Data Science Architect Master’s Certification Training

Master Data Science and Machine Learning skills with this industry-oriented training program designed by experts after extensive research.

4

Courses

20

Projects

152

Hours

Duration
4 Months

Start Date
29th Aug, 2021

Format
Online

EMI Options
No interest EMI

Program Overviews

Key Highlights

  • 180+ Hours of interactive learning
  • 140+ Hours of exercise and project work
  • 35+ Projects, hands-on, and case studies
  • Attend as many batches for lifetime
  • Lifetime access to LMS
  • 24/7 Technical Support
  • Resume Building
  • Placement Assistance
Talk To Us  
IND : +91-8003428234

US : +1- 833-777-233 Toll Free 
Top skills you will learn

Python, R, Tableau, SAS, TensorFlow, Keras, Data Science, Machine Learning Algorithm, Time Series Analysis, Natural Language Processing, Deep Network, more.

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Why should you take up this course?

The landscape of Data Science is projected to double its size by the year of 2025 (in 2019 it was 3.03 billion.) Indian government has decided to invest Rs 3,063 crore or $477 Million in the field of AI, ML, and 3-D printing. The salaries of Data Scientists in India and in the US are 8 lacs/yr. and $124k/yr. respectively. The salaries of AI Engineers in India and in the US are 7.5 lacs/yr. and $111k/yr. respectively. So, this is the perfect time to get a Master’s certification on the same.

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Who should take up this course?

Anyone willing to build a cloud computing career in Data Science.

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What are the prerequisites for this course?

No prerequisite. We teach everything from scratch.

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Hiring Companies

Program Syllabus

Course 1: Data Science Certification Training

36
WEEK

10
Modules

36
Hours

8
Skills

Watch Course Recording

• SAS Vs R Vs Python
• Business objectives
• Key driving factors in the analytics world
• R studio
• Graphical User Interfaces (GUI)
• Installing R studio
• Install essential packages along with different GUIs
• Data Structure
• Data Types
• Vectors
• Matrices
• Factors
• Data frames
• Lists
• Importing Data
• Connecting to database
• Exporting Data
• Viewing partial data and full data
• Variable & Value Labels: Date Values
• Variables to perform calculations & binning
• Operators and using multiple operators
• Built-in Functions & User-Defined Functions
• Control Structures
• Conditional statements
• Loops
• Functions
• Sorting, Merging and Appending Data
• Aggregating/summarizing Data
• Reshaping & Subsetting Data
• Data Type Conversions
• Sampling
• File preparation
• Aggregation
• Merging
• Appending
• Type conversion
• Renaming and formatting data
• Handling Duplicates/Missing values
• Creating Interactive Graphs on R using packages like
• GGPLOT, GGPLOT2, and PLOTLY
• Working with Histograms & Density Plot
• Dot Plots
• Bar Plots
• Line Charts
• Pie Charts
• Boxplots
• Scatterplots
• Exploratory Data Analysis (EDA)
• Understanding the spread and data points
• Understanding the sourced data for better analysis
• Understanding more about Analytics World
• Data Science Vs Data Analytics Vs ML Vs AI Vs Business Analysis
• Analytics keywords and their definitions
• Business Objectives
• Key driving factors in the Analytics world
• Types of Business problems
• Mapping of Techniques
• Different Phases of Predictive Modeling
• EDA - Exploratory Data Analysis and Need of Data preparation
• Data Preparation
• Performing Data Preparation steps
• Consolidation/aggregation
• Outlier treatment
• Flatliners
• Missing values
• Dummy Creation
• Variable Reduction
• Data Alignment and fine-tuning
• Cluster and Segmentation in R
• Working with various Behavioral Segmentation Techniques
• K-Means Cluster Analysis in R
• Heuristic Segmentation Techniques
• Value-Based, RFM Segmentation
• Life Stage Segmentation
• Implementing Decision Tree model in R
• Understanding steps to perform the Classification based on inferences on Decision Tree
• Extensive standard R Packages and Functions
• Assumptions of Linear Regression & Logistic Regression
• Linear & Logistic Regression
• Use of Linear & Logistic Regression Model
• What is Time Series Data?
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Understanding differences in Statistical learning vs. Machine learning
• Understanding essential classes of Machine Learning Algorithms: Supervised vs Unsupervised Learning
• Text Mining and Sentiment Analysis in R

