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.
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes, and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
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
- 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.
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|
|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|
|Programming with T-SQL|
|Error Handling and Transactions|
|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)|
|Course 4-Essential Statistics for Data Analysis using Excel|
|Sampling and Confidence Intervals|
|Course 5-Introduction to R for Data Science|
|Basics of R|
PART 2 - CORE DATA SCIENCE
|Course 5-Introduction to R for Data Science (contd.)|
|Course 6-Introduction to Python for Data Science|
|Functions and Packages|
|Control Flow and Pandas|
|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|
|Course 8-Principles of Machine Learning|
|Regression in Machine Learning|
|Improving Machine Learning Models|
|Tree and Ensemble Methods|
|Clustering and Recommenders (Part 1)|
|Course 8-Principles of Machine Learning (contd.)|
|Clustering and Recommenders (Part 2)|
PART 3 - APPLIED DATA SCIENCE
|Course 9-Programming with R for Data Science|
|Control Flow and Loops|
|Working with Vectors and Matrices|
|Reading in Data|
|Reading from SQL Databases|
|Working with Data|
|Advanced Graphics in R|
|Course 10-Programming with Python for Data Science|
|The Big Picture|
|Data and Features|
|Transforming Data (Part 1)|
|Course 10-Programming with Python for Data Science (contd.)|
|Transforming Data (Part 1)|
|Course 11-Applied Machine Learning|
|Time Series and Forecasting|
|Spatial Data Analysis|
|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|
|Course 13-Analysing Big Data with Microsoft Machine Learning Server|
|Introduction to RevoScaleR|
|Course 13-Analysing Big Data with Microsoft Machine Learning Server (contd.)|
PART 4 - DATA SCIENCE PROJECT