What does this Course Cover?

• Excel
• SQL
• Power BI
• Tableau
• Python
• Machine Learning

What Will You Learn in the Course?

• Reporting Analytics
• Dashboards Creation
• Data Management
• Data Reporting
• Machine Learning
• Tableau Dashboarding
• Python
• Machine Learning Model Building

Key Features of this Course

• 100% hands-on sessions
• Learning through problem-solving and case studies
• Continuous assessments and Feedback
• Multiple Quizzes and Projects
• Access to recorded videos
• Taught by industry professionals with many years of experience in the field
• It contains numerous examples right from the first lecture until the end.
• Every concept is explained using a business scenario or case study.
• It offers the right mix of theory and hands-on labs.
• The course simplifies complex statistical concepts.
• The course offers Python code and a sufficient number of data sets.
• The course is self-sufficient. You do not need any other resource or reference to grasp the concepts.

Target Audience for this Course

• Everybody who wants to get started with machine learning & deep learning
• Reporting analysts who aim to become data scientists.
• Predictive modeling profile candidates who want to learn ML and DL
• Data visualization experts
• Any Data science aspirants
• Graduates and undergraduate students
• Computer Science Engineering students

Pre-Requisites

• There is no strict pre-requisite
• Anyone with a primary degree can get started with this course
• Basic mathematical skills are sufficient
• Statistical Knowledge is NOT a prerequisite. It will be taught in class.
• Cutting-edge programming knowledge is NOT a pre-requisite. Coding will be taught in the class.

Duration:

• 3 months

Lab Setup:

• Windows 10 with 8 GB RAM.
• Proxy-free internet, Admin rights to execute scripts
• Firefox and Chrome browsers

Course Delivery Plan

• The training has four major phases in each concept discussion:
• Theory, Demo, in-class exercise, and project assignment.
• The algorithm theory will be explained first. The instructor will take a dataset and demonstrate the concept on a dataset. In the third phase, participants will try the code on a new dataset. In the fourth phase, a real-time dataset will be considered and floated as a project.