This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts.
이 강좌는 Data Science Fundamentals with Python and SQL 특화 과정의 일부입니다.
제공자:

이 강좌에 대하여
귀하가 습득할 기술
- Probability And Statistics
- Regression Analysis
- Data Visualization (DataViz)
- Statistical Hypothesis Testing
- Basic Descriptive Statistics
제공자:

IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
강의 계획표 - 이 강좌에서 배울 내용
Course Introduction and Python Basics
Welcome!
Introduction & Descriptive Statistics
This module will focus on introducing the basics of descriptive statistics - mean, median, mode, variance, and standard deviation. It will explain the usefulness of the measures of central tendency and dispersion for different levels of measurement.
Data Visualization
This module will focus on different types of visualization depending on the type of data and information we are trying to communicate. You will learn to calculate and interpret these measures and graphs.
Introduction to Probability Distributions
This module will introduce the basic concepts and application of probability and probability distributions.
Hypothesis testing
This module will focus on teaching the appropriate test to use when dealing with data and relationships between them. It will explain the assumptions of each test and the appropriate language when interpreting the results of a hypothesis test.
검토
- 5 stars71.56%
- 4 stars20.37%
- 3 stars4.73%
- 2 stars0.94%
- 1 star2.36%
STATISTICS FOR DATA SCIENCE WITH PYTHON의 최상위 리뷰
Great introduction to basic statistics for data science. Python specialization suits those with no experience in the language.
The videos, readings, and labs were not sufficient for me to feel prepared for the assessments. I ended up using outside resources just to understand what was being presented here.
It is few of the Data Science courses in my learning series. This is one of the Best in Series. Thanks to the team.
Excellent course with a step by step explanation and complete final assignment.
Data Science Fundamentals with Python and SQL 특화 과정 정보
Data Science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Specialization from IBM will help anyone interested in pursuing a career in data science by teaching them fundamental skills to get started in this in-demand field.

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