As I approach the climax of my Data Analytics course
@TechSphereAcad with
@ezekiel_aleke
This is what I covered in Week 11:
→ Introduction to Python: Overview of Python and its uses in data analysis
→ Introduction to coding environments: Google Colab, Jupyter Notebook, Anaconda, VS Code, etc.
→ Python is a high-level programming language used to write instructions for computers, designed to be easy to read and understand.
→ Python is widely used for data analysis, automation, web development, and machine learning. It allows users to work with data, perform calculations, and build applications
→ Python in data analytics is used to:
a. Clean data
b. Analyze data
c. Visualize data
d. Build data models
→ Basic Python Concepts
a. Variables: used to store data
b. Data types: include strings (texts), integers (numbers), float (decimals), & Boolean (Yes/No, True/False)
c. Operators: perform calculations
d. Functions: it's like mini programs; they help you organize code to be reused easily
Then at the weekend class, we looked at:
→ Career development and job preparation
→ I was exposed to tools like Canva, MS Word, & Kickresume to create a resume
→ How to build a portfolio using HTML & CSS
→ Code editing using Visual Studio coding environment
→ Where to find data analytics job opportunities and how to tailor every resume to work experience and match different job descriptions
What a ride it has indeed been—learning, unlearning, and stretching in my data analytics journey
@TechSphereAcad. I'm grateful for this experience and excited about what lies ahead.
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#Day21