I decided to add a bit of color to my site. I went with navy blue to give off a similar old-school vibe as my index page. The reason for doing this is to make my site stand out more.
I added color to the:
Portfolio section
About section
Resume section
I made the resume section multi-colored so that my skills and attributes could be individually recognized and not just as one page full of information.
I was able to reformat my resume.html page. While it’s still not completed, it looks better and it takes up the entire screen. My next course of action is to do some research on how the columns work in html.
Today, i was able to fill up my portfolio page a bit more. However, I was unable to embed youtube links on my page. For some reason, YouTube is refusing to connect to page so I added a picture as well as the link right under it.
For next week, I still need to fix the alignment on my resume because it’s still only on the left side of the page.
Hi there,
For this week’s update, I added my most relevant project to my resume.html page. The goal for next week is to fix the alignment so that the view is of three columns instead of one column on the left side of the page. I experimented with it this week but to no avail, fortunately, it shouldn’t be too difficult since w3schools has plenty of tips and tricks I can use.
Another goal for next week is to fill out my portfolio page more, adding pictures and more information on the relevant project I added to the resume.html page. Also instead of just having a link to YouTube channel, I’ll try and embed my latest 2 or 3 videos to fill out the space a bit more.
For this week’s AI assignment I started by asking the following:
“What would i need to put into an entry-level data analyst portfolio? Include as many details as possible.”
ChatGPT responded with:
”
Creating an entry-level data analyst portfolio is an excellent way to showcase your skills, projects, and achievements to potential employers. Here’s a comprehensive guide on what to include:
1. **Introduction:**
– Begin with a brief introduction about yourself, highlighting your educational background, relevant coursework, and career aspirations in data analysis.
2. **Resume/CV:**
– Include a well-formatted resume or curriculum vitae (CV) that highlights your education, relevant skills, work experience (if any), and any certifications you’ve obtained.
3. **Projects:**
– Showcase a selection of data analysis projects you’ve completed. Aim for a variety that demonstrates different skills and techniques. Each project should include:
– **Project Title:** A descriptive title for the project.
– **Description:** A brief overview of the project, including its objectives and scope.
– **Tools/Languages Used:** List the software, programming languages, and tools you used to perform the analysis.
– **Data Sources:** Specify where the data came from and any data cleaning or preprocessing steps you performed.
– **Methods:** Describe the analytical methods, techniques, and algorithms you employed.
– **Results:** Present your findings, insights, and any visualizations you created.
– **Impact/Conclusion:** Discuss the significance of your findings and any recommendations or actions resulting from the analysis.
– **Code Repository:** Provide a link to the GitHub repository or other platform where the project code is hosted (if applicable).
4. **Data Visualization Examples:**
– Include samples of data visualizations you’ve created using tools like Tableau, Power BI, Matplotlib, or Seaborn. Choose visuals that effectively communicate insights from your analysis.
5. **Reports and Documentation:**
– Include any reports, presentations, or documentation you’ve prepared as part of your analysis projects. These documents should be well-organized, clear, and concise.
6. **Case Studies:**
– If you’ve worked on real-world data analysis projects (e.g., internships, freelance work), include case studies that detail the problem, your approach, and the outcomes achieved.
7. **Skills Matrix:**
– Create a skills matrix or infographic that visually represents your proficiency in various data analysis tools, programming languages, statistical methods, and domain knowledge.
8. **Certifications and Courses:**
– List any relevant certifications you’ve obtained (e.g., SQL, Excel, Tableau) and include links to online courses or training programs you’ve completed.
9. **Optional Additions:**
– Depending on your background and interests, you may also include additional sections such as:
– Articles or blog posts you’ve written on data analysis topics.
– Participation in data analysis competitions (e.g., Kaggle).
– Testimonials or recommendations from colleagues, mentors, or clients.
10. **Design and Presentation:**
– Ensure your portfolio has a clean and professional design. Use consistent formatting, typography, and color schemes throughout. Pay attention to detail and make sure all content is well-written and error-free.
11. **Accessibility:**
– Make your portfolio easily accessible online by hosting it on a personal website or platforms like GitHub Pages, LinkedIn, or a PDF document shared via Google Drive or Dropbox.
Remember to tailor your portfolio to the specific job roles and industries you’re targeting. Continuously update and refine your portfolio as you gain new skills and complete additional projects. Finally, solicit feedback from peers, mentors, or professionals to ensure your portfolio effectively showcases your capabilities as an entry-level data analyst.”