Course 2: Python for Data Science Certification Training

36
WEEK

10
Modules

36
Hours

8
Skills

Watch Course Recording

• Introduction to Python
• Features of Python
• Advantages of using Python
• Companies using Python
• Installation process of Python
• Basic commands of Python
• Python Data Types
• Numbers
• Simple arithmetic operations in Python
• Assigning Variables in Python
• Operators in Python
• Strings
• Indexing, slicing, and formatting
• Lists
• Tuples
• Sets
• Boolean
• Dictionaries
• Python statements
• If Elif and Else Statements
• For loop
• While loop
• Range vs xrange in Python
• List Comprehensions in Python
• Chaining comparison in python
• Else with for and Switch Case in Python
• Using iteration in python
• Iterators in Python
• Iterators function
• Python functions and its types
• Defining a Function in Python
• Rules for naming Python function (identifier)
• Python Function Parameters
• Python Return Statement and calling a function
• Function arguments
• Python function argument and its types
• Default argument in Python
• Python keyword arguments
• Python arbitrary arguments
• Python built-In functions with syntax and examples
• Lambda expressions, map, and filter Functions
• OOP concept
• Attributes
• Class Keywords
• Class Object Attributes
• Methods in Python
• Data Hiding and Object Printing
• Constructors and Destructors in Python
• Class and static variable in python
• Class method and static method in python
• Inheritance, Encapsulation, Polymorphism & Abstraction
• Special Methods - Magic Method
• Modules
• Installing external packages and modules
• Working oPn yPi using pip Install
• Numeric, Logarithmic, Power, Trigonometric and Angular functions
• Python Errors and Exceptions
• Syntax Errors in Python
• Handling Exceptions in Python
• Raising Exceptions
• User-defined Exceptions
• Unit Testing in Python
• Decorators in Python
• Syntax of Decorators and Working with them
• Generators in Python
• Working with Generators
• NumPy
• Creating Arrays in NumPy
• Using Arrays and Scalars
• Indexing NumPy Arrays
• NumPy Array Manipulation
• Array Transportation
• Universal Array Function
• Array Processing
• Array Input and Output
• SciPy
• Clusters, Linning, Signals, Optimization, Integration, Sub packages
• Bayesian Theory
• Data manipulation
• Pandas libraries
• Dependency of NumPy libraries
• Pandas Series objects
• Pandas data frames
• Load and process data with Pandas
• Combining data objects
• Merging, and various types of data object attachments
• Record & clean notes, edit notes, visualize notes
• Matplotlib
• Seaborn
• Pandas Built-in Data Visualization
• Plotly and Cufflinks
• Geographical Plotting
• Web scraping in Python
• Web scraping libraries
• Beautifulsoup and Scrapy
• Installation of beautifulsoup
• Installation of Python parser lxml
• Creating soup object with input HTML
• Searching of tree
• Full or partial parsing
• Output print
• Searching the tree
• Introduction to Machine Learning
• Understanding SciKit Learn
• Need of Machine Learning
• Types in Machine Learning
• Machine Learning Workflow
• Understanding SciKit Learn!
• Machine Learning Use-Cases
• Machine Learning Algorithms
• Supervised Learning
• Unsupervised Learning
• Supervised learning
• Classification and Regression Algorithms
• Linear regression and how to do calculations in Linear Regression?
• Understanding Linear regression in Python
• Understanding Logistics regression
• Working with Supports vector machine
• xgboost (standalone step)
• Unsupervised Learning
• Use Cases of Unsupervised Learning Understanding Clustering,
• Types of Clustering - Exclusive Clustering, Overlapping Clustering, Hierarchical Clustering
• Understanding K-Means Clustering and its algorithm
• Stepwise calculation of k-means algorithm
• Running k-means with SciKit Library
• Understanding association mining rule
• Market basket analysis
• Association rule mining and Apriori Algorithm

