4 Python practical projects to do for freshers in data analytics

🧵⬇️

1️⃣ Exploratory Data Analysis (EDA) on a Public Dataset

Use a dataset from Kaggle or data.gov

Clean and preprocess the data

Perform statistical analysis and visualization

Draw insights and present findings

2️⃣ Stock Market Analysis Tool

Fetch real-time stock data using an API (e.g., yfinance)

Implement technical indicators (e.g., moving averages, RSI)

Create visualizations of stock performance

Build a simple prediction model

3️⃣ Social Media Sentiment Analysis

Collect tweets or Reddit posts using APIs

Preprocess text data

Perform sentiment analysis

Visualize sentiment trends over time

4️⃣ Customer Churn Prediction

Use a telecom or e-commerce dataset

Perform feature engineering

Build and compare multiple machine learning models

Evaluate model performance and interpret results

Hope it helps :)

SQL Projects with Datasets 👇

📌E-commerce Sales Analysis:
Dataset: Online retail dataset from the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Online+Retail)

📌Social Media Analytics:
Twitter API or Twitter datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=twitter)

📌Healthcare Data Management:
MIMIC-III (Medical Information Mart for Intensive Care III) dataset (https://mimic.mit.edu/docs/iii/)

📌Retail Inventory Management:
Sample retail sales dataset available on platforms like Kaggle (https://www.kaggle.com/datasets?search=retail)

📌Financial Portfolio Analysis:
Yahoo Finance API or finance datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=finance)

📌Real Estate Market Analysis:
Zillow dataset (https://www.zillow.com/research/data/) or real estate datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=real+estate)

📚 9 must-have Python developer tools.

1. PyCharm IDE

2. Jupyter notebook

3. Keras

4. Pip Package

5. Python Anywhere

6. Scikit-Learn

7. Sphinx

8. Selenium

9. Sublime Text

5 Handy Tips to Master Data Science ⬇️

1️⃣ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel

2️⃣ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.

3️⃣ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.

4️⃣ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.

5️⃣ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.

✅ 5 of the best Kaggle datasets
💸 For data science projects (in finance)

👨🏻‍💻 If you are looking for datasets to do financial projects, the datasets presented on the Kaggle site can be a great option.

⏪ These datasets are usually clean and ready to use and are very suitable for machine learning models. Some of these datasets are even updated daily and you can use them for deeper analysis.👇


1️⃣ S&P 500 stock dataset (daily update)

📎 Link: S&P 500 Stocks


2️⃣ Database of loans and debts

📎 Link: Loans & Liability


3️⃣ Dataset of frequent use of credit card

📎 Link: Credit Card Spending Habits


4️⃣ Company bankruptcy prediction dataset

📎 Link: Company Bankruptcy Prediction


5️⃣ Credit score classification dataset

📎 Link: Credit score classification

Hope this helps you

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Top 10 Python libraries commonly used by data scientists

1. NumPy: A fundamental package for scientific computing with support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions.

2. pandas: A powerful data manipulation and analysis library that provides data structures and functions for working with structured data.

3. matplotlib: A widely-used plotting library for creating a variety of visualizations, including line plots, bar charts, histograms, scatter plots, and more.

4. scikit-learn: A comprehensive machine learning library that provides tools for data mining and data analysis, including algorithms for classification, regression, clustering, and more.

5. TensorFlow: An open-source machine learning framework developed by Google for building and training machine learning models, particularly for deep learning tasks.

6. Keras: A high-level neural networks API that is built on top of TensorFlow and provides an easy-to-use interface for building and training deep learning models.

7. Seaborn: A data visualization library based on matplotlib that provides a high-level interface for creating informative and attractive statistical graphics.

8. SciPy: A library that builds on NumPy and provides a wide range of scientific and technical computing functions, including optimization, integration, interpolation, and more.

9. Statsmodels: A library that provides classes and functions for the estimation of many different statistical models, as well as conducting statistical tests and exploring data.

10. XGBoost: An optimized gradient boosting library that is widely used for supervised learning tasks, such as regression and classification.

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5 Essential Portfolio Projects for data analysts 😄👇

1. Exploratory Data Analysis (EDA) on a Real Dataset: Choose a dataset related to your interests, perform thorough EDA, visualize trends, and draw insights. This showcases your ability to understand data and derive meaningful conclusions.
Free websites to find datasets: https://t.iss.one/DataPortfolio/8

2. Predictive Modeling Project: Build a predictive model, such as a linear regression or classification model. Use a dataset to train and test your model, and evaluate its performance. Highlight your skills in machine learning and statistical analysis.

3. Data Cleaning and Transformation: Take a messy dataset and demonstrate your skills in cleaning and transforming data. Showcase your ability to handle missing values, outliers, and prepare data for analysis.

4. Dashboard Creation: Utilize tools like Tableau or Power BI to create an interactive dashboard. This project demonstrates your ability to present data insights in a visually appealing and user-friendly manner.

5. Time Series Analysis: Work with time-series data to forecast future trends. This could involve stock prices, weather data, or any other time-dependent dataset. Showcase your understanding of time-series concepts and forecasting techniques.

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Having a strong portfolio is one of the best ways to stand out when applying for a Data Analyst role. But it’s important to choose the right projects that show your skills and creativity. Here’s how you can create meaningful projects:-

Don’t work on the same old ideas like simple sales dashboards or stock price analysis. These projects are very common and don’t make you stand out. Instead, try to pick unique and interesting topics that recruiters haven’t seen before.

Think about real problems faced by companies. For example, mobility companies like Uber, Ola, or Rapido face issues where some drivers ask customers to cancel rides so they can complete trips offline. This leads to revenue loss for the company. You can take this as example to create a project to analyze this problem, quantify the losses, and suggest solutions.

Use multiple tools in a single project to show your versatility. For example, you can use SQL to clean and organize data, Python to analyze it, and Power BI to create dashboards. This shows you can handle an entire process from start to finish.

Focus on projects that solve real business problems like reducing customer churn, optimizing marketing budgets, or segmenting customers into different groups. These projects show that you understand how businesses operate and how data can make an impact.

Explain how you thought through the problem when you present your project. For example, if you analyzed driver cancellations, explain how you broke the problem into smaller parts, analyzed the data, and came up with solutions. This helps others see your problem-solving approach.

Combine multiple related problems into one project to make it more impactful. For example, you could analyze driver cancellations, identify peak times for offline completions, and create a dashboard to monitor revenue loss. Combining ideas makes your project more comprehensive and impressive.

Try to find data sets that aren’t commonly used. Instead of downloading the same datasets everyone uses, explore platforms like Kaggle or open data portals, or even create your own data. This will make your projects look fresh and unique.

Always share clear and actionable results in your projects. For example, if you worked on driver cancellations, suggest ways to reduce them, like adjusting incentives or monitoring systems. Finish your project with a clear and engaging dashboard to show your findings.


By working on unique and meaningful projects, you can show your skills, creativity, and ability to solve real problems.

#dataportfolio