6/2021 - 12/2021 Portfoilo of work produced while taking the Georgia Tech Data Science and Analytics Boot Camp.
Skills Gained:
Web Technologies and Data Visualization • HTML • CSS • Bootstrap • Dashboarding • JavaScript Charting • Geomapping with Leaflet.js
Business Intelligence Software • Tableau
Advanced Topics • R Programming • Big Data Analytics with Hadoop • Supervised Machine Learning • Unsupervised Machine Learning • Deep Learning
Intermediate Excel • Pivot Tables • VBA Scripting
Fundamental Statistics • Modelling • Forecasting
Python Programming • Python 3 • NumPy • SciPy • Pandas • Matplotlib • API Interactions
Databases • PostgreSQL/pgAdmin • MongoDB • Extract-Transform-Load (ETL)
I'm qualified for many different roles, including: Data Analyst Data Engineer Database Administrator (Entry Level) Data Scientist (Entry Level) Big Data Engineer (Entry Level) Data Journalist Business Intelligence Analyst Business Analyst Research Analyst SQL Developer Software Engineer (Entry Level) Computational Scientist Data Architect
What I learned: Utilize statistical analysis to characterize and interpret novel datasets. Use advanced SQL and NoSQL techniques to combine multiple datasets into one so as to create even more impressive and comprehensive databases. Create basic interactive websites and applications to show your work to the entire world. Work with and lead small-scale teams in order to create applications and visual datasets. Scrape information from web pages in order to collect data from a wide variety of online sources. Communicate and glean new business insights using enterprise-grade tools like Tableau. Build data-driven prediction algorithms using machine learning tools and techniques. Work independently or in a group on complex data-mining projects. Understand the basics of troubleshooting and enhancing legacy code. Use version-controlling software such as Git to collaborate on open-source software. Employ statistical models to predict and forecast trends. Build VBA scripts in Excel to automate tedious manual processes Utilize real-world data sources to showcase social, financial, and political phenomena. Create Python-based scripts to automate the cleanup, restructuring, and rendering of large, heterogeneous datasets. Interact with RESTful APIs using Python. Requests and JSON parsing techniques Create in-depth graphs, charts, and tables utilizing a wide-variety of data-driven programming languages and libraries. Use ETL process (Extract, Transform, Load) to transform and consolidate data from multiple sources. Use geographic data to create visually exciting, interactive, and informative maps. Build custom interactive data isualizations using JavaScript libraries. Write SQL commands to perform Create, Read, Update, and Delete commands. Over the course of 24 weeks, you’ll attend informative lectures, participate in a variety of individual and team exercises, and work independently inside and outside of class time. Homework assignments provide an opportunity to apply what you’ve learned and build on it. The goal is to give you a comprehensive learning experience and true insight into a “day in the life” of a data professional.
DISCUSSION PROJECT WORK PORTFOLIO PROJECTS My portfolio signals to employers that I am ready for primetime! I built a substantial portfolio of projects that demonstrate my abilities across a wide variety of technologies. I worked on timed in-class exercises and projects individually and in teams to put classroom teachings into practice. Instructor-led discussions covered the background, history, and use of new technologies or concepts.
My Portfolio It’s a fact: companies care about what you can do, not what you say you can do. The curriculum taught meu how to put what you’ve learned to work. I covered real-world data projects, ranging from visualizing bike sharing data in New York City to mapping worldwide earthquakes in real-time.
Bank Deserts Project Social economists have long noted a trend that in geographic areas with higher poverty rates, there is often a dearth of reputable banks or financial services. The shortage leads to higher rates of financial victimization in these areas. But how could we show this trend using data? In this activity, I learned how to combine data from the U.S. Census, Google Maps, and Google Places to visualize the relationship between various socioeconomic factors and the number of banks in a given zip code.
Skills Needed: • Python • Pandas • Google Maps • Google Places • Matplotlib • APIs
Objectives • Utilize the Python Requests library to make hundreds of API calls to the U.S. Census and Google Maps datasets • Utilize the Python pandas library to organize the retrieved information by zip code and socioeconomicfactors • Build scatter plots to easily communicate the Banking Desert phenomena • Design statistical models to quantify relationships between factors
Earthquake History Data isn’t just about finance and numbers. It can also be used for good as well. In this activity, you will create an interactive visualization of historic earthquakes over time using Leaflet.js, a popular JavaScript geomapping library. Your final application will provide a near-live feed of global earthquakes and their relative magnitudes.
