Python for Data Analysis and Automation Course

Lesson 1 • Functions and Modular Code

Define reusable functions with parameters, return values, and docstrings. Modular code reduces duplication and makes data pipelines easier to maintain.

Lesson 2 • Setting Up Your Python Environment

Install Python, configure virtual environments, and choose an IDE suited for data work. This foundation ensures reproducible, conflict-free project setups.

Lesson 3 • Core Python Syntax and Data Types

Master variables, operators, and built-in data types including strings, integers, floats, and booleans. These primitives underpin every data manipulation task ahead.

Lesson 4 • Collections: Lists, Tuples, Dicts, Sets

Understand Python's four core collection types and when to use each. Efficient collection use directly accelerates data storage and retrieval patterns.

Lesson 1 • Ingesting JSON and XML Data

Load nested JSON and XML structures into Python objects for further processing. These formats dominate API responses and configuration files.

Lesson 2 • Error Handling in Data Ingestion

Apply try/except blocks to catch encoding errors, missing files, and malformed records. Robust ingestion code prevents silent data corruption in pipelines.

Lesson 3 • Reading and Writing Text Files

Use Python's built-in open() function to read, write, and append plain text files. Proper file handling prevents data loss and resource leaks.

Lesson 4 • Reading Excel and Other Formats

Import multi-sheet Excel workbooks and other binary formats using openpyxl and pandas. Excel remains a primary data source in most business workflows.

Lesson 1 • Vectorized Operations and Broadcasting

Apply element-wise math and broadcasting rules to replace slow Python loops. Vectorization is the primary performance technique in numerical Python code.

Lesson 2 • NumPy Arrays and Array Creation

Create and inspect ndarrays from lists, ranges, and random generators. Arrays are the core data structure for all numerical and scientific Python libraries.

Lesson 3 • Statistical and Linear Algebra Functions

Use np.linalg and statistical functions for matrix operations and data summarization. These capabilities underpin machine learning preprocessing and scientific computing.

Lesson 1 • Grouping, Aggregating, and Pivoting

Summarize data with groupby(), agg(), and pivot tables to reveal patterns across categories. These operations power most business reporting and KPI calculations.

Lesson 2 • DataFrame and Series Fundamentals

Create DataFrames from dicts, lists, and files, and understand the Series structure. These objects are the primary containers for all pandas operations.

Lesson 3 • Selecting, Filtering, and Slicing Data

Extract subsets of data using boolean masks, query(), and column selection. Precise selection is essential before any transformation or analysis step.

Lesson 4 • Merging, Joining, and Concatenating

Combine multiple DataFrames using merge(), join(), and concat() to integrate data from different sources. Multi-source integration is a daily task in data roles.

Lesson 1 • Plotting with Matplotlib

Build line, bar, scatter, and histogram charts using matplotlib's object-oriented API. Matplotlib provides full control over every visual element in a figure.

Lesson 2 • Customizing and Exporting Charts

Apply themes, color palettes, annotations, and export charts to PNG or PDF for reports. Polished visuals increase stakeholder trust in analytical findings.

Lesson 3 • Statistical Visualization with Seaborn

Create distribution plots, heatmaps, and categorical charts with seaborn's high-level API. Seaborn accelerates EDA by combining statistics and aesthetics automatically.

Lesson 4 • Statistical Profiling of Datasets

Compute descriptive statistics, distributions, and correlations to understand dataset structure. Profiling guides every subsequent cleaning and modeling decision.

Lesson 1 • File System Automation

Navigate, create, copy, move, and delete files and directories programmatically. File system automation is the most common entry point for workplace Python scripts.

Lesson 2 • Sending Automated Email Notifications

Send emails with attachments using smtplib and email libraries to notify stakeholders. Automated alerts close the loop between data processing and business action.

Lesson 3 • Command-Line Interfaces for Scripts

Build user-friendly CLI tools with argparse so scripts accept runtime parameters. CLI interfaces make automation scripts reusable across different datasets and environments.

Lesson 4 • Scheduling and Running Scripts

Schedule Python scripts to run automatically using cron, Task Scheduler, and APScheduler. Scheduled automation ensures consistent, timely data processing without manual triggers.

Lesson 1 • HTTP Fundamentals and the Requests Library

Understand HTTP methods, status codes, and headers, then make GET and POST requests. This knowledge is required before consuming any web-based data source.

Lesson 2 • Authenticating with APIs

Implement API key, OAuth2, and token-based authentication to access protected endpoints. Most production APIs require authentication before returning data.

Lesson 3 • Storing and Updating API Data

Persist API responses to CSV, JSON, or a database and implement incremental update logic. Durable storage prevents redundant API calls and preserves historical data.

Lesson 4 • Paginating and Rate-Limiting API Calls

Handle paginated responses and respect rate limits to collect complete datasets reliably. Ignoring pagination or rate limits causes incomplete or blocked data pulls.

Lesson 1 • Loading Query Results into pandas

Use pd.read_sql() and SQLAlchemy to pull query results directly into DataFrames. Bridging SQL and pandas enables seamless analysis of database-stored data.

Lesson 2 • Introduction to NoSQL with Python

Connect to document and key-value stores using pymongo and redis-py for non-tabular data. NoSQL databases are common in modern data stacks alongside relational systems.

Lesson 3 • Connecting Python to SQL Databases

Use sqlite3, psycopg2, and SQLAlchemy to open connections and execute queries from Python. Connection management is the first step in any database-driven automation.

Lesson 4 • Executing CRUD Operations

Run SELECT, INSERT, UPDATE, and DELETE statements from Python and handle transactions. CRUD operations are the building blocks of all database-backed data pipelines.