Data Science Course
Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning. The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the “sexiest job of the 21st century” by Harvard Business Review (link resides outside of IBM). Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes.
What you’ll learn?
provides Data Science where we focused on extracting knowledge from typically large data sets and applying the knowledge and insights from that data to solve problems in a wide range of application domains.
The field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains.
As such, it incorporates skills from computer science, statistics, information science, mathematics, data visualization, information visualization, data sonification, data integration, graphic design, complex systems, communication and business.
Users should be able to intuitively control and explore database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities.
Requirements
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After completing this course, you will be able to:
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Perform basic operations related to data analysis
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Learn how to read, explore and visualize data
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Work with data frames and different libraries
DATA SCIENCE COURSE CONTENTS
- Introduction to Data Science
- What is Data
- Database Table
- Python
- DataFrame
- Functions
- Data Preparation
- Linear Functions
- Plotting Functions
- Slope and Intercept
- Stat Introduction
- Percentiles
- Standard Deviation
- Variance
- Correlation
- Correlation Matrix
- Correlation vs Causality
- Linear Regression
- Regression Table
- Regression lnfo
- Regression Coefficents
- Regression P-Value
- Regression R-Squared
- Linear Regression Case
