Microsoft Professional Program for Front-End Web Development

The demand for Front-End Web Developers is expected to increase by 27% through 2024.

 

That’s why companies need people who are fluent in programming languages and frameworks ranging from HTML, CSS, and JavaScript, to Angular, Bootstrap, and jQuery.

 

BATCH STARTS:

Early 2019

DURATION:

8 Months

ELIGIBILITY

Minimum of two years full-time work experience after graduation or post-graduation is required.

EFFORT:

7-10 Hours per week

(recommended)

CERTIFICATION:

Post Graduate Program in Blockchain Technology & Management (PGP-BTM) awarded by Amity University

FEES:

Rs. 125000/- (All Inclusive)

Options Available – One Time Payment, No Cost EMI

PROGRAM FORMAT:

Online + Optional Weekend Campus Classes
Hackathons, Closed the door workshops
Webinars and Faculty Doubts sessions

UNIQUE FEATURES:

  • 1-1 Mentoring

  • Gain in-depth subject knowledge and expert insights from industry-driven comprehensive curriculum.

  • 24/7 access to study material & video lectures

  • Faculties having total 80+ years of Experience, CXOs, IIMB, Ex-Apple, Microsoft

  • Live interactions with Blockchain experts and Corporate leaders

  • Hands-on experience to solve corporate level Blockchain issues with our industry partner Auxledger

  • Career guidance and support through leading recruitment partners

  • Get Alumni Status from Amity University

About Us

  • First  Microsoft partner in India for Microsoft Professional Programs delivery and certification.

  • Online platform powered by Openedx with robust and easy to use LMS features.

  • Over 2,50000 users learning online from 165 courses to choose from.

  • Offering Industry relevant skill programs, making people job ready to make a successful career.

  • The only ed-tech company in India to have presence on Mobile, TV and PC with a total reach of around 10 million.

  • Presence on partner platforms like Jio, TCS, Sharechat, Helo and many more.

Program Overview

  • The Millionlights- Micrososoft Professional Program in Data Science will enable you to:

  • Accelerate your career in one of the hottest fields – Data Science.

  • Learn data science fundamentals, key data science tools, and widely-used programming languages from industry and academic experts in this unique program created by Microsoft.

  • Develop the analytical and programming skills you need to take advantage of the 1.5 million career opportunities available now in data science.

Program Structure and Format

Program Structure

  • The Data Science Certification program is comprised of 10 courses and a final capstone project.  

  • Recorded videos in every course, and can be completed at self pace and practice questions to make sure topic is understood well.

  • Interactive live lectures and query solving sessions  by eminent faculties and industry specialists in Data Science.

  • The MPP Data Science Certificate will be awarded when you achieve a 70% pass rate and obtain a verified certificate for the 10 courses.

  • Guided lab sessions with instructors for hands on practice on various platforms covered in the program.

Program Details

Program Details: 10 courses + Final Project

Effort: 16 - 32 hours per course

The student needs to complete all the 10 courses along with the labs, assessments and Capstone project ,  to be eligible for MPP Certificate.

Skills Required:

Though, there is no particular prerequisite to learn Data Science, you will be able to do very well, if you have these basic skills:

  • Basic Programming Knowledge

  • Good command in Maths and Statistics

  • Business Knowledge

  • Data Visualisation skills

  • Good communication skills.  

Who Can Learn:

  • Any professional from Business Analytics/ Business Intelligence background.               

  • Engineering or Non-Engineering aspirants wanting to become a Data Scientist.

  • Managers from Analytics background and those who are leading a team of Analysts .

  • Any Data Analyst or Software Developer aspiring to be a Data Scientist.

  • Professionals wanting to build machine learning models, using distributed storage and distributed processing.