To look into the project portion of the previous section more I wanted to get some ideas to start working on. I want to incorporate more than one interest to kill two birds with one stone.
3. **Artist Similarity Analysis:**
– Utilize music metadata APIs to gather data on artists, albums, and songs. Explore similarities between different artists based on musical attributes such as genre, tempo, and mood. Visualize artist similarity networks and clusters to identify musical trends and influences.
4. **Genre Classification:**
– Build a machine learning model to classify songs into different genres based on musical features such as tempo, key, and instrumentation. Train the model using a dataset of labeled songs and evaluate its performance using cross-validation techniques.
5. **Popularity Prediction:**
– Predict the popularity of new songs or artists based on historical data. Use regression or time series forecasting models to predict future trends in music popularity. Evaluate model performance using metrics like mean squared error or accuracy.
6. **Lyric Generator:**
– Create a simple lyric generator using natural language processing techniques. Train a language model on a corpus of song lyrics and generate new lyrics based on user input or randomly sampled seed text. Experiment with different model architectures and training techniques to improve lyric quality.
7. **Pop Culture Analysis:**
– Analyze trends in pop culture using social media data. Use APIs like the Twitter API to gather data on trending topics, hashtags, and user engagement. Explore relationships between different pop culture phenomena and visualize trends over time.
8. **Movie and TV Show Recommender:**
– Build a movie or TV show recommender system based on user preferences and viewing history. Use collaborative filtering or content-based filtering techniques to recommend new movies or TV shows to users based on their interests and preferences.
9. **Celebrity Social Media Analysis:**
– Analyze social media data from celebrities and influencers to identify trends in celebrity behavior, engagement, and influence. Explore topics like celebrity endorsements, brand partnerships, and social media engagement strategies.
10. **Pop Culture Quiz Game:**
– Create a fun and interactive quiz game using RShiny or other web development frameworks. Design quizzes based on trivia questions about music, movies, TV shows, and celebrities. Add features like leaderboards, timers, and hints to enhance user engagement.
These projects offer a blend of data analysis, machine learning, and natural language processing techniques applied to topics related to music and pop culture. Feel free to customize and expand upon these ideas to match your interests and skill level in RStudio.”
In conclusion, this session was helpful. Before now I would’ve never thought to add a skills matrix to my portfolio. On top of that I plan on using ChatGPT more to help me with my projects moving forward. As long as I have interest that I want to pursue then AI in general will help me accomplish feats in the different fields I go in.
In the fast-paced world of market research, establishing a robust professional network is as essential as mastering the intricacies of the industry itself. This blog aims to guide you through the effective use of LinkedIn and the myriad networking opportunities provided by market research firms to accelerate your career in this dynamic field.
Beyond merely listing skills, highlight your proficiency in market research-related competencies. If actively seeking employment, make use of the “Open to Work” feature to signal to recruiters that you are open to new opportunities. When connecting with professionals, send personalized requests expressing genuine interest in forging meaningful connections.
Active engagement is the key on LinkedIn. Join industry-specific groups to participate in discussions. Share pertinent content, such as reports and articles, to showcase your expertise. Follow market research firms, industry associations, and companies to stay updated on industry developments and job opportunities.
**Navigating Networking Opportunities with Market Research Firms**
Market research firms offer a plethora of networking opportunities that can significantly elevate your career trajectory.
Attending conferences and events hosted by these firms provides a unique chance to network with professionals and gain firsthand insights into industry trends. Explore webinars and workshops for interactive learning and the opportunity to connect with industry experts.
Networking events, both organized by firms and within the industry, offer a more informal setting for professionals to connect, exchange ideas, and foster lasting relationships. Engage in online forums and communities facilitated by market research firms to participate in discussions and connect with industry peers.
Professional associations, such as the Insights Association or ESOMAR, play a pivotal role in connecting professionals in the market research field. Joining these associations opens doors to exclusive events, conferences, and webinars, creating ample networking opportunities.
Social media groups and vendor exhibitions provide additional channels for connecting with professionals and vendors in the industry. Participate in collaborative research projects and consider attending recruitment events or career fairs hosted by market research firms to explore job opportunities.
