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Course Outline
Day 1
- Data science team composition (data scientist, data engineer, data visualiser, process owner)
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Large Language Models
- Common libraries to deploy models (Transformers, PyTorch, Ollama)
- Automating report creation with LLMs
- Automatically generating reports with LLMs
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Business Intelligence
- Types of business intelligence
- Developing business intelligence tools
- Business intelligence and data visualisation
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Data Visualisation
- Importance of data visualisation
- Visual data presentation
- Data visualisation tools (infographics, dials and gauges, geographic maps, sparklines, heat maps, and detailed bar, pie and fever charts)
- Painting by numbers and playing with colours in crafting visual stories
- Activity
Day 2
-
Data visualisation in Python programming
- Data science with Python
- Review of Python fundamentals
- Variables and data types (str, numeric, sequence, mapping, set types, Boolean, binary, casting)
- Operators, lists, tuples, sets, dictionaries
- Conditional statements
- Functions, Lambda, arrays, classes, objects, inheritance, iterators
- Scope, modules, dates, JSON, RegEx, PIP
- Try / Except, command input, string formatting
- File handling
- Activity
Day 3
- Python and MySQL
- Creating database and table
- Manipulating database (insert, select, update, delete, where statement, order by)
- Drop table
- Limit
- Joining tables
- Removing list duplicates
- Reverse a string
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Data visualisation with Python and MySQL
- Using Matplotlib (basic plotting)
- Dictionaries and Pandas
- Logic, control flow and filtering
- Manipulating graph properties (font, size, colour scheme)
- Activity
Day 4
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Plotting data in different graph formats
- Histogram
- Line
- Bar
- Box plot
- Pie chart
- Donut
- Scatter plot
- Radar
- Area
- 2D / 3D density plot
- Dendogram
- Map (bubble, heat)
- Stacked chart
- Venn diagram
- Seaborn
- Activity
Day 5
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Data visualisation with Python and MySQL
- Group work: create a senior management data visualisation presentation using ITDI local ULIMS data
- Presentation of output
Requirements
- An understanding of data structures.
- Experience with programming.
Audience
- Programmers
- Data scientists
- Engineers
35 Hours
Testimonials (1)
Trainer was accommodative. And actually quite encouraging for me to take up the course.
