MOD3: Data Science with Python
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Description
In this module, participants will learn how to leverage Python for data analysis and manipulation. This course is designed for those who have a basic understanding of Python and want to explore its capabilities in data science. Key topics include data cleaning, data visualization, and statistical analysis using popular Python libraries such as pandas, NumPy, and matplotlib. By the end of this module, participants will be able to process and analyze large datasets, create meaningful visualizations, and derive insights from data.
What are the aims of the training?
The objective of MOD3: Data Analysis with Python is to equip participants with the skills necessary to perform data analysis and visualization using Python. By the end of this module, participants will be proficient in using libraries such as pandas, NumPy, and matplotlib to clean, manipulate, and visualize data. They will also learn techniques for statistical analysis and how to derive meaningful insights from large datasets, preparing them for real-world data science applications.
What do you need to know to follow the training?
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Completion of MOD1: Introduction to Python
- Participants should have completed the Introduction to Python module or have equivalent knowledge.
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Basic Understanding of Python
- Familiarity with Python syntax, variables, data types, control structures, and functions.
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Completion of MOD2: Advanced Python
- Participants should have completed the Advanced Python module or have equivalent knowledge.
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Basic Understanding of Data Structures
- Understanding of basic data structures like lists, dictionaries, and sets.
Training programme
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Introduction to Data Analysis
- Overview of data analysis and its importance
- Setting up the environment for data analysis in Python
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Working with Pandas
- Introduction to the pandas library
- DataFrames and Series
- Loading and saving data (CSV, Excel, etc.)
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Data Cleaning
- Handling missing values
- Data transformation and normalization
- Filtering and sorting data
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Data Manipulation
- Merging, joining, and concatenating DataFrames
- Grouping and aggregating data
- Pivot tables
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Introduction to NumPy
- Overview of NumPy and its uses
- Working with arrays
- Mathematical and statistical operations
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Data Visualization with Matplotlib
- Introduction to matplotlib
- Creating basic plots (line, bar, scatter, histogram)
- Customizing plots (labels, titles, legends)
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Advanced Data Visualization
- Using seaborn for statistical plots
- Creating complex visualizations
- Interactive visualizations with Plotly
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Statistical Analysis
- Descriptive statistics
- Hypothesis testing
- Correlation and regression analysis
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Project: Real-World Data Analysis
- Applying learned techniques to a real dataset
- Cleaning, analyzing, and visualizing the data
- Presenting findings and insights
How do I register for the course?
On the following link: https://forms.gle/1yonvCZhr1HZtrBx5