Python in Data Science Training Course

Day 1

1. Data Science
2. Data Science Team Composition (Data Scientist, Data Engineer, Data Visualizer, Process Owner)
3. Business Intelligence
   1. Types of Business Intelligence
   2. Developing Business Intelligence Tools
   3. Business Intelligence and the Data Visualization
4. Data Visualization
   1. Importance of Data Visualization
   2. The Visual Data Presentation
   3. The Data Visualization Tools (infographics, dials and gauges, geographic maps, sparklines, heat maps, and detailed bar, pie and fever charts)
   4. Painting by Numbers and Playing with Colors in Making Visual Stories
5. Activity

Day 2

1. Data Visualization in Python Programming
   1. Data Science with Python
   2. Review on Python Fundamentals

1. Variables and Data Types (str, numeric, sequence, mapping, set types, Boolean, binary, casting)
2. Operators, Lists, Tuples. Sets, Dictionaries
3. Conditional Statements
4. Functions, Lambda, Arrays, Classes, Objects, Inheritance, Iterators
5. Scope, Modules, Dates, JSON, RegEx, PIP
6. Try / Except, Command Input, String Formatting
7. File Handling

2. Activity

Day 3

1. Python and MySQL

1. Creating Database and Table
2. Manipulating Database (Insert, Select, Update, Delete, Where Statement, Order by)
3. Drop Table
4. Limit
5. Joining Tables
6. Removing List Duplicates
7. Reverse a String

2. Data Visualization with Python and MySQL
   1. Using Matplotlib (Basic Plotting)
   2. Dictionaries and Pandas
   3. Logic, Control Flow and Filtering
   4. Manipulating Graphs Properties (Font, Size, Color Scheme)
3. Activity

Day 4

1. Plotting Data in Different Graph Format
   • 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
2. Activity

Day 5

1. Data Visualization with Python and MySQL
   1. Group Work: Create a Top Management Data Visualization Presentation Using ITDI Local ULIMS Data
   2. Presentation of Output