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.
Experts from
Duration
4 Months
Start Date
15th May, 2022
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
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.
Read MoreWhy 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.
Read MoreWho should take up this course?
Anyone willing to build a cloud computing career in Data Science.
Read MoreWhat are the prerequisites for this course?
No prerequisite. We teach everything from scratch.
Read MoreHiring Companies
Program Syllabus
Course 1: Data Science Certification Training
36
WEEK
10
Modules
36
Hours
8
Skills
Watch Course Recording
• Business objectives
• Key driving factors in the analytics world
• Graphical User Interfaces (GUI)
• Installing R studio
• Install essential packages along with different GUIs
• 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
• 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
• GGPLOT, GGPLOT2, and PLOTLY
• Working with Histograms & Density Plot
• Dot Plots
• Bar Plots
• Line Charts
• Pie Charts
• Boxplots
• Scatterplots
• Understanding the spread and data points
• Understanding the sourced data for better analysis
• 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
• 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
• Understanding steps to perform the Classification based on inferences on Decision Tree
• Extensive standard R Packages and Functions
• Linear & Logistic Regression
• Use of Linear & Logistic Regression Model
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• 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
• Features of Python
• Advantages of using Python
• Companies using Python
• Installation process of Python
• Basic commands of Python
• Numbers
• Simple arithmetic operations in Python
• Assigning Variables in Python
• Operators in Python
• Strings
• Indexing, slicing, and formatting
• Lists
• Tuples
• Sets
• Boolean
• Dictionaries
• 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
• 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
• 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
• Syntax of Decorators and Working with them
• Generators in Python
• Working with Generators
• 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
• Clusters, Linning, Signals, Optimization, Integration, Sub packages
• Bayesian Theory
• 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
• Seaborn
• Pandas Built-in Data Visualization
• Plotly and Cufflinks
• Geographical Plotting
• 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
• 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
• 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)
• 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
• OOPs Concept in Python
• Introduction to NumPy
• Introduction to Pandas
• Data Pre-processing
• Data Manipulation
• Data Visualization
• Generalized Linear Models
• Regression and Clustering
• Supervised Learning – Regression
• Supervised Learning – Classification
• Model Selection and Boosting
• Unsupervised Learning
• Dimensionality Reduction
• Association Rules Mining and Recommendation
• 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
• Bayesian Network
• Inference
• Model learning
• Getting started with Reinforcement Learning
• Bandit Algorithms and Markov Decision Process
• Dynamic Programming and Temporal Difference Learning methods
• What is Deep Q Learning?
• Analyzing Sentence Structure
• Text Classification
• Sentiment Analysis
• Why Deep Learning?
• Advantage of Deep Learning over Machine learning
• 3 Reasons to go for Deep Learning
• Real-Life use cases of Deep Learning
• Activation Functions
• Illustrate Perceptron
• Train a Perceptron
• Parameters of Perceptron
• TensorFlow
• Graph Visualization
• Constants, placeholders, and variables
• Create a Model
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• What is a backpropagation?
• Getting started with TensorBoard
• 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
• Application of CNN
• Architecture of a CNN
• Convolution and Pooling layers in a CNN
• Modelling sequences
• Training RNNs with Backpropagation
• Long Short-Term memory (LSTM)
• Recursive Neural Tensor Network Theory
• Recurrent Neural Network Model
• Applications of RBM
• Collaborative Filtering with RBM
• Getting started with Autoencoders
• Autoencoders applications
• 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
• 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
● Different Business Intelligence tools
● Introduction to Tableau
● Tableau Architecture
● Tableau user interface
● Connection to DataSource
● Tableau data types
● 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
● Sort and Group
● Work with Sets
● Permanent Sets
● Calculated Sets
● Bins
● 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
● 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
● 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
● 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
● 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
● 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
● Building a Dashboard
● Dashboard Objects
● Dashboard Formatting
● Dashboard Interactivity Using Actions
● Dashboard Extensions
● Device Designer
● Story Points
● 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
● 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
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.
Read More
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.
Read More
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.
Read More
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.
Read More
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).
Read More
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
Read More
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.
Read More
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.
Read More
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.
Read More
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)?
Read More
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
Read MoreCourse 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 | |
|---|---|---|---|
| 15th May | Sat & Sun (Weekend) | 8:00 PM IST to 11:00 PM IST | |
| 21st May | Sat & Sun (Weekend) | 8:00 PM IST to 11:00 PM IST | |
| 28th May | Sat & Sun (Weekend) | 8:00 PM IST to 11:00 PM IST | |
| 5th Jun | Sat & Sun (Weekend) | 8:00 PM IST to 11:00 PM IST |
Course Fees
Details & Offers
Was $ 700
Now $ 349.00
50% OFF
What Our Learners Are Saying ?
Abhas Kant
Technical Consultant at Moueclick Technologies
Change is the need of our Technical Profession so it becomes very important to learn the new technologies like Azure Data Factory and i did the same to enhance my skills. Eduranz helped me a lot to learn all the concepts mainly focusing on Hands-On.
Asim Nouman
Senior Software Engineer at TECH MAHINDRA
The AWS Solutions Architect online Instructor-Led Training, which i took from Eduranz was interactive and also helped me to improve my knowledge on Cloud. Further helped me in changing the job as well. Thanks for providing a well-experianced trainer
Abhas Kant
Technical Consultant at Moueclick Technologies
Change is the need of our Technical Profession so it becomes very important to learn the new technologies like Azure Data Factory and i did the same to enhance my skills. Eduranz helped me a lot to learn all the concepts mainly focusing on Hands-On.
Suvarchala Koppella
Senior Consultant at Capgemini
"Wanted to Switch from BI to Azure Data Factory but being a senior professional i was skeptical about moving to Cloud and then Eduranz was referred by one of my colleagues. Cleared the Azure Data Engineer Certification and Switched to a new job role
Dileep M
Assistance Manager, Vodafone Idea Limited
This course on Azure will leave one owning all the information that is required to shape up and clear the certification. From Instructor to the support team has always made sure that every individual gets the entire volume of the subject. The structu
Shivapriya Goud
Application Programmer at Black Knight
I have been part of AZ 104, AZ 303-304 Trainings. The courses were brilliantly designed to make the leraners understand, gain confidence and Clear the Official Certifications on what they are learning for. I got more than what I am looking for and ga
Abbas Ali
Advisory Consultant at IBM India Private Limited
Let me start of by congratulating the entire Eduranz team for putting together such amazing content for their courses. I am enrolled in their Azure live batches courses and my learning experience has been phenomenal. I tried learning Azure on my own
Preksha Jain
Application development senior Analyst at Accenture
I had opted for the Azure course and to tell you the faculties were well experienced and the learning curve was really high. I have Cleared the Azure Solutions Arch Certification by the help of Eduranz Team including My Trainer.If you are having seco
Shivaji Mengar
Senior Software Engineer at AWC Software Pvt Ltd
I have been using Eduranz for learning different topics related to DevOps, Azure. I am very happy with the training and the help they are providing and I feel better than another online training where I registered for AWS. One of the great things is
Asim Nouman
Senior Software Engineer at TECH MAHINDRA
The AWS Solutions Architect online Instructor-Led Training, which i took from Eduranz was interactive and also helped me to improve my knowledge on Cloud. Further helped me in changing the job as well. Thanks for providing a well-experianced trainer
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