Course Curriculum

Module 1


Introduction to Data Science

  • Introduction
  • An Introduction to Data
  • Data Analysis Fundamentals
  • Getting Started with Statistics
  • Machine Learning Basics




Module 2


Option A . Analyzing and Visualizing Data with Power BI

  • Introduction
  • Power BI Desktop Data Transformations
  • Power BI Desktop Modelling
  • Power BI Desktop Visualization
  • Power BI Service
  • Working with Excel
  • Organization Packs, Security and Groups
  • Direct Connectivity
  • Developer API
  • Mobile App
Option B . Analyzing and Visualizing Data with Excel
  • Data Analysis in Excel
  • The Excel Data Model
  • Importing Data from a CSV File
  • Importing Data from Databases
  • Creating and Formatting Measures
  • Using Advanced DAX Functions
  • Importing Data from a Formatted Excel Report
  • Visualizing Data in Excel
  • Using Excel with Power BI
  • Importing Data from Multiple Files
  • Create a Date Table in Excel Data Model
  • Power BI Mobile App




Module 3


Analytics Storytelling for Impact

  • The Power of Analytics Storytelling
  • Craft Your Analytics Story
  • Perfect Your Analytics Story
  • Land Your Analytics Story
  • Final Evaluation




Module 4


Ethics and Law in Data and Analytics

  • Data, Ethics, and Law
  • Data, Individuals, and Society
  • Data Ethics and Law in Business
  • Artificial Intelligence and Future Opportunities
  • Final Evaluation




Module 5


Querying Data with Transact-SQL

  • Introduction to Transact-SQL
  • Querying Tables with SELECT
  • Querying Multiple Tables with Joins
  • Using Set Operators
  • Using Functions and Aggregating Data
  • Using Subqueries and APPLY
  • Using Table Expressions
  • Grouping Sets and Pivoting Data
  • Modifying Data
  • Programming with Transact-SQL
  • Error Handling and Transactions




Module 6


A. Introduction to R for Data Science

  • R: The true basics
  • Vectors
  • Matrices
  • Factors
  • Lists
  • Data frames
  • Graphics
B. Introduction to Python for Data Science
  • Python Basics
  • List - A Data Structure
  • Functions and Packages
  • Numpy
  • Plotting with Matplotlib
  • Control Flow and Pandas




Module 7


A. Essential Math for Machine Learning: R Edition

  • Course Introduction
  • Equations, Graphs, and Functions
  • Derivatives and Optimization
  • Vectors and Matrices
  • Statistics and Probability
B. Essential Math for Machine Learning: Python Edition
  • Equations, Graphs, and Functions
  • Derivatives and Optimization
  • Vectors and Matrices
  • Statistics and Probability
C. Essential Statistics for Data Analysis using Excel
  • Descriptive Statistics
  • Basic Probability
  • Random Variables
  • Sampling and Confidence Intervals
  • Hypothesis Testing




Module 8


A.Data Science Research Methods: R Edition

  • Introduction
  • The Research Process
  • Planning for Analysis
  • Research Claims
  • Measurement
  • Correlational and Experimental Design
B. Data Science Research Methods: Python Edition
  • Introduction
  • The Research Process
  • Planning for Analysis
  • Research Claims
  • Measurement
  • Correlational and Experimental Design




Module 9


A. Principles of Machine Learning: R Edition

  • Introduction to Machine Learning
  • Exploring Data
  • Cleaning and Preparing Data
  • Getting Started with Supervised Learning
  • Improving Model Performance
  • Machine Learning Algorithms
  • Unsupervised Learning
B. Principles of Machine Learning: Python Edition
  • Introduction to Machine Learning
  • Exploring Data
  • Cleaning and Preparing Data
  • Getting Started with Supervised Learning
  • Improving Model Performance
  • Machine Learning Algorithms
  • Unsupervised Learning




Module 10


A. Developing Big Data Solutions with Azure Machine Learning

  • Introduction to Azure Machine Learning
  • Building Predictive Models with Azure Machine Learning
  • Operationalizing Machine Learning Models
  • Using Azure Machine Learning in Big Data Solutions
B. Developing Big Data Solutions with Azure Machine Learning
  • Introduction to Azure Machine Learning
  • Building Predictive Models with Azure Machine Learning
  • Operationalizing Machine Learning Models
  • Using Azure Machine Learning in Big Data Solutions
C. Implementing Predictive Analytics with Spark in Azure HDInsight
  • Introduction to Data Science with Spark
  • Getting Started with Machine Learning
  • Evaluating and Optimizing Machine Learning Models
  • Recommenders and Unsupervised Models





Admission Process

  • Fill up the application form

  • You will receive a call for eligibility screening

  • Final Application form

Program Fees:

  • Zero EMI Option

  • Credit, Debit Card and Net Banking

Learning Outcome:

  • Earn  a certificate  in data science which opens the door to countless career opportunities .

