Prepare for a career as a data analyst. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.
- Master the most up-to-date practical skills and tools that data analysts use in their daily roles
- Learn how to visualize data and present findings using various charts in Excel spreadsheets and BI tools like IBM Cognos Analytics & Tableau
- Develop working knowledge of Python language for analyzing data using Python libraries like Pandas and Numpy, and invoke APIs and Web Services
- Gain technical experience through hands on labs and projects and build a portfolio to showcase your work
- ACE® College Credit Recommendation: Up to 12 credits toward select universities in the US
- FIBAA Certified: Recognized for 6 ECTS credits (European universities)
- IBM Digital Badge: Earn a verified IBM Data Analyst badge upon completion
- Career Support: Access to IBM Talent Network, interview prep, and job search resources
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Introduction to Data Analytics
- Explain what Data Analytics is and the key steps in the Data Analytics process
- Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst
- Describe the different types of data structures, file formats, and sources of data
- Describe the data analysis process involving collecting, wrangling, mining, and visualizing data
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Excel Basics for Data Analysis
- Display working knowledge of Excel for Data Analysis.
- Perform basic spreadsheet tasks including navigation, data entry, and using formulas.
- Employ data quality techniques to import and clean data in Excel.
- Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables.
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Data Visualization & Dashboards with Excel and Cognos Analytics
- Create basic visualizations such as line graphs, bar graphs, and pie charts using Excel spreadsheets.
- Explain the important role charts play in telling a data-driven story.
- Construct advanced charts and visualizations such as Treemaps, Sparklines, Histogram, Scatter Plots, and Filled Map Charts.
- Build and share interactive dashboards using Excel and Cognos Analytics.
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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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IBM Data Analyst Capstone Project
- Apply techniques to gather and wrangle data from multiple sources.
- Analyze data to identify patterns, trends, and insights through exploratory techniques.
- Create visual representations of data using Python libraries to communicate findings effectively.
- Construct interactive dashboards with BI tools to present and explore data dynamically.
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! 👋
