Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career.
- Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles
- Learn the tools, languages, and libraries used by professional data scientists, including Python and SQL
- Import and clean data sets, analyze and visualize data, and build machine learning models and pipelines
- Apply your new skills to real-world projects and build a portfolio of data projects that showcase your proficiency to employers
- ACE® College Credit Recommendation: Earn up to 12 US semester credits upon completion (via ACE® and Coursera)
- FIBAA Certified: Recognized for 6 ECTS credits by European institutions
- IBM Digital Badge: Receive a verified IBM Data Science Professional Certificate badge via Credly
- Career Support: Access IBM’s Talent Network with personalized job opportunities, skill-based recommendations, and interview tips
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What is Data Science?
- Define data science and its importance in today’s data-driven world.
- Describe the various paths that can lead to a career in data science.
- Summarize advice given by seasoned data science professionals to data scientists who are just starting out.
- Explain why data science is considered the most in-demand job in the 21st century.
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Tools for Data Science
- Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools
- Utilize languages commonly used by data scientists like Python, R, and SQL
- Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features
- Create and manage source code for data science using Git repositories and GitHub.
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Data Science Methodology
- Describe what a data science methodology is and why data scientists need a methodology.
- Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.
- Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study.
- Determine appropriate data sources for your data science analysis methodology.
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Python for Data Science, AI & Development
- Develop a foundational understanding of Python programming by learning basic syntax, data types, expressions, variables, and string operations.
- Apply Python programming logic using data structures, conditions and branching, loops, functions, exception handling, objects, and classes.
- Demonstrate proficiency in using Python libraries such as Pandas and Numpy and developing code using Jupyter Notebooks.
- Access and extract web-based data by working with REST APIs using requests and performing web scraping with BeautifulSoup.
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Python Project for Data Science
- Play the role of a Data Scientist / Data Analyst working on a real project.
- Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis.
- Apply Python fundamentals, Python data structures, and working with data in Python.
- Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.
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Databases and SQL for Data Science (with Python)
- Analyze data within a database using SQL and Python.
- Create a relational database and work with multiple tables using DDL commands.
- Construct basic to intermediate level SQL queries using DML commands.
- Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.
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Data Analysis with Python
- Construct Python programs to clean and prepare data for analysis by addressing missing values, formatting inconsistencies, normalization, and binning
- Analyze real-world datasets through exploratory data analysis (EDA) using libraries such as Pandas, NumPy, and SciPy to uncover patterns and insights
- Apply data operation techniques using dataframes to organize, summarize, and interpret data distributions, correlation analysis, and data pipelines
- Develop and evaluate regression models using Scikit-learn, and use these models to generate predictions and support data-driven decision-making
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Data Visualization with Python
- Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story
- Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble
- Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps
- Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library
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Machine Learning with Python
- Explain key concepts, tools, and roles involved in machine learning, including supervised and unsupervised learning techniques.
- Apply core machine learning algorithms such as regression, classification, clustering, and dimensionality reduction using Python and scikit-learn.
- Evaluate model performance using appropriate metrics, validation strategies, and optimization techniques.
- Build and assess end-to-end machine learning solutions on real-world datasets through hands-on labs, projects, and practical evaluations.
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Applied Data Science Capstone
- Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders
- Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation
- Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors
- Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model
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Generative AI: Elevate Your Data Science Career
- Leverage generative AI tools, like GPT 3.5, ChatCSV, and tomat.ai, available to Data Scientists for querying and preparing data
- Examine real-world scenarios where generative AI can enhance data science workflows
- Practice generative AI skills in hand-on labs and projects by generating and augmenting datasets for specific use cases
- Apply generative AI techniques in the development and refinement of machine learning models
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Data Scientist Career Guide and Interview Preparation
- Describe the role of a data scientist and some career path options as well as the prospective opportunities in the field.
- Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.
- Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.
- Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.
This repo is open source! Feel free to:
- 👀 Browse the course readings, exercises, and case studies
- 💻 Fork/clone for your own self-study or review
- 🤝 Collaborate by submitting issues or improvements via pull requests
- 🌟 Get inspired if you’re preparing to be a data professional or want to level up your data skills
Disclaimer: All content is for educational purposes only and is shared to help aspiring data professionals. Please don’t submit this work as your own in graded assessments—let’s keep it ethical!
✨ I’m always open to networking, collaboration, or sharing insights ✨
Don’t be shy — connect with me on LinkedIn! 👋
