Data Science in Python – Data Science Academy

About Course

Your first step toward a career in Data Analysis and AI.

Course Overview: Before you can analyze Big Data or build AI models, you must master the language they are written in. This course is not just a generic Python tutorial; it is a specialized bootcamp designed to take you from a complete beginner to a confident Python programmer capable of handling complex data.

We strip away the fluff and focus on the core programming concepts essential for Data Science. From setting up a professional coding environment to mastering Object-Oriented Programming (OOP), this course builds the solid foundation you need to tackle libraries like Pandas, NumPy, and Scikit-Learn in the next stage of your journey.

❓ Why This Course?

Data-First Approach: Every concept is taught with an eye toward how it applies to data manipulation and analysis.
Professional Tooling: Learn to write code not just in a browser, but in professional IDEs like VS Code, mirroring a real job environment.
From Scripting to Engineering: We don’t stop at simple scripts. You will learn OOP (Object Oriented Programming), which is critical for building scalable data pipelines.

Master Python from Scratch to Professional Level Build a rock-solid foundation in Python programming, moving from basic syntax and loops to advanced Object-Oriented Programming (OOP) and functional coding patterns used by senior developers.
Wrangle and Clean Messy Data Become an expert in the Pandas library to ingest, clean, merge, and reshape complex datasets. You will learn to handle missing values, fix data types, and prepare raw data for analysis.
Create Stunning Data Visualizations Learn to tell stories with data using Matplotlib and Seaborn. You will generate professional line charts, heatmaps, and distribution plots to uncover hidden trends and insights.
Build a Real-World Portfolio with 8+ Case Studies Apply your skills immediately by solving real business problems, including analyzing E-commerce sales, predicting bank customer behavior, and exploring NYC public school performance.
Perform Exploratory Data Analysis (EDA) Develop the "Data Scientist Mindset." Learn the systematic process of investigating new datasets, identifying outliers, and discovering patterns using statistical functions and ydata-profiling.
Master Advanced Data Handling Techniques Go beyond the basics by learning to work with Regular Expressions (Regex) for text pattern matching, handle complex Date & Time data, and import data from JSON, SQL, and CSV sources.
Develop Critical Data Communication Skills Learn the soft skills missing from most technical courses: how to structure reports, build compelling presentations, and communicate your technical findings to non-technical stakeholders effectively.
Automate and Optimize Workflow Write efficient, reusable code using decorators, context managers, and advanced functions to automate repetitive data tasks and build scalable analysis pipelines.

Course Content

Python Fundamentals
Covers "Python Basics," "Data Structures," "OOP," "Modules," and "File Handling."

Introduction

Python Language Fundamentals

Writing your first program in Python
Variables and Basic Data Structures
Operators in Python
Strings in Python
Lists and Tuples in Python
Dictionaries in Python
Sets in Python
Using Code Editors to write source code
Code Blocks and Indentation in Python
Controlling Program Flow using Conditional Statements
Controlling Program Flow using Loops
List and Dictionary Comprehensions
Functions in Python
Classes and Object-Oriented Programming (OOP)
The Four Pillars of OOP – Part One
The Four Pillars of OOP – Part Two

Modules and Packages

Dealing with Text Files

Regular Expressions

Data Analysis

Dealing with Data and Statistics (Pandas)

Introduction to Pandas Library
Understanding CSV Files
Handling CSV Files using Pandas:
Understanding JSON Files
Handling JSON Files using Pandas
Merging DataFrames
Reshaping DataFrames
Selecting Data from DataFrames
Iterating over Row and Column Values
Cleaning DataFrame Columns
Data Discovery
Data Cleaning
Introduction to Statistical Functions
Standard Deviation (Std) and Mode
Comparing Student Grades using Statistical Functions
Descriptive Statistics Function (describe)
Grouping Data using groupby
Correlations between Column Values

Data Visualization via Pandas

Data Visualization via Matplotlib

Data Visualization via Seaborn
Introduction to Seaborn: Creating statistical graphics with less code and better aesthetics.

Case Study – Analyzing Student Performance

Case Study – E-commerce Sales Strategy

Case Study – Customer Behavior Analysis

Case Study – Marketing Strategy Analysis

Case Study – Bank Marketing Prediction

Case Study – Car Sales Analysis

PROJECT BONUS – NYC Public Schools

Advanced Data Skills & EDA

Working with Categorical Data

Data Communication Concepts

Introduction to Importing Data

Cleaning Data in Python

Working with Dates and Times

Writing Functions in Python

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About Course

Your first step toward a career in Data Analysis and AI.

❓ Why This Course?

What Will You Learn?

Course Content

Python Fundamentals Covers "Python Basics," "Data Structures," "OOP," "Modules," and "File Handling."

Introduction

Python Language Fundamentals

Writing your first program in Python

Variables and Basic Data Structures

Operators in Python

Strings in Python

Lists and Tuples in Python

Dictionaries in Python

Sets in Python

Using Code Editors to write source code

Code Blocks and Indentation in Python

Controlling Program Flow using Conditional Statements

Controlling Program Flow using Loops

List and Dictionary Comprehensions

Functions in Python

Classes and Object-Oriented Programming (OOP)

The Four Pillars of OOP – Part One

The Four Pillars of OOP – Part Two

Modules and Packages

Modules in Python

Packages in Python

The Package Manager (pip)

Dealing with Text Files

Dealing with Text Files in Python – Part One

Dealing with Text Files in Python – Part Two

Reading Structured Data in JSON Files

Regular Expressions

Regular Expressions

Practical Examples of Using Regular Expressions

Regular Expressions in Python

Data Analysis

Track Introduction:

Introduction to Data Analysis

Setting up the Work Environment:

Dealing with Data and Statistics (Pandas)

Introduction to Pandas Library

Understanding CSV Files

Handling CSV Files using Pandas:

Understanding JSON Files

Handling JSON Files using Pandas

Merging DataFrames

Reshaping DataFrames

Selecting Data from DataFrames

Iterating over Row and Column Values

Cleaning DataFrame Columns

Data Discovery

Data Cleaning

Introduction to Statistical Functions

Standard Deviation (Std) and Mode

Comparing Student Grades using Statistical Functions

Descriptive Statistics Function (describe)

Grouping Data using groupby

Correlations between Column Values

Data Visualization via Pandas

Introduction to Data Visualization

Histogram

Density Plot and Box Plot

Normal Distribution of Data

Data Visualization via Matplotlib

Introduction to Matplotlib Library

Line and Bar Charts

Pie Chart

Subplots

Scatter Plots

Visualizing Correlations between Columns

Data Visualization via Seaborn Introduction to Seaborn: Creating statistical graphics with less code and better aesthetics.

Introduction to Seaborn Library

Line Plot

Line Plot for Multiple Variables

Bar Plot

Count Plot

Scatter Plots

Box Plot

Case Study – Analyzing Student Performance

Python Fundamentals
Covers "Python Basics," "Data Structures," "OOP," "Modules," and "File Handling."

Data Visualization via Seaborn
Introduction to Seaborn: Creating statistical graphics with less code and better aesthetics.