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Which course is right for you? This tool allows you to compare any class or certificate program in our catalogue with the important information you need to make the right decision.

Key Information
Data Analytics Certificate Online (Self-Paced)
$4,995 156 Hours
Overview
Learn the essential skills needed to become a Data Analyst or Business Analyst, including data analysis, data visualization, and statistical analysis. Gain practical experience through real-world projects and prepare for a successful career in the field of data analytics.
Prerequisite
Open to Beginners
Location
Online course – start anytime
Schedule
Self-Paced
Certification
Receive a Certificate of Completion
Software Included
Free Retake Within 1 Year See our class policies for more details
Workbook Included
Courses Included (Certificates & packages only)
  • 18 HoursExcel for Data Analytics Course Online (Self-Paced)
  • 12 HoursData Analytics Foundations Course Online (Self-Paced)
  • 30 HoursPython for Data Science Course Online (Self-Paced)
  • 18 HoursSQL Course Online (Self-Paced)
  • 6 HoursPython for Automation Course Online (Self-Paced)
  • 24 HoursPython Data Visualization & Interactive Dashboards Online (Self-Paced)
  • 30 HoursPython Machine Learning Course Online (Self-Paced)
  • 12 HoursTableau Course Online (Self-Paced)
  • 0 hoursData Science Capstone Projects (Self-Paced)
Financing See our Climb Credit FAQ for more details
This program is eligible for 12-month financing through Climb Credit.
Target Audience

The Data Analytics Certificate is best suited for:

  • Anyone looking to launch their career as a Data Analyst or Business Analyst.
  • Anyone who wants to build their knowledge of data visualization, Python, statistics, and database management.
  • People wanting to become a Marketing Analyst or Financial Analyst (requires additional courses after this program).
What You’ll Learn
  • Learn how to use Excel to manipulate and visualize data
  • Gather, clean, analyze, and visualize data with the Python programming language using scientific libraries
  • Extract information using SQL to interact with relational databases and perform database operations
  • Interpret the results in an accurate and meaningful way using advanced techniques and statistical tools to conduct thorough and insightful analyses
  • Use Tableau and Python to visualize data so people can understand it and even interact with it

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