What you will learn
- Gain a solid foundation in data science using Python.
- Learn data manipulation, cleaning, and analysis with Pandas and NumPy.
- Master data visualization using Matplotlib and Seaborn.
- Understand statistical analysis, hypothesis testing, and probability concepts.
- Develop skills in machine learning with Scikit-learn.
- Work on real-world data science projects to build your portfolio.
Program Overview
Introduction to Data Science with Python
⏱️4-6 weeks
- Overview of data science workflow and Python programming.
- Learn the basics of data types, loops, and functions in Python.
Data Wrangling & Cleaning
⏱️6-8 weeks
- Work with Pandas and NumPy for data manipulation.
- Learn data preprocessing techniques for structured and unstructured data.
Data Visualization & Exploratory Data Analysis (EDA)
⏱️8-10 weeks
- Use Matplotlib and Seaborn to create insightful visualizations.
- Perform exploratory data analysis to uncover patterns and trends.
Statistics & Probability for Data Science
⏱️10-12 weeks
- Understand descriptive and inferential statistics.
- Learn about hypothesis testing, regression, and probability distributions.
Machine Learning with Python
⏱️12-15 weeks
- Introduction to machine learning models using Scikit-learn.
- Work on classification, regression, and clustering techniques.
Capstone Project: Real-World Data Science Application
⏱️Final Project
- Apply all concepts learned in a hands-on data science project.
- Use Python to clean, analyze, visualize, and build machine learning models.
Get certificate
Job Outlook
- Data science is a top in-demand field, with a 35% job growth projection by 2030.
- Python is the most widely used language in data science and AI.
- Data Scientists earn an average salary of $100K – $150K per year.
- Career paths include Data Scientist, Data Analyst, AI Engineer, and Machine Learning Engineer.
- Companies across finance, healthcare, tech, and e-commerce seek data science professionals.
Explore More Learning Paths
Strengthen your Python-powered data science journey with complementary courses that enhance your technical toolkit, deepen your analytical approach, and help you grow into a confident data professional.
Related Courses
1. Tools for Data Science Course
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2. Data Science Methodology Course
Learn the structured framework that guides how successful data scientists define problems, select approaches, and deliver meaningful insights.
3. Executive Data Science Specialization Course
Develop the strategic and leadership skills needed to manage analytics projects and lead data initiatives at an organizational level.
Related Reading
What Is Data Management?
Understand the fundamentals of organizing, storing, and governing data—an essential skill for every aspiring data scientist.
Specification: Applied Data Science with Python Specialization – By University of Michigan Course
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FAQs
- Yes, it’s designed for beginners with basic Python or programming knowledge.
- The specialization introduces data science concepts using Python libraries like pandas, matplotlib, scikit-learn, nltk, and networkx.
- Ideal for those aiming to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques.
- The specialization consists of 5 courses:
- Introduction to Data Science in Python
- Applied Plotting, Charting & Data Representation in Python
- Applied Machine Learning in Python
- Applied Text Mining in Python
- Applied Social Network Analysis in Python
- The estimated time to complete is 20 weeks, with a commitment of 4–6 hours per week.
- A capstone project allows you to apply learned concepts to a real-world data science problem.
- Yes, upon completing the specialization, you will earn a Certificate of Completion from the University of Michigan.
- The certificate can be added to your resume or LinkedIn profile to showcase your skills in data science.
- The course has received a 4.5 out of 5 stars rating, with over 26,000 reviews.
- Learners appreciate the structured lessons, hands-on projects, and real-world applications provided throughout the specialization.
- Many have found it to be an excellent preparation for careers in data science and analytics.
