What you will learn in Applied Data Science Specialization Course
- This course provides a comprehensive introduction to applied data science, focusing on practical programming and analytical skills using Python.
- Learners will gain hands-on experience with tools like Jupyter notebooks, Pandas, Matplotlib, and Scikit-learn to perform end-to-end data analysis.
- The course emphasizes data wrangling and exploratory data analysis to uncover trends and make data-driven decisions.
- Students will explore real-world applications of machine learning models, including supervised and unsupervised techniques.
- Visualization tools and storytelling methods are used to effectively communicate analytical insights.
- Case studies and labs provide real-time exposure to solving data challenges in business and research contexts.
- The capstone project allows learners to demonstrate applied data science skills by completing a real-world analytics project.
Program Overview
Python Basics for Data Science
⏱️3-4 weeks
Start with the essentials of Python, focusing on syntax, data structures, and writing clean code for data analysis.
Learn basic Python operations, functions, and loops.
Work with data types and apply logic using Python.
Use Jupyter notebooks for hands-on practice.
Build confidence to move into data-centric coding.
Data Analysis with Python
⏱️4-6 weeks
Dive into analyzing datasets using Pandas and NumPy.
Import, clean, and transform real-world datasets.
Perform descriptive statistics and exploratory analysis.
Understand data distributions and relationships.
Use statistical functions and aggregations.
Data Visualization with Python
⏱️4-5 weeks
Learn to visualize insights using Python’s top libraries.
Create graphs using Matplotlib and Seaborn.
Build visual narratives from complex datasets.
Customize visualizations for clarity and impact.
Practice storytelling through data dashboards.
Machine Learning with Python
⏱️6-8 weeks
Explore foundational machine learning techniques and applications.
Understand supervised vs. unsupervised learning.
Train models using regression, classification, and clustering.
Evaluate model performance using industry metrics.
Apply Scikit-learn to real datasets.
Applied Data Science Capstone Project
⏱️6-8 weeks
Put your knowledge into action by solving a practical data problem.
Clean, analyze, and model data to draw insights.
Use visualization to tell the story behind the data.
Deliver a project portfolio piece for job applications.
Showcase your skills with an IBM-recognized certificate.
Get certificate
Job Outlook
- The data science field continues to grow rapidly, with a 36% increase in demand projected by 2031 (U.S. Bureau of Labor Statistics).
- Data science skills are sought across tech, healthcare, marketing, finance, and government sectors.
- Entry-level data scientists typically earn $70K–$95K annually, with senior roles exceeding $120K+.
- Python, machine learning, and data visualization are top skills employers look for.
- The IBM certificate stands out on LinkedIn and job boards, signaling real-world readiness.
- Data science knowledge opens doors to specialized roles in AI, ML, and business analytics.
- The practical focus of this course builds confidence for both job-seekers and upskillers.
- Remote and freelance roles are expanding with the rise in data-driven transformation.
Specification: Applied Data Science Specialization – By IBM
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FAQs
- Learners appreciate the applied focus and structure but note occasional technical difficulties—especially with cloud notebooks that may not always align across versions.
- Despite minor frustrations, many still recommend the course as a solid introduction, though suggest supplementing it with additional practice to cement learning.
