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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 Diploma in Data Analysing 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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