Skills Needed • HTML • CSS • Javascript • Leaflet.js • APIs • JSON
Objectives • Harness the power of APIs and JSON to gather earthquake data from USGS datasets • Utilize Leaflet.js library to create visually compelling,animated maps • Embed the created map onto a live web page using HTML and CSS
Non-Profit Investment Analysis Using a dataset with more than 34,000 non-profit organizations that have received funding from Alphabet Soup over the years, I developed a binary classifier neural network model to predict whether applicants will be successful if funded by Alphabet Soup. I preprocessed data, design network structure, and train, evaluate, and optimize a neural network model.
Skills Needed • TensorFlow • Keras • Machine Learning • Deep Learning
Objectives • Use Python and Pandas to preprocess the data for training a deep neural network model • Compile, train, and evaluate a deep neural network model with TensorFlow and Keras • Communicate results of the model in comparison to other machine learning models
Web Scraping Application Sometimes, data is just out of reach. Whether it’s a social media website that is guarding its information, a government agency that has poorly organized records, or a cookbook website filled with secret recipes — data isn’t always accessible by external applications. This is where data scraping comes in. Utilizing Python libraries like Beautiful Soup, I learned to convert data straight from raw HTML into a queryable and storable form, opening up troves of data for your future applications.
Skills Needed • Python • Beautiful Soup • HTML • CSS • MongoDB
Objectives • Scrape your favorite social media website for otherwise inaccessible data • Parse through the retrieved information and store it into a MongoDB database • Create new representations of the data using HTML and CSS
Game Studio Analytics Congratulations! I have landed a job as the Lead Analyst for an independent game company and for my first assignment I have been given the difficult task of analyzing data and creating a report for their latest smash hit release. I will be using the Python Pandas Library and Jupyter Notebook to create demographic and financial reports.
Skills Needed • Python • Jupyter Notebook • Pandas Library
Objectives • Use Python and the Pandas library to create a report containing a vast amount of data • Make the data viewable using Jupyter Notebook • Find, analyze, and write up descriptions of observable trends in the data
Classifying Yelp Reviews A Nielsen report concluded that 82% of visitors to Yelp intended to make a purchase, so it’s no surprise that companies take online customer reviews and ratings seriously. In this section of the course, I built an application that can analyze reviews and tell you through Natural Language Processing whether it’s negative or positive. This means you don’t have to have a human read every review that gets posted and respond accordingly. You can instead have a machine flag negative reviews for you so you can trigger an action like outreach and more.
Skills Needed • PySpark • Machine Learning • Natural Language Processing
Objectives • Perform Natural Language Processing with PySpark-ML • Establish a big data processing pipeline to clean and process data • Train and validate a Naive Bayes machine learning model that can make predictions from customer reviews
Curriculum By Module: Module 1: Excel Crash Course Learn to do more with Microsoft Excel. In this module we’ll cover advanced topics like statistical modeling, forecasting and prediction, pivot tables, and VBA scripting. You’ll even learn to model historic stock trends – and hopefully, learn to beat the market! • Microsoft Excel • VBA Script • Statistics Modeling
Module 2: Python Data Analytics Gain a strong foothold in one of today’s fundamental programming languages. In the course of this module, you’ll gain deep proficiencies with core Python, data analytic tools like NumPy, Pandas, Matplotlib, and specific libraries for interacting with web data like Requests and BeautifulSoup. • Python • APIs • JSON • NumPy • Pandas • Matplotlib • Beautiful Soup • SciPly
Module 3: Databases Work with PostgreSQL and MongoDB to organize data into well-structured and easily retrievable data formats. • SQL • PostgreSQL • MongoDB • ETL Process
Module 4: Web Visualization Building visualizations is of little benefit without a way to communicate the message. In this module, you’ll be learning the core technologies of web development (HTML, CSS, and JavaScript) to create new, interactive data visualizations that you can share with everyone on the web. • HTML • CSS • JavaScript • AJAX • Leaflet
Module 5: Advanced Topics By program’s end, you’ll be immersed in new and indemand topics like Tableau, Hadoop, and Machine Learning. • Tableau • Hadoop • Supervised Machine Learning • Unsupervised Machine Learning • Deep Learning
Module 6: Final Project Bring everything that you have learned in class altogether to create an impressive data-visualization application with a small team. Get creative and come up with something cool to show off to the whole world! • Dreaming up something fantastic and understanding the bounds of reasonable and achievable