My Data Analyst Educational Background
Skills Portfolio
I will outline the data analyst courses I’ve taken from Udacity.
“Udacity is an online learning platform that has industry leading programs built with and recognized by Google, AWS, IBM, and more.” - Udacity
I will outline each program and the skills I’ve learned with each.
Data Analyst Nanodegree
The following courses are contained in this nanodegree and all the information was collected from their syllabus available for download here.
1. Introduction to Data Analysis
“Learn the data analysis process of wrangling, exploring, analyzing, and communicating data. Work with data in Python, using libraries like NumPy and Pandas.” -Udacity
2. Practical Statistics
“Learn how to apply inferential statistics and probability to real-world scenarios, such as analyzing A/B tests, multiple linear regression and building supervised learning models.” -Udacity
3. Data Wrangling
“Learn the data wrangling process of gathering, assessing, and cleaning data. Learn to use Python to wrangle data programmatically and prepare it for analysis.” -Udacity
4. Data Visualization with Python
“Learn to apply visualization principles to the data analysis process. Explore data visually at multiple levels to find insights and create a compelling story.” -Udacity
Check out my project repositories on my Github:
SQL Nanodegree
The following courses are contained in this nanodegree and all the information was collected from their syllabus available for download here.
1. Introduction to SQL
“Learn how to execute core SQL commands to define, select, manipulate, control access, aggregate and join data and data tables. Understand when and how to use subqueries, several window functions, as well as partitions to complete complex tasks. Clean data, optimize SQL queries, and write select advanced JOINs to enhance analysis performance.” -Udacity
Check out my repository on my Github:
2. Management of Relational & Non-Relational Databases
“Build normalized, consistent, and performant relational data models. Use SQL Database Definition Language (DDL) to create the data schemas designed in Postgres and apply SQL Database Manipulation Language (DML) to migrate data from a denormalized schema to a normalized one. Understand the tradeoffs between relational databases and their non-relational counterparts, and justify which one is best for different scenarios. With a radical shift of paradigms, learn about MongoDB and Redis to get an understanding of the differences in behaviours and requirements for non-relational databases.” -Udacity
Check out my repository on my Github:
Conclusion
- I have the skills to query relational and relational databases using SQL and No-SQL.
2. I also have the statistical data analysis skills to uncover correlations between variables, hypothesis and A/B testing, and exploring variables with data visualizations.
3. I have demonstrated my experience by linking my Github account with it’s projects to show my understanding
See more of my experience here:
