Gain the job-ready skills for an entry-level data analyst role through this eight-course Professional Certificate from IBM and position yourself competitively in the thriving job market for data analysts, which will see a 20% growth until 2028 (U.S. Bureau of Labor Statistics).
Power your data analyst career by learning the core principles of data analysis and gaining hands-on skills practice. You’ll work with a variety of data sources, project scenarios, and data analysis tools, including Excel, SQL, Python, Jupyter Notebooks, and Cognos Analytics, gaining practical experience with data manipulation and applying analytical techniques.
This IBM Professional Certificate is earned after successfully completing 9 courses on various topics in Data Analytics. The learner understands the core principles of data analysis and has worked hands-on with a variety of data sources, project scenarios, and data analysis tools, including Excel, SQL, Relational Databases, Python, Jupyter Notebooks, and Cognos Analytics, gaining practical experience with data manipulation, data analysis, and data visualization. The earner of this Certificate has demonstrated proficiency in applying different analytical techniques by analyzing real-world datasets, creating visualizations & interactive dashboards, and presenting reports to share findings of data analysis, and is now equipped with skills for an entry-level role in data analytics.
There are 9 Courses in this Professional Certificate Specialization are as follows:
This course presents a gentle introduction into the concepts of data analysis, the role of a Data Analyst, and the tools that are used to perform daily functions. You will gain an understanding of the data ecosystem and the fundamentals of data analysis, such as data gathering or data mining.
This course is designed to provide you with basic working knowledge for using Excel spreadsheets for Data Analysis. It covers some of the first steps for working with spreadsheets and their usage in the process of analyzing data. It includes plenty of videos, demos, and examples for you to learn, followed by step-by-step instructions for you to apply and practice on a live spreadsheet.
This course covers some of the first steps in the development of data visualizations using spreadsheets and dashboards.
Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries.
This mini-course is intended to for you to demonstrate foundational Python skills for working with data. The completion of this course involves working on a hands-on project where you will develop a simple dashboard using Python.
The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment.
Learn how to analyze data using Python. Topics covered:
- Importing Datasets
- Cleaning the Data
- Data frame manipulation
- Summarizing the Data
- Building machine learning Regression models
- Building data pipelines
The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.
In this course you will apply various Data Analytics skills and techniques that you have learned as part of the previous courses in the IBM Data Analyst Professional Certificate. You will assume the role of an Associate Data Analyst who has recently joined the organization and be presented with a business challenge that requires data analysis to be performed on real-world datasets.
© 2021 Leah Nguyen