CareerSwerve | Online Data Training Platform | Python Core – Foundations for Data Careers (Self Study)

Course / Course Details

Python Core – Foundations for Data Careers (Self Study)

By - Emmanuel Edegbo
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Course Curriculum

15 chapters
108 lectures
0 quizzes
N/A total length

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1 Learning Objectives

Preview

1 Min

2 What Is Python?

Preview

5 Min

3 Python Roles Across Professions

Preview

2 Min

4 The Two Datasets

Preview

3 Min

1 Learning Objectives

Preview

1 Min

2 Your First Python Script

5 Min

3 Installing Python

3 Min

4 Setting Up VS Code

5 Min

5 Virtual Environments

5 Min

6 Jupyter Notebook

2 Min

7 Hands-on Lab

15 Min

8 Module Readiness Check

5 Min

1 Learning Objectives

Preview

1 Min

2 Variables

5 Min

3 Core Data Types

15 Min

4 F-Strings

8 Min

5 Hands-on Lab

15 Min

6 Professional Challenge

10 Min

7 Module Readiness Check

8 Min

1 Learning Objectives

1 Min

2 Arithmetic Operators

5 Min

3 Comparison Operators

5 Min

4 Logical Operators

10 Min

5 Operator Precedence

3 Min

6 Augmented Assignment

8 Min

7 Professional Challenge

10 Min

8 Hands-on Lab

25 Min

9 Module Readiness Check

8 Min

1 Learning Objectives

Preview

1 Min

2 if/elif/else

10 Min

3 Multi-Condition Logic

10 Min

4 Ternary Expressions

7 Min

5 Reusable Classification Functions

10 Min

6 Professional Challenge

3 Min

7 Hands-on Lab

12 Min

8 Module Readiness Check

10 Min

1 Learning Objectives

Preview

1 Min

2 Lists

20 Min

3 Tuples

10 Min

4 Hands-on Lab

15 Min

5 Professional Challenge

10 Min

6 Module Readiness Check

15 Min

1 Learning Objectives

Preview

1 Min

2 Dictionaries

20 Min

3 Sets

10 Min

4 Hands-on Lab

25 Min

5 Professional Challenge

30 Min

6 Module Readiness Check

20 Min

1 Learning Objectives

1 Min

2 The for Loop

10 Min

3 enumerate() and zip()

12 Min

4 break, continue, pass

8 Min

5 while Loops

6 Min

6 List Comprehensions

20 Min

7 Aggregation Pattern (GROUP BY in Python)

20 Min

8 Git Integration - Starting Now

10 Min

9 Hands-on Lab

20 Min

10 Professional Challenges

20 Min

11 Module Readiness Check

25 Min

1 Learning Objectives

1 Min

2 Defining Functions

15 Min

3 Parameters - Positional, Keyword, Default

12 Min

4 Returning Multiple Values

12 Min

5 Scope - The LEGB Rule

3 Min

6 Docstrings

15 Min

7 Pure Functions

8 Min

8 Hands-on Lab

25 Min

9 Professional Challenge

20 Min

10 Module Readiness Check

20 Min

1 Learning Objectives

1 Min

2 Case Methods

15 Min

3 Stripping

10 Min

4 Finding and Replacing

12 Min

5 Split and Join

12 Min

6 SQL Scalar Function Mapping

10 Min

7 Hands-on Lab

20 Min

8 Professional Challenge

20 Min

9 Module Readiness Check

10 Min

1 Learning Objectives

1 Min

2 The with Statement

8 Min

3 Reading Text Files

6 Min

4 Writing Text Files

10 Min

5 PathLib

15 Min

6 Safe Numeric Parsing - parse_float()

10 Min

7 CSV - csv.DictReader

25 Min

8 CSV - csv.DictWriter

25 Min

9 Complete Pipeline Pattern

30 Min

10 Hands-on Lab

30 Min

11 Professional Challenge

30 Min

12 Module Readiness Check

30 Min

1 Learning Objectives

Preview

1 Min

2 Exception Types

25 Min

3 Handling Errors in a Loop

25 Min

4 finally for Cleanup

15 Min

5 raise - Informative Exceptions

10 Min

6 Logging - A Better print()

15 Min

7 PEP 8 - Code Style

7 Min

8 Reading Tracebacks

6 Min

9 Hands-on Lab

35 Min

10 Professional Challenge

10 Min

11 Module Readiness Check

20 Min

1 Learning Objectives

1 Min

2 Standard Library vs Third-Party

20 Min

3 Hands-on Lab

30 Min

4 Professional Challenge

20 Min

5 Module Readiness Check

15 Min

1 Quick Reference Cheat Sheet

15 Min

1 What Comes Next - Python Applied

6 Min

Instructor

Emmanuel Edegbo

Emmanuel Edegbo is a highly experienced Lead Data Consultant with a strong track record of delivering data-driven solutions in complex, enterprise environments. With hands-on experience across analytics, data engineering, and cloud-based data platforms, Emmanuel has worked extensively with technologies including SQL, Python, data modelling, Databricks, Spark, and Microsoft Azure.

His professional background spans designing scalable data models, building robust data pipelines, and transforming raw data into actionable insights that support business decision-making. Emmanuel brings a practitioner’s perspective to learning, combining technical depth with real-world context drawn from working in high-impact, data-intensive organisations.

His experience cuts across manufacturing, retail, oil and gas, insurance, Ed-Tech, Insure-Tech, and government sectors.

As an instructor on CareerSwerve, Emmanuel is passionate about helping learners build practical, job-ready skills. His teaching style emphasises clarity, hands-on practice, and structured progression guiding learners from foundational concepts through to advanced, industry relevant applications. He is committed to empowering aspiring data and AI professionals to gain confidence, think critically, and succeed in today’s data-driven roles.

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