IBM Data Analyst

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. Excel is an essential tool for working with data — whether for business, marketing, data analytics, or research. This course is suitable for those aspiring to take up Data Analysis or Data Science as a profession, as well as those who just want to use Excel for data analysis in their own domains. You will gain valuable experience in cleansing and wrangling data using functions and then analyze your data using techniques like filtering, sorting and creating pivot tables.

This course starts with an introduction to spreadsheets like Microsoft Excel and Google Sheets and loading data from multiple formats. With this introduction you will then learn to perform some basic level data wrangling and cleansing tasks and continue to expand your knowledge of analyzing data through the use of filtering, sorting, and using pivot tables within the spreadsheet. By performing these tasks throughout the course, it will give you an understanding of how spreadsheets can be used as a data analysis tool and understand its limitations. There is a strong focus on practice and applied learning in this course. With each lab, you will gain hands-on experience in manipulating data and begin to understand the important role of spreadsheets. Clean and analyze your data faster by understanding functions in the formatting of data. You will then convert your data to a pivot table and learn its features to make your data organized and readable.

The final project enables you to show off your newly acquired data analysis skills. By the end of this course you will have worked with several data sets and spreadsheets and demonstrated the basics of cleaning and analyzing data all without having to learn any code.

Getting started with Excel is made easy in this course. It does not require any prior experience with spreadsheets or coding. Nor does it require downloads or installation of any software. All you need is a device with a modern web browser, and ability to create a Microsoft account to access Excel online at no-cost. However if you already have a desktop version of Excel, you can follow along quite easily as well.

This course covers some of the first steps in the development of data visualizations using spreadsheets and dashboards. Begin the process of telling a story with your data by creating the many types of charts that are available in spreadsheets like Excel. Explore the different tools of a spreadsheet, such as the important pivot function and the ability to create dashboards and learn how each one has its own unique property to transform your data. Continue to gain valuable experience by becoming familiar with the popular analytics tool — IBM Cognos Analytics — to create interactive dashboards.

By completing this course, you will have a basic understanding of using spreadsheets as a data visualization tool. You will gain the ability to effectively create data visualizations, such as charts or graphs, and will begin to see how they play a key role in communicating your data analysis findings. All of this can be accomplished by learning the basics of data analysis with Excel and IBM Cognos Analytics, without having to write any code.

By the end of this course you will be able to describe common dashboarding tools used by a data analyst, design and create a dashboard in a cloud platform, and begin to elevate your confidence level in creating intermediate level data visualizations. Throughout this course you will encounter numerous hands-on labs and a final project. With each lab, gain hands-on experience with creating basic and advanced charts, then continue through the course and begin creating dashboards with spreadsheets and IBM Cognos Analytics. You will then end this course by creating a set of data visualizations with IBM Cognos Analytics and creating an interactive dashboard that can be shared with peers, professional communities or prospective employers.

This course does not require any prior data analysis, or computer science experience. All you need to get started is basic computer literacy, high school level math, access to a modern web browser such as Chrome or Firefox, the ability to create a Microsoft account to access Excel for the Web, and a basic understanding of Excel spreadsheets.

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 course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and create a final project to demonstrate your new skills.

By the end of this course, you’ll feel comfortable creating basic programs, working with data, and solving real-world problems in Python. You’ll gain a strong foundation for more advanced learning in the field, and develop skills to help advance your career. This course can be applied to multiple Specialization or Professional Certificate programs.

Upon completion of any of the above programs, in addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing your expertise in the field.

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. This course is part of the IBM Data Science Professional Certificate and the IBM Data Analytics Professional Certificate.

PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data.

NOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.

Much of the world’s data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist. 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. The emphasis in this course is on hands-on and practical learning. As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python.

No prior knowledge of databases, SQL, Python, or programming is required. Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course.

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!

Topics covered:

1. Importing Datasets
2. Cleaning the Data
3. Data frame manipulation
4. Summarizing the Data
5. Building machine learning Regression models
6. Building data pipelines

Data Analysis with Python will be delivered through lecture, lab, and assignments. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.

If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge.

“A picture is worth a thousand words.” We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.

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. You will undertake the tasks of collecting data from multiple sources, performing exploratory data analysis, data wrangling and preparation, statistical analysis and mining the data, creating charts and plots to visualize data, and building an interactive dashboard.

The project will culminate with a presentation of your data analysis report, with an executive summary for the various stakeholders in the organization. You will be assessed on both your work for the various stages in the Data Analysis process, as well as the final deliverable. This project is a great opportunity to showcase your Data Analytics skills, and demonstrate your proficiency to potential employers.