Learn Python for Data Science

About the Course: Python for Data Science

In a world driven by data, the ability to extract, clean, analyze, and visualize information is a superpower. This intensive 40-hour live training bootcamp from SEOMasters.DEV is your launchpad into the exciting field of Data Science. We take you on a hands-on journey from the absolute basics of Python programming to performing complex data analysis and building predictive models.

This is not a theoretical course. Through live coding sessions, practical exercises, and a capstone project, you will learn by doing. You'll master the essential tools that data scientists use every day, including Python, SQL, Pandas, NumPy, and Matplotlib, and learn how to apply them to solve real-world problems. By the end of this course, you will have the confidence and a portfolio-worthy project to prove your skills.

What You'll Learn?

Master Python Programming: Write clean, efficient Python code, understand Object-Oriented Programming (OOP), and handle errors gracefully.
Manipulate Data with Pandas: Confidently load, clean, transform, group, and merge diverse datasets.
Perform Numerical Computing with NumPy: Leverage the power of NumPy for high-performance scientific computing and array operations.
Query Databases with SQL: Write SQL queries to extract and filter data directly from databases within your Python environment.

Automate Data Collection: Build web scrapers using BeautifulSoup and Requests to gather data from websites.
Create Compelling Visualizations: Tell powerful stories with your data using Matplotlib and Seaborn to create insightful charts and plots.
Apply Statistical Concepts: Understand the basics of statistical analysis using libraries like SciPy.
Build an End-to-End Project: Complete a capstone project that showcases your ability to take a raw dataset and deliver meaningful insights.

Requirements

A computer (Windows, macOS, or Linux) with internet access.
No prior programming experience is needed. We start from scratch!
A motivated mindset and a desire to learn and solve problems.

Who Is This Course For?

This course is designed for anyone eager to step into the world of data. No prior programming or data science experience is required. It's a perfect fit for:

Aspiring Data Analysts & Scientists: Build the foundational skills you need to land your first job in the field.
Professionals & Career Changers: Marketers, financial analysts, business analysts, and others who want to leverage data in their current roles or transition into a data-focused career.
Students & Academics: Gain practical, in-demand programming skills that complement your studies.
Programmers: Developers familiar with other languages (like Java, C++, or JavaScript) who want to quickly learn Python for data applications.
Entrepreneurs & Business Owners: Learn how to make data-driven decisions to grow your business.

Topics for This Course

Here is a detailed, module-wise breakdown of the curriculum.

Topic 1: Introduction to Data Science
What is Data Science, Data Analysis, and Machine Learning? The Data Science Lifecycle, Why Python is the language of choice for Data Science.
Topic 2: Setting Up Your Professional Environment
Installing Anaconda and managing packages with Conda, Introduction to Jupyter Notebooks for interactive coding, Overview of IDEs like VS Code.
Topic 3: Python Programming Kickstart
Your first Python script: "Hello, World!", Variables and Core Data Types (Integers, Floats, Strings, Booleans), Working with Strings (formatting, methods, slicing), Basic Input/Output operations.

Topic 1: Python's Built-in Data Structures
Lists: Creating, indexing, slicing, methods (append, pop, etc.), Tuples: Immutability and when to use them, Dictionaries: Key-value pairs, accessing data, and methods, Sets: Unordered collections of unique items.
Topic 2: Logic and Control Flow
Conditional Statements (if, elif, else), for Loops: Iterating over sequences, while Loops: Looping based on conditions, break, continue, and List Comprehensions for concise code.
Topic 3: Functions and Modularity
Defining and calling your own functions, Parameters, arguments, and return values, Scope: Local vs. Global variables, Lambda (Anonymous) Functions.

Topic 1: Object-Oriented Programming (OOP) Concepts
The 'why' of OOP: Creating reusable and organized code, Classes and Objects (Attributes and Methods), The __init__ constructor, Inheritance and Polymorphism (Conceptual).
Topic 2: Exception Handling
Understanding errors and exceptions, Using try, except, else, and finally blocks to build resilient code.
Topic 3: File Handling
Reading from and writing to text (.txt) files, Working with structured files: CSV and JSON.

Topic 1: Numerical Python with NumPy
Introduction to the NumPy ndarray, Creating arrays, indexing, and slicing, Vectorization: The key to speed, Universal functions and mathematical/statistical operations.
Topic 2: Data Manipulation with Pandas - Part 1
The Pandas Series and DataFrame objects, Importing data from various sources (CSV, Excel), Inspecting a DataFrame (.head(), .tail(), .info(), .describe()), Selecting data: Indexing and slicing with .loc and .iloc, Conditional filtering.
Topic 3: Data Manipulation with Pandas - Part 2 (Data Cleaning)
Handling missing data (.isnull(), .dropna(), .fillna()), Dealing with duplicates, Changing data types (.astype()), Applying functions to columns and rows (.apply()).
Topic 4: Data Aggregation and Merging
The groupby() operation for powerful aggregations (sum, mean, count), Merging, joining, and concatenating DataFrames, Mini-Project: Cleaning and exploring a real-world dataset (e.g., Titanic or a Sales dataset).

Topic 1: SQL for Data Analysts
Introduction to relational databases, Writing essential SQL queries: SELECT, FROM, WHERE, Sorting (ORDER BY) and aggregating data (GROUP BY, COUNT, AVG), Combining tables with JOIN, Connecting Python to a database using pandas.read_sql().
Topic 2: Web Scraping with Python
Introduction to HTML structure for scraping, Using the requests library to fetch web page content, Parsing HTML with BeautifulSoup to find and extract data, Practical Exercise: Scraping a table or product information from a website, Discussing ethics and best practices in web scraping.

Topic 1: Foundational Plotting with Matplotlib
The anatomy of a Matplotlib plot, Creating essential plots: Line, Bar, Histogram, and Scatter plots, Customizing plots: Titles, labels, colors, and legends.
Topic 2: Advanced Statistical Visualization with Seaborn
Introduction to Seaborn for aesthetically pleasing plots, Creating advanced plots: Box plots, Violin plots, Heatmaps, and Pair plots, Visualizing relationships and distributions effectively, Mini-Project: Creating an insightful dashboard of visualizations.

Topic 1: The Machine Learning Landscape
Conceptual Overview: Supervised vs. Unsupervised Learning, Introduction to the scikit-learn library, the gold standard for ML in Python, The importance of SciPy for scientific and statistical functions.
Topic 2: Building Your First Predictive Model
The concept of training and testing data, Implementing a simple Linear Regression or Classification model, A brief look at model evaluation.
Topic 3: Capstone Project
Students will be given a complex, real-world dataset, Task: Apply all the learned skills to perform an end-to-end data analysis project ( Acquire & Load: Load the data into a Pandas DataFrame, Clean & Prepare: Handle missing values and prepare the data for analysis, Explore & Analyze: Use groupby and other methods to find initial insights, Visualize: Create meaningful plots to communicate findings, Conclude: Present a summary of key insights derived from the data.) Final session for project presentations and course wrap-up.