  • Become an expert in software development, predictive analytics, database systems, and statistics.

  • Apply data science techniques to your organization’s data management challenges.

  • Identify and avoid common difficulties  in big data analytics and simplify analytical models to make better business decisions

  • Understand the challenges associated with scaling big data algorithms and convert datasets to models through predictive analytics.

Career Paths

Data Scientist:

  • as data scientist you are specialized in Retrieving, mining and statistically analysing large chunk of data

  • understand the challenges of a business and offer the best solutions using data analysis and data processing.

  • perform predictive analysis and run a fine-toothed comb through an “unstructured/disorganized” data to extract actionable insights

  • identify trends and patterns that can help the companies in making decisions, that would otherwise be difficult to make.

Data Analyst:

  • perform analysis on data using various mathematical calculations

  • perform variety of tasks like visualization, munging, and processing of massive amounts of data.

  • analyse databases by performing queries on the databases from time to time.

  •  The most important skills of a data analyst is optimization.

  • create and modify algorithms that can be used to pull information from very large databases without affecting and corrupting the data.

Job Titles

The popular job titles for in the field of Data Analysis and  Data Science:

  • Data Architect:

  • Data Engineers:

  • Database Administrator:

  • Business Analyst:

  • Data and Analytics Manager:

Evaluation

  • The evaluation of the program is based on completion of assessments in each course as well as the Capstone Project.

Assignment

Each module contains  graded assignments. you have to  complete the assessments and labs available

  • Some courses contains lab assignments without due dates. You can progress through the course at your own speed.

  • Assess your progress with grade/score of each assignment that contributes to the overall grade.

  • The overall passing percentage for each course is 70%, including the lab assessments.

  • The student has to pass in all courses to be eligible for the MPP Certificate.

Projects

Capstone Project

  • Microsoft Professional Capstone : Data Science

  • You get opportunity to Showcase your data science knowledge and skills, and solve a real-world data science problem by doing the  Microsoft Professional Capstone : Data Science

  • The project takes the form of a challenge in which you will explore a dataset and develop a machine learning solution that is tested and scored to determine your grade.

Career Assistance

  • Our career support team will counsel you on the right career path and also help you prepare for the role of your interest

  • Get specific inputs on updating your resume structure, content and receive personalised feedback to improve on it

  • interview readiness session to help you to prepare for interviews

  • We conduct 1-1 mock interviews based on the role you wish to apply for and provide you with feedback.

Note: We don’t provide any 100% Job Guarantee

Career Assistance

FAQ

Frequently asked questions

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Frequently asked questions

Module 1


Introduction to Data Science

  • Introduction
  • An Introduction to Data
  • Data Analysis Fundamentals
  • Getting Started with Statistics
  • Machine Learning Basics




Module 2


Option A . Analyzing and Visualizing Data with Power BI

  • Introduction
  • Power BI Desktop Data Transformations
  • Power BI Desktop Modelling
  • Power BI Desktop Visualization
  • Power BI Service
  • Working with Excel
  • Organization Packs, Security and Groups
  • Direct Connectivity
  • Developer API
  • Mobile App
Option B . Analyzing and Visualizing Data with Excel
  • Data Analysis in Excel
  • The Excel Data Model
  • Importing Data from a CSV File
  • Importing Data from Databases
  • Creating and Formatting Measures
  • Using Advanced DAX Functions
  • Importing Data from a Formatted Excel Report
  • Visualizing Data in Excel
  • Using Excel with Power BI
  • Importing Data from Multiple Files
  • Create a Date Table in Excel Data Model
  • Power BI Mobile App




Module 3


Analytics Storytelling for Impact

  • The Power of Analytics Storytelling
  • Craft Your Analytics Story
  • Perfect Your Analytics Story
  • Land Your Analytics Story
  • Final Evaluation




