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Data Science Professional

Data Science Professional – Microsoft

Does create a buzz in you? Hop on to the most comprehensive Machine Learning & Data Science training program. Driven by the Microsoft technology stack. A systematic learning approach into the Machine Learning universe. Each module will give new skills and will give you better understanding on this exciting field.

Forms Developer

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes, and projects.

Application Developers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Developer

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

What’s Included in the course

Course Duration: 20 Days

What do I get from this course?

More than 400 hours of learning with project work, role-plays, code challenges, peer collaboration. Comprehensive project and coding time all through and at the end of the program. Projects designed and implemented as in the real world.

  • Machine Learning Principles
  • Data Science
  • R
  • Python
  • Statistical Analysis & Data Visualization with Excel
  • Analysis & Visualization with T-SQL (SQL Server)
  • Advance Programming with R
  • Advance Programming with Python
  • MS Azure
  • MS Machine Learning Server and MUCH MORE

What are the requirements for joining in?

You can just start if you know High school level mathematics, some statistics. Better yet if you programmed (or) coded (or) worked with MS Excel. But don’t worry we will make this fun and exciting even for starters.

You will need a laptop/ desktop and connected to internet.

How will course happen?

This is a instructor-led classroom course. Our professional mentors will deliver the course, coursework, hands-on everything online. Participate with the co-learners, have your Q&A posted on forums. Get your queries answered on our learning platform.

Who will train me?

Our team of experienced data science experts with great academic experience will take you through this experience. They are geeky but also know how to make this a fun filled mix of learning, real time runs, coding challenges, presentations and many more.

Who should attend this course?

  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning.
  • College students in college who want to start a career in Data Science.
  • Analysts who want to level up in Machine Learning.
  • People who are not satisfied with their job and who want to become a Data Scientist.
  • People who want to create added value to their business by using powerful Machine Learning tools.

Course Structure

PART 1- FUNDAMENTALS OF DATA SCIENCE
Course 1-Data Science Orientation
Introduction to Data Science
The Data Science Curriculum
Data Science Fundamentals
A Basic Introduction to Statistics
Exploring Data with Excel
Assessment 1
Course 2-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 (Part 1)
Course 2-Querying Data with Transact-SQL (contd.)
Using Subqueries and APPLY (Part 2)
Using Table Expressions
Grouping Sets and Pivoting Data
Modifying Data
Programming with T-SQL
Error Handling and Transactions
Assessment 2
Course 3: Analysing and Visualizing Data with Excel
Gather and Transform Data from Multiple Sources
Discover and Combine Data in Mashups
Data Model Creation
Explore, Analyse, and Visualize Data (Part 1)
Course 3: Analysing and Visualizing Data with Excel (contd.)
Explore, Analyse, and Visualize Data (Part 2)
Assessment 3
Course 4-Essential Statistics for Data Analysis using Excel
Descriptive Statistics
Basic Probability
Random Variables
Sampling and Confidence Intervals
Hypothesis Testing
Assessment 4
Course 5-Introduction to R for Data Science
Basics of R
Vectors
Matrices
Factors
PART 2 - CORE DATA SCIENCE
Course 5-Introduction to R for Data Science (contd.)
Data Frames
Lists
Basic Graphics
Assessment 5
Course 6-Introduction to Python for Data Science
Python Basics
Python Lists
Functions and Packages
Numpy
Matplotlib
Control Flow and Pandas
Assessment 6
Course 7-Data Science Essentials
Explore the Data Science Process
Course 7-Data Science Essentials (contd.)
Probability and Statistics in Data Science
Data Exploration and Visualization
Data Ingestion and Cleansing
Introduction to Machine Learning
Assessment 7
Course 8-Principles of Machine Learning
Explore Classification
Regression in Machine Learning
Improving Machine Learning Models
Tree and Ensemble Methods
Optimization-Based Methods
Clustering and Recommenders (Part 1)
Course 8-Principles of Machine Learning (contd.)
Clustering and Recommenders (Part 2)
Assessment 8

 

PART 3 - APPLIED DATA SCIENCE
Course 9-Programming with R for Data Science
Functions
Control Flow and Loops
Working with Vectors and Matrices
Reading in Data
Writing Data
Reading from SQL Databases
Working with Data
Manipulating Data
Simulation
Linear Model
Advanced Graphics in R
Assessment 9
Course 10-Programming with Python for Data Science
The Big Picture
Data and Features
Exploring Data
Transforming Data (Part 1)
Course 10-Programming with Python for Data Science (contd.)
Transforming Data (Part 1)
Data Modelling
Evaluating Data
Assessment 10
Course 11-Applied Machine Learning
Time Series and Forecasting
Spatial Data Analysis
Text Analytics
Image Analysis
Assessment 11
Course 12-Implementing Predictive Solutions with Spark in Azure HDInsight
Introduction to Data Science with Spark (Part 1)
Course 12-Implementing Predictive Solutions with Spark in Azure HDInsight (contd.)
Introduction to Data Science with Spark (Part 2)
Getting Started with Machine Learning
Evaluating Machine Learning Models
Recommenders and Unsupervised Models
Assessment 12
Course 13-Analysing Big Data with Microsoft Machine Learning Server
Introduction to RevoScaleR
Reading Data
Course 13-Analysing Big Data with Microsoft Machine Learning Server (contd.)
Preparing Data
Examining Data
Visualizing Data
Clustering
Modelling
Deploying
Scaling
Assessment 13
PART 4 - DATA SCIENCE PROJECT
Project Discussion
Project Development

 

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