Course 3: Artificial Intelligence and Deep Learning Certification Training

36
WEEK

10
Modules

36
Hours

8
Skills

Watch Course Recording

• Basics of Python
• OOPs Concept in Python
• Introduction to NumPy
• Introduction to Pandas
• Data Pre-processing
• Data Manipulation
• Data Visualization
• Fundamentals of Statistics
• Generalized Linear Models
• Regression and Clustering
• What is Machine Learning?
• Supervised Learning – Regression
• Supervised Learning – Classification
• Model Selection and Boosting
• Unsupervised Learning
• Dimensionality Reduction
• Association Rules Mining and Recommendation
• What is a Time Series?
• Time Series Analysis techniques and applications
• Components of Time Series
• Moving average
• Smoothing techniques
• Exponential smoothing
• Univariate time series models
• Multivariate time series analysis
• Arima model
• Time Series in Python
• Understanding graphical model
• Bayesian Network
• Inference
• Model learning
• Module 6: Introduction to Reinforcement Learning
• Getting started with Reinforcement Learning
• Bandit Algorithms and Markov Decision Process
• Dynamic Programming and Temporal Difference Learning methods
• What is Deep Q Learning?
• Text Preprocessing and Natural Language Processing
• Analyzing Sentence Structure
• Text Classification
• Sentiment Analysis
• What is Deep Learning?
• Why Deep Learning?
• Advantage of Deep Learning over Machine learning
• 3 Reasons to go for Deep Learning
• Real-Life use cases of Deep Learning
• How Deep Learning Works?
• Activation Functions
• Illustrate Perceptron
• Train a Perceptron
• Parameters of Perceptron
• TensorFlow
• Graph Visualization
• Constants, placeholders, and variables
• Create a Model
• Understand limitations of a Single Perceptron
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• What is a backpropagation?
• Getting started with TensorBoard
• What is Deep Network?
• Why Deep Networks?
• Understand How Deep Network Works?
• How Backpropagation Works?
• Illustrate Forward pass, Backward pass
• Different variants of Gradient Descent
• Types of Deep Networks
• What is CNN?
• Application of CNN
• Architecture of a CNN
• Convolution and Pooling layers in a CNN
• Application use cases of RNN
• Modelling sequences
• Training RNNs with Backpropagation
• Long Short-Term memory (LSTM)
• Recursive Neural Tensor Network Theory
• Recurrent Neural Network Model
• Introduction to Restricted Boltzmann Machine
• Applications of RBM
• Collaborative Filtering with RBM
• Getting started with Autoencoders
• Autoencoders applications
• Here you will learn how to implement Keras API and how to use Keras with TensorBoard.
• Getting started with Keras
• Compose Models in Keras
• What is sequential composition?
• What is functional composition?
• Predefined Neural Network Layers
• What is Batch Normalization?
• Save and Load a model with Keras
• Customize the model training process
• What is TFLearn?
• Compose Models in TFLearn
• Sequential Composition
• Functional Composition
• Predefined Neural Network Layers
• Batch Normalization
• Save and Load a model with TFLearn
• Customize the Training Process

