PROFICIENCY IN USING GOOGLE COLAB TO HANDLE VARIOUS DATA ANALYSIS AND VISUALIZATION TASKS

Live-Online Training: N250,000
Classroom Training: N350,000
3 - 4 participants: 5% discount
5 or more participants: 10% discount

(Available also for Customised Training by Duration, Venue & Fee)

Course Description:

This intensive 5-day training course is designed to equip participants with essential skills in Python programming, leveraging Google Colab as a powerful tool for data analysis and visualization. Participants will learn to manipulate datasets, perform statistical analysis, and create insightful visualizations using popular Python libraries such as Pandas and Numpy. By the end of the course, participants will be proficient in using Google Colab to handle various data analysis tasks efficiently.

Course Objectives:

• Understand Python Basics: Gain a solid foundation in Python programming concepts necessary for data analysis.
• Master Google Colab: Learn to utilize Google Colab's features for efficient coding, collaboration, and GPU support.
• Data Manipulation: Acquire skills to import, clean, and manipulate datasets using Pandas.
• Statistical Analysis: Perform descriptive statistics on data using Python.
• Data Visualization: Create clear and compelling visualizations using Matplotlib.
• Practical Applications: Apply learned concepts to real-world datasets and scenarios.
• Collaborative Work: Understand how to share and collaborate on Google Colab notebooks.

 Course Content:

Day 1:

 Introduction to Python Basics and Google Colab

•       Introduction to Python programming language

•       Setting up Google Colab environment

•       Basic operations and data types in Python

•       Introduction to Jupyter notebooks

Day 2: 

Data Handling with Pandas

•       Importing and exporting data in Google Colab

•       Data exploration and manipulation with Pandas

•       Handling missing data and duplicates

•       Grouping, aggregating, and transforming data

Day 3:

 Statistical Analysis with Python

•       Descriptive statistics: mean, median, mode etc.

•       Introduction to probability distributions

•       Correlation and regression analysis

Day 4:

 Data Visualization with Matplotlib 

•       Introduction to data visualization principles

•       Creating static and interactive plots with Matplotlib

•       Customizing plots: labels, colors, annotations

Day 5:

 Advanced Topics and Applications

•       Best practices for sharing and collaborating on Colab notebooks

•       Case studies and practical examples