Python
Data Analytics and
Engineering With Python
To provide participants with the knowledge and practical skills to efficiently gather, process, analyze, and visualize data using Python
5
11 enrolled students
Objective
Enabling them to build data pipelines and perform analytics that drive informed decision-making in various business and technical contexts.This objective emphasizes both the data analytics and engineering aspects of the course, focusing on practical skills for data processing and analysis.
Basic To Advance
You will progress through this course from basics to advanced level.
Duration
3 Months
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Modules
Module 1: Introduction to Data Analytics and Data Engineering
Topics:
- Overview of Data Analytics and Data Engineering
- Roles and Responsibilities in Data Projects
- Understanding Data Pipelines and Workflow
Hands on exercises:
- Practical programs
Module 2: Python Programming Fundamentals (for Data)
Topics:
- Data Types, Variables, and Operators
- Control Flow (Loops, Conditionals)
- Functions and Lambda Functions
- Working with Modules and Libraries
Hands on exercises:
- Practical programs
Module 3: Data Handling and Manipulation with Pandas
Topics :
- DataFrames and Series Basics
- Data Cleaning and Preprocessing
- Filtering, Sorting, and Grouping Data
- Merging, Joining, and Concatenating DataFrames
- Handling Missing Data
Hands on exercises:
- Practical programs
Module 4: Data Wrangling and Transformation
Topics :
- Data Transformation Techniques
- Reshaping and Pivoting Data
- Feature Engineering
- Data Encoding and Scaling
Hands on exercises:
- Practical programs
Module 5: Working with Databases (SQL and NoSQL)
Topics :
- Introduction to SQL and Relational Databases
- CRUD Operations (Create, Read, Update, Delete)
- Joins, Aggregations, and Subqueries
- Introduction to NoSQL Databases (e.g., MongoDB)
- Connecting Python to Databases (using sqlite3, SQLAlchemy, pymongo)
Hands on exercises:
- Practical programs
Module 6: Data Collection and Web Scraping
Topics :
- Introduction to Web Scraping with BeautifulSoup and Scrapy
- Accessing APIs with requests
- Handling JSON and XML Data
- Automating Data Collection
Hands on exercises:
- Practical programs
Module 7: Data Engineering Basics
Topics :
- Data Pipeline Architecture
- Batch vs. Stream Processing
- Data Ingestion Techniques
- Introduction to ETL (Extract, Transform, Load) Processes
- Scheduling and Automating Workflows (e.g., with Apache Airflow)
Hands on exercises:
- Practical programs
Module 8: Data Storage and Cloud Integration
Topics :
- File Formats (CSV, JSON, Parquet, etc.)
- Introduction to Cloud Data Storage (AWS S3, Google Cloud Storage)
- Connecting Python to Cloud Storage Services
- Best Practices in Data Storage and Retrieval
Hands on exercises:
- Practical programs
Module 9: Data Visualization
Topics :
- Introduction to Data Visualization Principles
- Plotting with Matplotlib and Seaborn
- Creating Interactive Dashboards with Plotly
- Using Pandas Visualization Features
- Storytelling with Data
Hands on exercises:
- Practical programs
Module 10: Exploratory Data Analysis (EDA)
Topics:
- Descriptive Statistics
- Data Distribution and Summary
- Correlations and Relationships in Data
Detecting Outliers and Anomalies
Hands on exercises:
- Practical programs
Module 11: Introduction to Machine Learning for Data Analytics
Topics:
- Basics of Machine Learning Concepts
- Introduction to Supervised and Unsupervised Learning
- Building Simple Predictive Models (e.g., Regression, Classification)
- Model Evaluation Metrics
Hands on exercises:
- Practical programs
Module 12: Big Data Tools and Frameworks (Introductory)
Topics:
- Introduction to Big Data Concepts
- Overview of Apache Spark for Data Processing
- Working with PySpark DataFrames
- Processing Large Datasets in Python
Hands on exercises:
- Practical programs
Module 13: Data Pipeline Deployment and Monitoring
Topics:
- Deploying Data Pipelines (e.g., Docker, Cloud Platforms)
- Monitoring Data Pipelines for Performance
- Error Handling and Logging
- Version Control and Pipeline Maintenance
Hands on exercises:
- Practical programs
Module 14: Final Project
Final project focuses on building an end-to-end Data Analytics and Engineering Project with Integrating Data Collection, Storage, Processing, Visualization and Presenting Insights and Recommendations.
1. Customer Segmentation for E-commerce
- Build an end-to-end pipeline that collects, cleans, and analyzes customer data for an e-commerce company. Apply clustering techniques to segment customers based on purchasing behavior and demographics, and visualize insights in a dashboard. This project uses data wrangling, database integration, ETL processes, and machine learning.
2. Real-Time Stock Price Monitoring and Analysis System
Create a real-time data pipeline that ingests stock price data from an API, stores it in a database, and performs continuous analysis for trends and alerts. Integrate a web dashboard for visualization and automated notifications. This project involves data ingestion, stream processing, data storage, and visualization.
3. Sales Forecasting and Inventory Management
- Develop a system that analyzes historical sales data to forecast future demand. Use predictive models to anticipate inventory needs and build visualizations to support decision-making. This project covers ETL, data engineering, exploratory data analysis, machine learning, and dashboard creation.
4. Air Quality Monitoring and Prediction System
- Build a data pipeline to collect and process air quality data from various sources (APIs, sensors). Perform analysis to understand trends and apply machine learning models to predict future air quality levels in different regions. This project uses data collection, ETL, storage, predictive analytics, and visualization.
5. Product Recommendation Engine for an Online Retailer
- Design a recommendation system that suggests products based on users’ previous purchases or browsing history. Create a pipeline to process and analyze user behavior data, train recommendation algorithms, and visualize the recommendations. This project involves data engineering, collaborative filtering, and visualization.
Each of these projects is designed to combine both data analytics and data engineering skills, providing practical experience with real-world data workflows, processing, and analysis.
Frequently Asked Questions
1. Who is this course for?
This course is ideal for:
- Beginners looking to start a career in data analytics or engineering.
- Professionals seeking to enhance their Python skills for data-centric roles.
- Anyone interested in leveraging data to make informed decisions.
2. What tools and libraries will I learn?
You’ll learn:
- Pandas, NumPy, and Matplotlib for data analysis and visualization
- Seaborn for advanced visualizations
- SQL for database integration
- PySpark and Hadoop for big data processing
- Jupyter Notebooks for coding and documentation
3. How will this course benefit my career?
This course equips you with skills in Python, data analysis, and engineering, preparing you for roles like:
- Data Analyst
- Data Engineer
- Business Intelligence Developer
- Machine Learning Engineer
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