Module 4


Ethics and Law in Data and Analytics

  • Data, Ethics, and Law
  • Data, Individuals, and Society
  • Data Ethics and Law in Business
  • Artificial Intelligence and Future Opportunities
  • Final Evaluation




Module 5


Querying Data with Transact-SQL

  • Introduction to Transact-SQL
  • Querying Tables with SELECT
  • Querying Multiple Tables with Joins
  • Using Set Operators
  • Using Functions and Aggregating Data
  • Using Subqueries and APPLY
  • Using Table Expressions
  • Grouping Sets and Pivoting Data
  • Modifying Data
  • Programming with Transact-SQL
  • Error Handling and Transactions




Module 6


A. Introduction to R for Data Science

  • R: The true basics
  • Vectors
  • Matrices
  • Factors
  • Lists
  • Data frames
  • Graphics
B. Introduction to Python for Data Science
  • Python Basics
  • List - A Data Structure
  • Functions and Packages
  • Numpy
  • Plotting with Matplotlib
  • Control Flow and Pandas




Module 7


A. Essential Math for Machine Learning: R Edition

  • Course Introduction
  • Equations, Graphs, and Functions
  • Derivatives and Optimization
  • Vectors and Matrices
  • Statistics and Probability
B. Essential Math for Machine Learning: Python Edition
  • Equations, Graphs, and Functions
  • Derivatives and Optimization
  • Vectors and Matrices
  • Statistics and Probability
C. Essential Statistics for Data Analysis using Excel
  • Descriptive Statistics
  • Basic Probability
  • Random Variables
  • Sampling and Confidence Intervals
  • Hypothesis Testing




Module 8


A.Data Science Research Methods: R Edition

  • Introduction
  • The Research Process
  • Planning for Analysis
  • Research Claims
  • Measurement
  • Correlational and Experimental Design
B. Data Science Research Methods: Python Edition
  • Introduction
  • The Research Process
  • Planning for Analysis
  • Research Claims
  • Measurement
  • Correlational and Experimental Design




Module 9


A. Principles of Machine Learning: R Edition

  • Introduction to Machine Learning
  • Exploring Data
  • Cleaning and Preparing Data
  • Getting Started with Supervised Learning
  • Improving Model Performance
  • Machine Learning Algorithms
  • Unsupervised Learning
B. Principles of Machine Learning: Python Edition
  • Introduction to Machine Learning
  • Exploring Data
  • Cleaning and Preparing Data
  • Getting Started with Supervised Learning
  • Improving Model Performance
  • Machine Learning Algorithms
  • Unsupervised Learning




Module 10


A. Developing Big Data Solutions with Azure Machine Learning

  • Introduction to Azure Machine Learning
  • Building Predictive Models with Azure Machine Learning
  • Operationalizing Machine Learning Models
  • Using Azure Machine Learning in Big Data Solutions
B. Developing Big Data Solutions with Azure Machine Learning
  • Introduction to Azure Machine Learning
  • Building Predictive Models with Azure Machine Learning
  • Operationalizing Machine Learning Models
  • Using Azure Machine Learning in Big Data Solutions
C. Implementing Predictive Analytics with Spark in Azure HDInsight
  • Introduction to Data Science with Spark
  • Getting Started with Machine Learning
  • Evaluating and Optimizing Machine Learning Models
  • Recommenders and Unsupervised Models





Frequently asked questions

Module 1


Introduction to Data Science

  • Introduction
  • An Introduction to Data
  • Data Analysis Fundamentals
  • Getting Started with Statistics
  • Machine Learning Basics




Module 2


Option A . Analyzing and Visualizing Data with Power BI

  • Introduction
  • Power BI Desktop Data Transformations
  • Power BI Desktop Modelling
  • Power BI Desktop Visualization
  • Power BI Service
  • Working with Excel
  • Organization Packs, Security and Groups
  • Direct Connectivity
  • Developer API
  • Mobile App
Option B . Analyzing and Visualizing Data with Excel
  • Data Analysis in Excel
  • The Excel Data Model
  • Importing Data from a CSV File
  • Importing Data from Databases
  • Creating and Formatting Measures
  • Using Advanced DAX Functions
  • Importing Data from a Formatted Excel Report
  • Visualizing Data in Excel
  • Using Excel with Power BI
  • Importing Data from Multiple Files
  • Create a Date Table in Excel Data Model
  • Power BI Mobile App




