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.



Python for Data Science: Real Projects & Analytics Specialization
Master Python Data Science Techniques. Build real-world data science projects using Python, statistics, ML, and forecasting models.

Instructor: EDUCBA
Included with 
Recommended experience
Recommended experience
What you'll learn
Apply Python programming to analyze, visualize, and interpret real-world datasets.
Build, train, and evaluate supervised machine learning and forecasting models.
Integrate statistical methods with Python tools to create data-driven solutions.
Overview
Skills you'll gain
- Regression Analysis
- Database Management
- Data Visualization
- Data Science
- Forecasting
- Box Plots
- Supervised Learning
- Probability & Statistics
- Computer Networking
- Descriptive Statistics
- Time Series Analysis and Forecasting
- Statistical Analysis
- Statistics
- Statistical Hypothesis Testing
- Predictive Modeling
- Exploratory Data Analysis
- Histogram
- Feature Engineering
Tools you'll learn
What’s included

Add to your LinkedIn profile
October 2025
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from EDUCBA

Specialization - 5 course series
What you'll learn
Analyze datasets with Python scripting, functions, and libraries.
Visualize data using charts, scatter plots, histograms, and box plots.
Apply ML techniques like regression and gradient descent models.
Skills you'll gain
What you'll learn
Summarize datasets with descriptive stats and visualizations.
Apply probability concepts and test hypotheses with Python.
Build and evaluate regression models for predictive analysis.
Skills you'll gain
What you'll learn
Implement client-server apps, chatbots, and database integration.
Optimize data analysis with NumPy arrays, matrices, and vectors.
Build scalable Python solutions using advanced techniques.
Skills you'll gain
What you'll learn
Skills you'll gain
What you'll learn
Preprocess and decompose time series data to uncover patterns and trends.
Build and evaluate SARIMA models for robust sales forecasting in Python.
Apply Prophet to model trend, seasonality, and holidays for accurate forecasts.
Skills you'll gain
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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Frequently asked questions
The Specialization can typically be completed in approximately 15 to 16 weeks, with learners dedicating about 3–4 hours per week. This flexible schedule allows you to progress steadily through foundational concepts, advanced Python techniques, applied statistics, supervised machine learning, and time series forecasting. By following this structured pace, you will not only master the technical skills but also gain the confidence to apply them in real-world projects, ensuring both depth and practical readiness for data science roles.
Learners are expected to have a basic understanding of Python programming along with familiarity with fundamental mathematics and statistics. Prior exposure to concepts such as variables, loops, functions, and descriptive statistics will be helpful but not mandatory, as the courses are structured to gradually build on these skills.
Yes, it is recommended to follow the courses in the prescribed sequence. The curriculum is designed to start with essential Python and statistical foundations before advancing into applied machine learning and time series forecasting. Completing the courses in order ensures a smooth learning progression and maximizes comprehension.
More questions
Financial aid available,

