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In this course we teach three major topics 1) Reporting Analytics Tools 2) Business Intelligence Tools 3) Machine Learning with Python

30,000

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.

Course Currilcum

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