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Gene Mapping is the graphic representation of the arrangement of a gene or a DNA sequence on a chromosome. Also known as linkage mapping, it is used to locate and identify the gene or group of genes that determines a particular inherited trait.

The [course_title] course teaches linkage disequilibrium mapping that helps you to analyse non-familial data. Basic concepts of genetic variations will also be discussed in the course.

Prior knowledge of statistical tests and estimation is required.

### Course Curriculum

 Module: 01 Introdução ao CSV 00:02:00 CSVs in Python 00:03:00 修正数据类型 00:03:00 Questions about Student Data 00:01:00 Investigating the Data – Solution 00:02:00 Problems in the Data 00:01:00 Missing Engagement Records 12 00:01:00 Missing Engagement Records 00:01:00 Checking for More Problem Records 00:01:00 找到剩余问题 00:01:00 Module: 02 Refining the Question 00:03:00 Getting Data from First Week 00:01:00 满足好奇心 00:01:00 探索学员参与度 00:04:00 Number of Visits in the First Week 00:02:00 Splitting out Passing Students 00:01:00 Comparing the Two Student Groups 00:01:00 Making Histograms – Solution 00:02:00 Seus Resultados são Apenas Sujeira? 00:01:00 A correlação não implica a causa 00:03:00 Module: 03 Previsão baseada em muitas características 00:01:00 沟通 00:01:00 Improving Sharing Plots – Solution 00:01:00 数据分析与相关术语 00:02:00 Conclusão 00:01:00 Dados Uni-Dimensional em NumPy e Pandas 00:02:00 NumPy Arrays – Solution 00:04:00 + vs. += Solution 00:01:00 In-Place vs. Not In-Place 00:01:00 Series Indexes – Solution 00:01:00 Module: 04 Vectorized Operations and Series Indexes 00:01:00 Filling Missing Values 00:02:00 Pandas Series apply() – Solution 00:01:00 Plotting in Pandas – Solution 00:01:00 Conclusion 12345 00:01:00 Subway Data 00:01:00 Subway Data – Solution 00:01:00 Two-Dimensional NumPy Arrays – Solution 00:02:00 Two-Dimensional NumPy Arrays – Solution 00:02:00 NumPy Axis 00:01:00 Module: 05 NumPy Axis – Solution 00:01:00 Accessing Elements of a DataFrame 00:02:00 Accessing DataFrame Elements – Solution 00:02:00 CSVs in Python – Solution 00:02:00 Questions about Student Data – Solution 00:02:00 Missing Engagement Records – Solution 00:01:00 More Problem Records – Solution 00:01:00 Refining the Question – Solution 00:01:00 Getting Data from First Week – Solution 00:01:00 Debugging Data Analysis Code – Solution 00:02:00 Module: 06 Lessons Completed in First Week 00:01:00 Number of Visits – Solution 00:02:00 Splitting Students – Solution 00:02:00 Comparing Student Groups – Solution 00:03:00 Gapminder Data – Solution 00:01:00 NumPy Index Arrays – Solution 00:01:00 Multiplying by a Scalar – Solution 00:01:00 Overall Completion Rate – Solution 00:01:00 Standardizing Data – Solution 00:01:00 NumPy Index Arrays – Solution 00:01:00 Module: 07 + vs. += 00:01:00 In-Place vs. Not In-Place 00:01:00 Pandas Series – Solution 00:02:00 Series Indexes 00:03:00 Series Vectorized Operations – Solution 00:02:00 Filling Missing Values – Solution 00:01:00 apply() Example 00:04:00 Sons Of The East – Into The Sun [Official Video] 00:05:00 Loading Data into a DataFrame 00:01:00 Calculating Correlation 00:03:00 Pandas Axis Names 00:01:00 Module: 08 DataFrame Vectorized Operations 00:01:00 Vectorized Operations – Solution 00:02:00 DataFrame applymap() 00:01:00 DataFrame applymap() – Solution 00:01:00 DataFrame apply() Use Case 2 00:01:00 DataFrame apply() – Solution 00:01:00 DataFrame apply() Use Case 2 00:01:00 DataFrame apply() Use Case 2 – Solution 00:02:00 Adding a DataFrame to a Series 00:01:00 Standardizing Each Column Again 00:04:00 Module: 09 Combining Pandas DataFrames – Solution 00:01:00 Plotting for DataFrames – Solution 00:02:00 Conclusão 00:01:00 Assessment Submit Your Assignment 00:00:00 Certification 00:00:00

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4.7
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