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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