Experienced Python professional and skills trainer offering hands-on, practical lessons focused on real-world, data-driven solutions.

Details

Audience:
Level 1: For students and beginners without programming experience.
Level 2: For those familiar with Python basics but want to dive deeper.
Level 3: For aspiring professional analysts and data scientists.

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Level 1: Beginner (RM20 per session)
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Goal: Build a foundation in Python for absolute beginners with an analyst-oriented approach.

Topics to Cover:

Introduction to Python for Data Analysis

Why Python for analysts?
Installing Python and setting up IDEs (Jupyter Notebook, VS Code).
Python Basics

Variables, Data Types, and Operators.
Input and Output.
Control Flow

If-Else statements.
Loops (For and While).
Functions and Modules

Creating and calling functions.
Using built-in modules for analysis.
Working with Data Structures

Lists, Tuples, Dictionaries, and Sets.
Introduction to Libraries for Analysts

Overview of pandas, numpy, and matplotlib.
Mini-Project:

Simple data cleaning and basic visualization.

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Level 2: Intermediate (RM40 per session)
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Goal: Introduce data handling, cleaning, and basic analytics.

Topics to Cover:

Data Handling with pandas

Reading and writing data (CSV, Excel).
Indexing and slicing dataframes.
Data Cleaning

Handling missing values.
Data type conversion and formatting.
Data Manipulation

Aggregation and grouping data.
Merging and joining datasets.
Data Visualization

Creating charts with matplotlib and seaborn.
Customizing plots for better storytelling.
Introduction to Statistics for Analysts

Mean, Median, Mode, and Standard Deviation.
Correlation and simple regression analysis.
Working with APIs

Fetching data from APIs using requests.
Intermediate Automation

Automating repetitive tasks (e.g., file handling).
Mini-Project:

Analyze and visualize a real-world dataset (e.g., sales or customer data).

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Level 3: Advanced (RM60 per session)
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Goal: Train students to perform professional-level analysis and create reusable Python workflows.

Topics to Cover:

Advanced Data Analysis Techniques

Pivot tables and advanced aggregation in pandas.
Advanced filtering and conditional logic.
Exploratory Data Analysis (EDA)

Identifying trends and anomalies.
Advanced visualization techniques.
Introduction to Machine Learning with Python

Basics of sklearn for analysts.
Predictive analysis: regression and classification.
Time Series Analysis

Working with datetime objects.
Basic forecasting techniques.
Database Integration

Connecting Python to SQL databases.
Querying and handling large datasets.
Building Dashboards with Plotly Dash

Interactive visualizations for data storytelling.
Version Control and Collaboration

Introduction to Git and GitHub for analysts.
Mini-Project:

End-to-end analysis project, including data sourcing, cleaning, analysis, and presentation of insights.

Structure:
Each level contains 8 sessions of 1.5-2 hours.
Hands-on exercises and real-world datasets.