For this week’s assignment, i decided to use handshake.com. Handshake is an employment available to all GSU students. Here it’s easier to set your job preferences when searching for entry-level positions or specific companies. My search terms were data analyst and business analyst, preferably any job focusing on the degree I’m getting (Computer Information Systems). Internships are my main focus, particularly during the summer months. Summer internship applications are due starting in May. I’m also looking at entry-level full-time jobs. I decided to look locally (specifically in Atlanta within a 25-mile radius) because I plan to work in the city after graduation. Some jobs fit my qualifications that were outside of the state however most of them did not provide a housing benefit. I ended up finding a decent amount of relevant opportunities all located in Atlanta and all looking for a CIS major.
Qualifications:
All jobs require a fundamental understanding of college-level mathematics as well as the ability to juggle and execute multiple tasks. Computer programming knowledge and skills are also required. Since most internships require teamwork, they mostly require the ability to collaborate with other team members and have dedication to meeting and exceeding their expectations. However, it will be a lot of responsibilities therefore they require strong organizational skills and attention to detail. Most jobs I applied to require you to clear a pre-employment drug test.
Responsibilities:
The role involves providing analytical support for the preparation of asset business plans. This includes the extraction, review, analysis, and interpretation of various financial and operational documents such as financial statements, rent rolls, third-party reports, tenant lease agreements, surveys, title reports, zoning reports, property insurance policies, credit reports, and other due diligence requirements. Additionally, the responsibilities extend to researching, analyzing, and summarizing tenant-level information, including financial trends and credit ratings. Market and submarket information, encompassing inventory, absorption, new/proposed development, rent, and vacancy trends, is also a focal point for analysis. The extraction of pertinent data from appraisal, engineering, environmental, and other reports is crucial. The role further involves writing property, market, borrower/sponsor descriptions, and third-party report summaries for inclusion in credit memos and asset summary reports. Beyond these tasks, the position requires a comprehensive understanding of the company’s overall concept, including its brand, customer base, product goals, and service aspects. The intern is expected to rotate through various company divisions, contributing ideas to enhance business growth and improvement. Designated business-focused projects are assigned, involving research, proposal development, and presentation during the internship. Engaging with office employees and clients to provide strategic opinions and support is part of the role. Moreover, the intern is encouraged to offer suggestions to management for enhancing factory building project processes and perform other related duties as assigned by their manager.
Software Expectations:
MS Office
Tableau
R Studio
Python SQL
PowerBI
3. A list of all of the specific abilities employers are looking for.
Understanding of Agile methodology
Manual Testing, APIs, Data Validation, Test Automation tools Understanding of statistical analysis
Working knowledge of software design technologies, best practices, etc.
4. A list of your documents, videos, images, etc. that will demonstrate one or more of the specific abilities they are looking for.
Understanding of Agile Methodology:
Manual Testing, APIs, Data Validation, and Test Automation Tools:
API Testing Reports: Share reports from API testing exercises, indicating proficiency in testing endpoints and data validation.
Understanding of Statistical Analysis:
Working Knowledge of Software Design Technologies, Best Practices, etc.:
5. A list of the documents, videos, images, etc. between now and when you graduate you need to create.
Data Analysis Reports: Share reports or presentations where statistical analysis was applied to draw meaningful insights from datasets.
Statistical Models: Showcase any developed statistical models, such as regression analysis or predictive modeling, to demonstrate analytical skills.
6. Finally, write a paragraph reflecting on your experience doing this research. What did you learn, what did you know that was reinforced, and what do you want to do/learn next? How are you feeling about all this at this point?
The main thing I expected going into my research was that most companies are seeking strong-minded people with a will to be well-organized and committed to detail. One thing I’ve learned is that companies are expecting you to have already done some sort of work in that field as that would provide you with an advantage in the screening process and eventually later on once you get accepted. My next course of action in pursuit of a job is to start a personal project that shows my expertise in data science to add to my portfolio. I feel as though I have my work cut out for me this semester if I plan on taking on this personal project because that’ll require me squeezing it into an already tight schedule.
My name is Jaylen Daniels and I’m a senior majoring in Computer Information Systems with a concentration in Data Analytics.
I aim to secure a position as a junior data analyst, where I can apply my technical knowledge in extracting, analyzing, and interpreting complex datasets. I hope to gain hands-on experience with cutting-edge analytics tools this semester and techniques to contribute effectively to decision-making processes within the organization.
I have taken relevant courses such as Database Management and Unstructured Data, however, I need to keep developing proficiency in the relevant tools and technologies used in data analytics, such as SQL for database querying, Excel for data manipulation, and data visualization tools like Tableau or Power BI.