Get ready for a high-growth career in data analytics with the IBM Data Analyst program. This 4-month course equips you with in-demand skills like Python, Excel, and SQL, making you job-ready in no time. No prior experience or degree is required.
Throughout the program, you'll learn the foundational data skills sought after by employers for entry-level data analytics roles. From mastering Excel spreadsheets to delving into Python libraries like Pandas and Numpy, you'll gain hands-on experience with a variety of data sources and project scenarios.
Upon completion, you'll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. You'll also earn an IBM Digital badge and gain access to career resources, including mock interviews and resume support.
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The IBM Data Analyst program comprises nine modules covering essential skills such as data analytics introduction, Excel basics, data visualization, Python for data science, SQL for data science, data analysis with Python, data visualization with Python, and a capstone project.
Exploring the fundamentals of data analytics, this module covers the key steps in the data analytics process and different data roles. It also delves into various data structures, file formats, and sources of data, providing a comprehensive overview of the field.
This module focuses on utilizing Excel for data analysis, covering basic spreadsheet tasks, data quality techniques, and data analysis using filter, sort, look-up functions, and pivot tables. It provides a solid foundation in Excel for data analytics.
Here, you'll learn to create visualizations and dashboards using Excel spreadsheets, including basic and advanced charts and visualizations. Additionally, you'll gain proficiency in building and sharing interactive dashboards using Excel and Cognos Analytics.
Gain a comprehensive understanding of Python basics, programming logic, and proficiency in using Python libraries such as Pandas, Numpy, and Beautiful Soup. Additionally, you'll learn to access web data using APIs and web scraping from Python in Jupyter Notebooks.
Engage in a real project as a Data Scientist or Data Analyst, demonstrating skills in Python and data analysis. This module emphasizes applying Python fundamentals, data structures, and creating a dashboard using Python and libraries like Pandas, Beautiful Soup, and Plotly.
This module provides insight into analyzing data within a database using SQL and Python, creating relational databases, working with multiple tables, and composing basic to advanced level SQL queries using DML commands and advanced SQL techniques.
Develop Python code for cleaning and preparing data for analysis, perform exploratory data analysis, manipulate data using dataframes, and build and evaluate regression models using machine learning scikit-learn library.
Implement data visualization techniques and plots using Python libraries such as Matplotlib, Seaborn, and Folium. You'll create different types of charts and plots, including advanced visualizations and interactive dashboards using the Dash framework and Plotly library.
For the capstone project, you'll apply different techniques to collect and wrangle data, showcase your data analysis and visualization skills, and create a comprehensive data analysis report and presentation, demonstrating proficiency with various Python libraries.
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