Module 3


Analytics Storytelling for Impact

  • The Power of Analytics Storytelling
  • Craft Your Analytics Story
  • Perfect Your Analytics Story
  • Land Your Analytics Story
  • Final Evaluation




Module 4


Ethics and Law in Data and Analytics

  • Data, Ethics, and Law
  • Data, Individuals, and Society
  • Data Ethics and Law in Business
  • Artificial Intelligence and Future Opportunities
  • Final Evaluation




Module 5


Querying Data with Transact-SQL

  • Introduction to Transact-SQL
  • Querying Tables with SELECT
  • Querying Multiple Tables with Joins
  • Using Set Operators
  • Using Functions and Aggregating Data
  • Using Subqueries and APPLY
  • Using Table Expressions
  • Grouping Sets and Pivoting Data
  • Modifying Data
  • Programming with Transact-SQL
  • Error Handling and Transactions




Module 6


A. Introduction to R for Data Science

  • R: The true basics
  • Vectors
  • Matrices
  • Factors
  • Lists
  • Data frames
  • Graphics
B. Introduction to Python for Data Science
  • Python Basics
  • List - A Data Structure
  • Functions and Packages
  • Numpy
  • Plotting with Matplotlib
  • Control Flow and Pandas




Module 7


A. Essential Math for Machine Learning: R Edition

  • Course Introduction
  • Equations, Graphs, and Functions
  • Derivatives and Optimization
  • Vectors and Matrices
  • Statistics and Probability
B. Essential Math for Machine Learning: Python Edition
  • Equations, Graphs, and Functions
  • Derivatives and Optimization
  • Vectors and Matrices
  • Statistics and Probability
C. Essential Statistics for Data Analysis using Excel
  • Descriptive Statistics
  • Basic Probability
  • Random Variables
  • Sampling and Confidence Intervals
  • Hypothesis Testing




Module 8


A.Data Science Research Methods: R Edition

  • Introduction
  • The Research Process
  • Planning for Analysis
  • Research Claims
  • Measurement
  • Correlational and Experimental Design
B. Data Science Research Methods: Python Edition
  • Introduction
  • The Research Process
  • Planning for Analysis
  • Research Claims
  • Measurement
  • Correlational and Experimental Design




Module 9


A. Principles of Machine Learning: R Edition

  • Introduction to Machine Learning
  • Exploring Data
  • Cleaning and Preparing Data
  • Getting Started with Supervised Learning
  • Improving Model Performance
  • Machine Learning Algorithms
  • Unsupervised Learning
B. Principles of Machine Learning: Python Edition
  • Introduction to Machine Learning
  • Exploring Data
  • Cleaning and Preparing Data
  • Getting Started with Supervised Learning
  • Improving Model Performance
  • Machine Learning Algorithms
  • Unsupervised Learning




Module 10


A. Developing Big Data Solutions with Azure Machine Learning

  • Introduction to Azure Machine Learning
  • Building Predictive Models with Azure Machine Learning
  • Operationalizing Machine Learning Models
  • Using Azure Machine Learning in Big Data Solutions
B. Developing Big Data Solutions with Azure Machine Learning
  • Introduction to Azure Machine Learning
  • Building Predictive Models with Azure Machine Learning
  • Operationalizing Machine Learning Models
  • Using Azure Machine Learning in Big Data Solutions
C. Implementing Predictive Analytics with Spark in Azure HDInsight
  • Introduction to Data Science with Spark
  • Getting Started with Machine Learning
  • Evaluating and Optimizing Machine Learning Models
  • Recommenders and Unsupervised Models





Frequently asked questions

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91 Delamere Road

London

W5 3JP

Marisoft Tower 1, Adlabs

Pune

India

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