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Keep shoppingEnd-to-End Data Analytics: From Raw Data to Cloud Deployment
15 students
End-to-End Data Analytics: From Raw Data to Cloud Deployment
Build, Analyze, Deploy Real-World Data Analytics Projects –from Data Wrangling to Full-Scale Deployment
Created byBluelime Learning Solutions
Last updated 3/2026
English
What you'll learn
- Understand the Roles in Data Analytics: Distinguish between Data Analysts, Data Scientists, and Data Engineers.
- Understand the End-to-End Data Analytics Workflow
- Develop a Data-Driven Mindset: Make decisions based on data insights, statistical evidence, and predictive models.
- Extract and import data
- Clean and Preprocess Raw Data: Handle missing values, duplicates, and incorrect data types to prepare data for analysis.
- Transform and Normalize Data: Scale and normalize numerical features for machine learning models.
- Apply Feature Engineering: Create new features from raw data to improve model accuracy
- Create Data Visualizations: Generate bar charts, histograms, scatter plots, heatmaps, and pie charts to visualize patterns and relationships.
- Conduct Correlation Analysis: Identify and visualize the relationships between variables using correlation heatmaps.
- Use Pandas and NumPy for EDA: Use Pandas for filtering, sorting, and aggregating data, and NumPy for advanced calculations.
- Feature Encoding for Categorical Data: Apply techniques like One-Hot Encoding, Label Encoding, and Frequency Encoding for categorical variables.
- Apply Feature Scaling and Normalization: Scale features using MinMaxScaler or StandardScaler to prepare data for machine learning.
- Create Interaction Features: Generate new features by combining multiple features to create non-linear relationships.
- Understand the Basics of Machine Learning: Learn the difference between supervised, unsupervised, and reinforcement learning.
- Build and Train Machine Learning Models: Train and evaluate models like Logistic Regression
- Split Data into Training and Testing Sets: Use train-test split to ensure models generalize well to new data.
- Evaluate Model Performance: Use metrics like accuracy, precision, recall, and F1-score to evaluate models.
- Generate Visual Reports Using Python: Use Matplotlib, Seaborn, and Plotly to create interactive visual reports.
- Build a Flask Web Application: Use Flask to create web applications that display predictions and visualizations.
- Create RESTful APIs: Build simple REST APIs to accept inputs from users and return predictions from machine learning models
- Save and Load Trained Machine Learning Models: Save models using Pickle (.pkl files) and load them for use in Flask apps.
- Deploy Models as Web Applications: Deploy trained machine learning models to the cloud using Flask.
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Course content
9 sections • 80 lectures • 5h 54m total length
Requirements
- Basic Python Programming Skills
- Data Analysis and Visualization Basics
- Computer with internet connection.
Description
Are you ready to master the full lifecycle of data analytics and showcase a complete end-to-end project in your portfolio? This comprehensive, project-based course takes you on a journey from data collection to cloud deployment, giving you the technical skills, tools, and confidence to excel in the field of data analytics, machine learning, and model deployment.
In this course, you will learn how to collect, clean, analyze, model, and deploy data analytics projects using industry-leading tools like Python, Pandas, NumPy, Scikit-learn, Flask, and SQL. You'll gain hands-on experience with real-world datasets, develop essential machine learning skills, and learn how to create and deploy a live, interactive web application that showcases your work.
The course is designed to provide you with a complete, end-to-end experience, enabling you to work on a real-world project from scratch. By the end of the course, you will have a fully deployable web app that allows users to interact with your predictive model — a powerful project to add to your professional portfolio. This project will demonstrate your ability to handle every aspect of the data analytics workflow, from raw data ingestion to live model deployment.
This course follows a step-by-step, hands-on approach, making it suitable for beginners, aspiring data analysts, business analysts, and career changers. Each module covers a key phase of the data analytics process, starting with data collection and data wrangling, followed by exploratory data analysis (EDA), and progressing to machine learning model development and model evaluation. You’ll also learn Flask web development to transform your model into an interactive web application, and finally, you’ll deploy your project to the cloud using platforms like PythonAnywhere, AWS, or Heroku.
This course is packed with practical, hands-on exercises that reinforce every concept. You'll clean and analyze raw datasets, visualize patterns using Matplotlib and Seaborn, train predictive models like Logistic Regression, Decision Trees, and Random Forests, and deploy your project to the web. You’ll also learn how to manage model files, APIs, and web servers using Flask, enabling you to develop interactive prediction tools that can be accessed by users worldwide.
By the end of the course, you’ll be able to:
Build a portfolio-ready, end-to-end data analytics project.
Create a live web app that users can interact with in real-time.
Gain hands-on experience with data analysis, machine learning, and web development.
Showcase your ability to handle the entire data analytics lifecycle — a key skill that employers value in data analysts, data scientists, and business analysts.
If you’re ready to move beyond theory and work on real-world projects that showcase your data analytics skills, this course is for you. Sign up now and transform raw data into valuable insights, deploy machine learning models, and create web apps that make your analysis accessible to the world!
Who this course is for:
- Aspiring Data Analysts
- Entry-Level and Junior Data Analysts
- Career Changers and Transitioners
- Aspiring Data Scientists and Machine Learning Enthusiasts
- Data Enthusiasts and Freelancers
- Students and Recent Graduates