Introduction to Programming for Data Science (Classroom and Asynchronous e-learning)
Training Provider: SINGAPORE POLYTECHNIC
Course Reference: TGS-2025059022
S$720
Original: S$2,400
Save S$1,680
About This Course
This module provides participants with the fundamental skills to code applications to retrieve, clean and visualise data using the Python programming language. Students learn key concepts such as what structured and unstructured data are, and how they can create and manipulate relational and NoSQL databases to explore data and to create visualisations that can help them gain useful insights from it.
What You'll Learn
By the end of the module, participants will be able to:
1. Code a basic Python program using programming constructs such as variables, lists, operators, control structures and functions.
2. Declare and use variables using standard data types such as numbers, strings, lists, tuples, and dictionary.
3. Perform numerical operations on Numpy arrays.
4. Index, slice and iterate Numpy arrays.
5. Reshape and combine Numpy arrays.
6. Create basic data visualisations (line chart, scatter plot, histogram, box plot) using the Matplotlib package
7. Customise the appearance of data visualisations (change default colors and line widths, set axis labels and ticks, add legends and annotations)
8. Create subplots and overlays on data visualisations
9. Export data visualisations to an external file.
10. Perform basic data retrieval, data wrangling and data analysis using the Pandas package in Python.
11. Explain the purpose of normalisation.
12. Explain 1st, 2nd, and 3rd normal forms.
13. Retrieve and process structured data from a relational database.
14. Explain the difference between structured and unstructured data.
15. Identify the needs for NoSQL database.
16. Retrieve and process unstructured data from a NoSQL database.
1. Code a basic Python program using programming constructs such as variables, lists, operators, control structures and functions.
2. Declare and use variables using standard data types such as numbers, strings, lists, tuples, and dictionary.
3. Perform numerical operations on Numpy arrays.
4. Index, slice and iterate Numpy arrays.
5. Reshape and combine Numpy arrays.
6. Create basic data visualisations (line chart, scatter plot, histogram, box plot) using the Matplotlib package
7. Customise the appearance of data visualisations (change default colors and line widths, set axis labels and ticks, add legends and annotations)
8. Create subplots and overlays on data visualisations
9. Export data visualisations to an external file.
10. Perform basic data retrieval, data wrangling and data analysis using the Pandas package in Python.
11. Explain the purpose of normalisation.
12. Explain 1st, 2nd, and 3rd normal forms.
13. Retrieve and process structured data from a relational database.
14. Explain the difference between structured and unstructured data.
15. Identify the needs for NoSQL database.
16. Retrieve and process unstructured data from a NoSQL database.
Entry Requirements
At least a Higher Nitec qualification. Basic English Proficiency, foundational numeracy skills and familiarity with using digital tools such as Excel. Prior experience in programming or data analysis is advantageous but not mandatory. Participants without the necessary qualification can be considered on a case-by-case basis.
Course Details
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