Course 4: Tableau Certification Training

36
WEEK

10
Modules

36
Hours

8
Skills

Watch Course Recording

● Data Visualization
● Different Business Intelligence tools
● Introduction to Tableau
● Tableau Architecture
● Tableau user interface
● Connection to DataSource
● Tableau data types
● Getting Started with Data
● Data preparation and processing with NULL values
● Data linking
● Cross-database links
● Merging data
● Managing Metadata
● Managing Extracts
● Saving and Publishing Data Sources
● Connecting to Data from the Web
● Data Preparation with Text and Excel Files
● Join Types with Union
● Cross-database Joins
● Data Blending
● Additional Data Blending Topics
● Connecting to PDFs
● Connecting to Cubes
● Select and Mark
● Sort and Group
● Work with Sets
● Permanent Sets
● Calculated Sets
● Bins
● Filter (Add and Remove)
● Continuous Date Filtering
● Dimensions
● Sizes
● Interactive Filters
● Brand Mapping
● Hierarchy
● Creating Tableau Folders
● Tableau Sorting
● Sorting Types
● Filtering in Tableau
● Filtering Types
● Filtering by Operations
● Data formatting
● Formatting panels (Menus, Settings, Fonts, Orientation, Copy-paste
● Trends and reference lines
● Forecasting
● Cluster analysis of tableau k-means
● Visual analysis in baselines and ribbons Tableau
● Confidence interval
● Drawing Coordinates
● Latitude and Longitude
● Editing Unrecognized Places
● Custom Geocoding
● Polygon Maps
● WMS: Web Mapping Service
● Background Image
● Generating Image Coordinates
● Map Preview
● Custom Areas
● Cards
● WMS Map
● Syntax and Tableau calculation functions
● Calculation types (table, string, logic, date, number, aggregate)
● LOD expressions (concepts and syntax)
● Nested LOD expressions
● Level of detail
● Level of fixed details
● Level of detail lower
● Higher level of detail
● Fast table calculations
● Creating calculated fields
● Preset calculations
● Validating
● Create parameters
● Parameters in the calculation of filter parameters
● Column selection parameters
● Chart selection parameters
● Parameters in the filter session
● Parameters in calculated fields
● Parameters in reference lines
● Double axis charts, bar charts
● Fields
● Pareto diagrams
● Motion graphs and funnel diagrams
● Tree maps and heat charts, heat maps
● Text tables
● Grained graphs
● Pie charts Tree diagrams
● Bar charts
● Line charts
● Balloon Diagrams
● Bullet Diagrams
● Scatterplots
● Biaxial Diagrams
● Funnel Diagrams
● Pare Diagrams
● Pare Diagrams
● Getting Started with Dashboards and Stories
● Building a Dashboard
● Dashboard Objects
● Dashboard Formatting
● Dashboard Interactivity Using Actions
● Dashboard Extensions
● Device Designer
● Story Points
● Tableau Prep Builder Introduction
● Prep Builder Interface
● Input Step
● Cleaning Step
● Group and Replace
● Profile Pane
● Pivot Step
● Aggregate Step
● Join Step
● Union Step
● Output Step
● Tableau Prep Conductor
● Introduction to the R programming
● Applications and use cases of R
● R programming on Tableau platform implementation
● Integration Tableau with R
● Introduction to Hadoop
● Integration Tableau with Hadoop
150+

Hours of Content

50+

Live Sessions

14+

Tools & Softwares

30+

Case Studies & Projects

Industry Projects

Learn through real-life industry projects sponsored by top companies across industries
  • Engage in collaborative projects with student-mentor interaction
  • Benefit by learning in-person with expert mentors
  • Personalised subjective feedback on your submissions to facilitate improvement
  • Project 1: Loan Prediction

    A major government bank has approached your company to analyze from their customer loan data set. Since the last 4 months, a lot of customers who are not able to repay their loan amount has increased. You have been assigned a task of analyzing from the data set and giving insights about which customer should be given the loan approval and which shouldn’t be.

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    Project 2: Identify the most optimal ratio/aspects to allocate funds/spending

    Develop an ML algorithm to identify the most optimal ratio/ aspects to allocate funds/ spending proportionately by organizations in different areas of expenses like R&D, Marketing, Employee Cost, HR & Admin Cost, Infrastructure Cost etc. Identifying the optimal ratio of the amount of allocation of funds to various segments is utmost important. This would also help the Management team with below aspects, to increase the revenue and profitability, to better design marketing strategies, to allocate the internal resources better.

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    Project 3: Twitter Sentiment Analysis

    As you are holding the position of Data Scientist in your current organization, you need to build a model to categorize words based on sentiments. This model should tell whether words detected are positive or negative.

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    Project 4: Analyzing the Stock Market

    Andrew is a Data Analyst in a company named ValueAnalytics, he has been assigned a project to analyze the Stock Market from a data set of Technology Stocks, by using the different libraries, he has to extract the stock information and perform the visualization of different aspects, along with analyzing the risk of a stock from its past history. find the change in the price of the stock over time, find the daily return of the stock on average, find the moving average of the various stocks, find the correlation between different stocks' closing prices, find the correlation between different stocks' daily returns, find the value we should put at risk by investing in a particular stock, also, attempt to predict future stock behaviour.

