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Data Science Methodology

Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don’t have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course provided by IBM shares a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. The following are the notes I took during this course.

Tools for Data Science

In this course provided by IBM, I learned about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio, what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Skills Network Labs, I can now run simple code in Python, R or Scala. The following are the notes I took during this course.

IBM Data Analyst Capstone Project

In this course provided by IBM, I 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. The capstone project will culminate with a presentation of your data analysis report, with an executive summary for the various stakeholders in the organization. I believe this project is a great opportunity to showcase Data Analytics skills, and demonstrate proficiency to potential employers. The following are the notes I took during this course.

Data Analysis With Python

This Data Analysis with Python course provided by IBM is designed to teach future data analysts how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data through a number of lecture, lab, and assignments using Python libraries. The following are the notes I took during this course.

Data Visualization With Python

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 helps one understand the data better, and make more effective decisions. The main goal of this Data Visualization with Python course provided by IBM is to use various techniques and several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium for presenting data visually. The following are the notes I took during this course.

Python Project for Data Science

This Python Project mini-course provided by IBM is intended to demonstrate basic Python skills by performing specific tasks such as extracting data, web scraping, visualizing data, and creating a dashboard. The following are the notes I took during this course.

Databases and SQL for Data Science with Python

Much of the world’s data resides in databases, A working knowledge of databases and SQL is a must to become a data scientist. The emphasis in this course provided by IBM is on hands-on and practical learning. So, I’ll try to record how I work with real databases, real data science tools, real-world datasets and eventually, how I create a database instance in the cloud on the following notes I took during this course.

Python for Data Science, AI & Development

This course provided by IBM will not teach everything about Python, but it gives me the tools to work as a data scientist and enough knowledge to continue to expand Python learning. The following are the notes I took during this course. Since that I’ve learned Python for everybody on Coursera before, so this note will only contain the necessary outlines and newly learned content.

Data Visualization and Dashboards with Excel and Cognos

“A picture is worth 1,000 words”. This Course provided by IBM endows me with the ability to effectively create data visualizations, such as charts or graphs, with Excel and IBM Cognos Analytics, without having to write any code. It also elevates my confidence level in creating intermediate level data visualizations after numerous hands-on labs and the final project. The following are the notes I took during this course.

Excel Basics for Data Analysis

Excel is an essential tool for working with data - whether for business, marketing, data analytics, or research. Throughout this course provided by IBM, I’ve gained valuable experience in cleansing and wrangling data using functions and then analyze data using techniques like filtering, sorting and creating pivot tables. The following are the notes I took during this course.




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