum that further facilitates learning through peer interaction and knowledge sharing.

Certification
Certification

Successfully complete your final course project and Edtia will provide you with a completion certification.

Data Science Masters Program

By enrolling in the Data Science Masters Program Training and completing the Module, you can get the Edtia Data Science Masters Program Certification.

The recommended duration to complete this program is 34 weeks. However, it is up to the individual to complete this program at their own pace.

Yes, We will be providing you with a certificate of completion for every course part of the learning pathway once you have successfully submitted the final assessment and our subject matter experts have verified it.

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