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    Project 5: Analyzing the Election Data

    Post the election, the government has given your company a contract of doing the analysis on the Election and Donor Data. You as the data analyst are supposed to answer a few questions by analyzing the aggregated poll data. Like how many votes are done and different aspects in it along with analyzing the average donations given to Democrats and Republican (more questions are asked during the project).

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    Project 6: Build a Machine Learning model which should be able to detect multiple faces when shown. Use OpenCV to perform the operations.

    Provide your machine with the multiple datasets of multiple persons in OpenCV, and after running it, it should be able to detect those faces and display their names whenever detected on the screen or camera. A Dataset which should consist of everyone’s face images into a subfolder by names. Each person’s dataset should consist of at least 3 images which will be used to verify the operation of your model

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    Project 7: Make a Hybrid Deep Learning Model by Analyzing the Credit Card Applications Data Set

    Build a model to identify the frauds from the Data Set. Then, move to build an advanced deep learning mapping model to identify and predict the probability that each customer cheated. First, build the unsupervised deep learning branch from your hybrid deep learning model. Second, develop the supervised learning branch and then compose this hybrid deep learning model comprising both supervised and unsupervised deep learning.

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    Project 8: Natural Language Processing for Text Classification. A filter using NLP, which can identify the SPAM Messages

    Build a Spam filter which can identify the SPAM Messages from the Data Set given to you by using NLTK and Scikit Learn.

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    Project 9: Build a Machine Learning model which should be able to detect multiple faces when shown. Use OpenCV to perform the operations.

    Provide your machine with the multiple dataset of multiple persons in OpenCV, and after running it, it should be able to detect those faces and display their names whenever detected on the screen or camera. A Dataset which should consist of everyone’s face images into subfolders by names. Each person’s dataset should consist of at least 3 images which will be used to verify the operation of your model

    Read More
    Project 10: Make a Hybrid Deep Learning Model by Analyzing the Credit Card Applications Data Set

    Build a model to identify the frauds from the Data Set. Then, move to building an advanced deep learning mapping model to identify and predict the probability that each customer cheated. First, build the unsupervised deep learning branch from your hybrid deep learning model. Second, develop the supervised learning branch and then compose this hybrid deep learning model comprising both supervised and unsupervised deep learning.

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    Project 11: Sales analysis of a top mega store company

    You are working as a BI Specialist for a company called DataViziOne. Your company got a project from one of the top megastore companies in the US. They want a sales analysis in USA, city, state-wise, and country wise. You need to create a dashboard visualizing. Which country which has the best sales record (need to show the map view), which state which has the best sales record based on revenue (need to show geographical visualization), which salesman from a city to find out the best salesman (Drill down from every city of a state to find out the best sales record in a city)?

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    Project 12: Show the sales view of a giant garment store in a specific region

    A giant garment store company has approached your company, now you as a BI professional need to find out the insights from one of their stores in a region and perform the following tasks on their sales report. Use report background colour or background image on your report. Create a report header using Text, Image, Shape visuals (Report Name, Company Name, Company Logo). Make sure to use the containers in the above tasks. Plot all required filters which are useful to analysis. Show Gross Sales & Total Profit using card visuals. Show sales and profit using appropriate visuals by Year/Month (Using Drilldown). Show Segment wise sales & profit. Maximum & minimum discount has to be given to which product. Show profit % by country. Publish report to the Tableau Workbook in the "Finance" workspace. Create and share the workbook with your colleagues

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    Course Fees

    Life time Free Upgrade, 24x7 Life time Support & Access, Attend as many batches for Life time,
    1:1 Doubt Resolution Sessions

    Batches  Date 
    TIME TABLE 

      Date  Days  Time 
    29th Aug Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST
    4th Sep Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST
    12th Sep Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST
    19th Sep Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST

    Course Fees
    Details & Offers

    Was $ 700

    Now $ 349.00
    50% OFF

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    Suvarchala Kopella

    Avatar System Engineer
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    Certification

    Master's Degree from Eduranz

    Complete all the courses successfully to obtain this prestigious recognition from LJMU.
  • Connect with Global network of Faculty
  • Get access to complete digital library
  • Earn a Master's degree same as on-campus degree at 1/10th the cost
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