Python for Data Science: Real Projects & Analytics

EDUCBA via Coursera Specialization

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Overview

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This Specialization provides a complete, hands-on pathway to mastering Python for data science. Learners begin by analyzing datasets, visualizing results, and applying statistical methods before progressing into advanced programming, supervised machine learning, and time series forecasting. With practical, project-based training, you will bridge theory with application—gaining the confidence to design, implement, and evaluate data-driven solutions. Ideal for aspiring data scientists, analysts, and professionals seeking practical skills for industry success.

Syllabus

Course 1: Data Science with Python: Analyze & Visualize
Course 2: Statistics for Data Science with Python
Course 3: Advanced Python for Data Analysis: Build & Optimize
Course 4: Python: Logistic Regression & Supervised ML
Course 5: Python: Apply & Evaluate Sales Forecasting with Time Series

Courses

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5 hours 12 minutes
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This practical, hands-on course equips learners with the skills to analyze, build, and evaluate sales forecasting models using advanced time series techniques in Python. Designed for learners with foundational Python skills, the course progresses from preprocessing raw time series data to implementing complex forecasting models including SARIMA and Facebook Prophet. Learners begin by preparing data through structured preprocessing, feature engineering, and time series decomposition to uncover patterns and trends. The course then guides learners in training and statistically evaluating SARIMA models, validating model performance, and visualizing predictions. Through real-world comparisons of multiple datasets and categories, learners explore advanced model evaluation methods. The second half of the course focuses on the Prophet library, where learners will construct, visualize, and critically assess forecasts using Prophet’s intuitive capabilities for modeling trend, seasonality, and holidays. By the end of the course, learners will be able to apply statistical reasoning, build robust forecasting models, compare prediction strategies, and visualize results to support data-driven sales decisions.
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4 hours 39 minutes
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This hands-on course equips learners with the foundational knowledge and practical skills required to build and evaluate supervised machine learning models using Python. Designed around the real-world Titanic dataset, the course walks learners through the complete machine learning pipeline—from project setup and lifecycle understanding to model deployment readiness. In Module 1, learners will define the machine learning project structure, identify essential Python libraries such as NumPy and pandas, and understand the conceptual foundations of algorithms including Decision Trees and Logistic Regression. In Module 2, learners will apply exploratory data analysis techniques, clean and prepare datasets, and construct engineered features. They will also evaluate their models using metrics such as confusion matrices and cross-validation to improve model reliability and generalization. By the end of this course, learners will be able to independently implement supervised learning models on real datasets and interpret results with confidence.
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11 hours 48 minutes
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By the end of this course, learners will be able to apply advanced Python techniques, implement client-server networking, develop chatbot applications, integrate databases, and optimize data analysis with NumPy. Through hands-on lessons, you will analyze datasets, design efficient programs, construct socket-based applications, and execute SQL queries in Python. This course is designed to bridge the gap between intermediate Python knowledge and professional data analysis applications. You will gain practical experience with PyCharm, explore real-time communication through networking, and master database integration for managing client data. The course also emphasizes high-performance computing with NumPy, from array creation to matrix operations and vectorized computations. What makes this course unique is its blended approach to Python, combining development environments, networking, chatbot building, database integration, and advanced data analysis into one complete package. By completing this course, learners will develop the technical skills and confidence to design scalable, real-world Python solutions for data-driven projects.
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7 hours 59 minutes
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By completing this course, learners will be able to apply Python programming to analyze datasets, construct compelling visualizations, evaluate statistical measures, and implement machine learning techniques to generate actionable insights. You will develop hands-on skills in Python scripting, create reusable libraries, build functions, and preprocess data for accurate analysis. Learners will also construct charts, scatter plots, histograms, and box plots, evaluate probabilities and hypotheses, and implement regression and optimization models using gradient descent. This course benefits anyone aiming to advance a career in data science, analytics, or business intelligence, providing practical, project-based learning experiences. Unlike generic tutorials, this program integrates Python foundations with real-world statistical methods, Bayesian inference, and applied machine learning workflows. The structured approach—spanning Python basics to advanced analysis—ensures learners can confidently interpret data, validate assumptions, and present findings with clarity.
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7 hours 3 minutes
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By the end of this course, learners will be able to summarize datasets using descriptive statistics, visualize distributions with Python, evaluate probabilities, test hypotheses, and build regression models for predictive analysis. This hands-on training equips learners with the ability to apply statistical thinking to real-world data science projects, ensuring they can analyze, interpret, and present data effectively. The course begins with the foundations of data science and descriptive statistics, covering measures of central tendency, dispersion, correlation, and visualizations using histograms. Learners will then advance into probability and hypothesis testing, mastering concepts such as exclusive events, p-values, test statistics, and error types. Finally, the course culminates in regression and model building, where learners fit models, analyze outputs, evaluate residuals, and apply advanced curve-fitting techniques. What makes this course unique is its practical integration of Pandas and NumPy with statistical theory, enabling learners to not only understand the concepts but also implement them directly in Python. With structured modules and guided exercises, this course bridges the gap between statistical foundations and applied data science, preparing learners for advanced analytics, machine learning, and data-driven decision-making.