Programming for Scientific Research with Python and R
- Description
- Curriculum
- FAQ
- Reviews
Conquer Scientific Data with Python and R!
Unleash the power of programming for your research!
This comprehensive course equips you with the essential programming skills needed to tackle scientific data analysis and research projects. Whether you’re a researcher, student, or simply curious about scientific computing, this course offers a perfect blend of Python and R to empower your scientific journey.
Master the Fundamentals:
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Gain a solid understanding of both Python and R programming languages.
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Learn core programming concepts like variables, data types, control flow, functions, and modules.
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Explore the strengths and weaknesses of each language to choose the right tool for the job. (New!)
Wrangle and Analyze with Confidence:
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Grasp essential file handling techniques in Python to manage your scientific data effectively.
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Master data manipulation methods in R to organize and prepare your data for analysis.
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Leverage powerful libraries like NumPy, SciPy, and core R functionalities to perform statistical calculations crucial for your research.
Visualize Your Findings Clearly:
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Craft informative and visually appealing graphs and plots using Matplotlib in Python and ggplot2 in R.
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Effectively communicate your research results through compelling data visualizations.
Real-World Applications:
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Solidify your learning by applying your newfound programming skills to practical case studies.
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Gain valuable experience tackling real-world scientific research scenarios.
By the end of this course, you’ll be able to:
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Confidently navigate the world of scientific computing with Python and R.
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Clean, manipulate, and analyze your scientific data with ease.
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Conduct essential statistical analyses to support your research.
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Create impactful data visualizations to communicate your findings effectively.
Enroll now and unlock the potential of programming for your scientific endeavors!
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1
Python: Install Miniconda and Python 3 on Windows -
2
Python: How to Create Environments and Install Packages
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3
Python: How to Install and Run jupyter notebook
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4
Python: Run a Python program
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5
Python: Types of data in Python
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6
Python: Control Flow in Python
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7
Python: Functions
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8
Python: Modules
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9
Writing a Simple Function in Python -
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R: Course Script and Download R -
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R: Functions in R programming
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12
R: Packages
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13
R: Working directory
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14
Choosing the right language for your research project
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15
Introduction to Python Programming
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16
Python: File handling
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17
Python: Directories
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18
R: Data types
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19
R: Importing data
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20
Importing Data in R
Write R code to import a CSV file named "data.csv" and display the first 6 rows of the data.
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21
R: Prepare and Download Annual Weather Data
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22
R: Exporting data
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23
R: Data manipulation Part1
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24
R: Data manipulation Part2
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25
Data Handling in Python and R
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36
Python: Plotting Part One
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37
Python: Plotting Part Two
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38
Python: Plotting Part Three
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39
R: Create a report
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40
R: Wind Rose Plots for Different Time Periods
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41
R: Homework: Create a windrose plot for the Erbil station.
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42
R: Basic plotting
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43
R: Advanced and animated graphs
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44
Plotting in Python and R
