Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence

🔟 Data Analyst Project Ideas for Beginners 1. Sales Analysis Dashboard: Use tools like Excel or Tableau to create a dashboard analyzing sales data. Visualize trends, top products, and seasonal patterns. 2. Customer Segmentation: Analyze customer data using clustering techniques (like K-means) to segment customers based on purchasing behavior and demographics. 3. Social Media Metrics Analysis: Gather data from social media platforms to analyze engagement metrics. Create visualizations to highlight trends and performance. 4. Survey Data Analysis: Conduct a survey and analyze the results using statistical techniques. Present findings with visualizations to showcase insights. 5. Exploratory Data Analysis (EDA): Choose a public dataset and perform EDA using Python (Pandas, Matplotlib) or R (tidyverse). Summarize key insights and visualizations. 6. Employee Performance Analysis: Analyze employee performance data to identify trends in productivity, turnover rates, and training effectiveness. 7. Public Health Data Analysis: Use datasets from public health sources (like CDC) to analyze trends in health metrics (e.g., vaccination rates, disease outbreaks) and visualize findings. 8. Real Estate Market Analysis: Analyze real estate listings to find trends in pricing, location, and features. Use data visualization to present your findings. 9. Weather Data Visualization: Collect weather data and analyze trends over time. Create visualizations to show changes in temperature, precipitation, or extreme weather events. 10. Financial Analysis: Analyze a company’s financial statements to assess its performance over time. Create visualizations to highlight key financial ratios and trends. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)

Data Analytics Projects for Beginners 👇 Web Scraping https://github.com/shreyaswankhede/IMDb-Web-Scraping-and-Sentiment-Analysis Product Price Scraping and Analysis https://github.com/CodesdaLu/Web-Scrapping News Scraping https://github.com/rohit-yadav/scraping-news-articles Real Time Stock Price Scraping with Python https://youtu.be/rONhdonaWUo?si=A3oDEVbLIAP78cCz Zomato Analysis https://youtu.be/fFi_TBw27is?si=E0iLd3J06YHfQkRk IPL Analysis https://github.com/Yashmenaria1/IPL-Data-Exploration https://www.youtube.com/watch?v=ur-v0dv0Qtw https://www.youtube.com/watch?v=ur-v0dv0Qtw Football Data Analysis https://youtu.be/yat7soj__4w?si=h5CLIvVFzzKm8IEP Market Basket Analysis https://youtu.be/Ne8Sbp2hJIk?si=ThEuvdOnRrpcVjOg Customer Churn Prediction https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction Employee’s Performance for HR Analytics https://www.kaggle.com/code/rajatraj0502/employee-s-performance-for-hr-analytics Food Price Prediction https://github.com/VectorInstitute/foodprice-forecasting

Creating a data science portfolio is a great way to showcase your skills and experience to potential employers. Here are some steps to help you create a strong data science portfolio: 1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions. 2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis. 3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization. 4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs. 5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis. 6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented. 7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail. 8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills. By following these steps, you can create a comprehensive and visually appealing data science portfolio that will impress potential employers and help you stand out in the competitive job market.

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Sites to Find Datasets Below are sites I've found free and public datasets. Datahub - This site covers a wide range of topics from climate change to entertainment, but it mainly focuses on economic and business data. Dataset Search - You're able to use Google to search for datasets. It's great if you have a particular topic in mind. Kaggle - It has variety of free datasets provided by users from everything to arts & entertainment to social science data. Data Gov - Public data from the US government from everything from crime to healthcare. Maven Analytics Data Playground - Datasets that are hand picked by Maven's instructors. These datasets can be more fun like analyzing the Harry Potter movies scripts to more business focused like analyzing sales of a pizza place. Awesome Public Datasets - A list of topic focused public data sources that are high quality. These are collected from blogs, answers, and user responses. Datacamp Datasets - These datasets are from a variety of fields from real estate to retail. All of the datasets have the data and packages needed. NASA Data - Has open-data provided to the public from NASA. The dataset pages only hold the metadata and the actual data may be on another NASA site. There will be links to the data in these other locations. Dataportfolio - Telegram Channel with Free Datasets Google BigQuery - It's free to sign up and you can practice with plenty of free datasets.

Creating a data science portfolio is a great way to showcase your skills and experience to potential employers. Here are some steps to help you create a strong data science portfolio: 1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions. 2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis. 3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization. 4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs. 5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis. 6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented. 7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail. 8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills. So, start today and let the data analysis career begin I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)

𝐒𝐐𝐋 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰: Join for more: https://t.me/sqlanalyst 1. Danny’s Diner: Restaurant analytics to understand the customer orders pattern. Link: https://8weeksqlchallenge.com/case-study-1/ 2. Pizza Runner Pizza shop analytics to optimize the efficiency of the operation Link: https://8weeksqlchallenge.com/case-study-2/ 3. Foodie Fie Subscription-based food content platform Link: https://lnkd.in/gzB39qAT 4. Data Bank: That’s money Analytics based on customer activities with the digital bank Link: https://lnkd.in/gH8pKPyv 5. Data Mart: Fresh is Best Analytics on Online supermarket Link: https://lnkd.in/gC5bkcDf 6. Clique Bait: Attention capturing Analytics on the seafood industry Link: https://lnkd.in/ggP4JiYG 7. Balanced Tree: Clothing Company Analytics on the sales performance of clothing store Link: https://8weeksqlchallenge.com/case-study-7 8. Fresh segments: Extract maximum value Analytics on online advertising Link: https://8weeksqlchallenge.com/case-study-8

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Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself. 1. Basic python and statistics Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness Automobile :- https://www.kaggle.com/toramky/automobile-dataset 2. Advanced Statistics Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset 3. Supervised Learning a) Regression Problems How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview b) Classification problems Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview Titanic :- https://www.kaggle.com/c/titanic San Francisco crime:- https://www.kaggle.com/c/sf-crime Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification Categorize cusine:- https://www.kaggle.com/c/whats-cooking 4. Some helpful Data science projects for beginners https://www.kaggle.com/c/house-prices-advanced-regression-techniques https://www.kaggle.com/c/digit-recognizer https://www.kaggle.com/c/titanic 5. Intermediate Level Data science Projects Black Friday Data : https://www.kaggle.com/sdolezel/black-friday Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset Million Song Data : https://www.kaggle.com/c/msdchallenge Census Income Data : https://www.kaggle.com/c/census-income/data Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2 Share with credits: https://t.me/sqlproject ENJOY LEARNING 👍👍