100 Days Data Analysis Roadmap for 2025

Daily hours: 1-2 hours. the practical application of what you learn is crucial, so allocate some time for hands-on projects and real- world applications.

Days 1-10: Foundations of Data Analysis

Days 1-2:Install Python, Jupyter Notebooks, and necessary libraries (NumPy, Pandas).

Days 3-5: Learn the basics of Python programming.

Days 6-10: Dive into data manipulation with Pandas.

Days 11-20: SQL for Data Analysis

Days 11-15: Learn SQL for querying and analyzing databases.

Days 16-20: Practice SQL on real-world datasets.

Days 21-30: Excel for Data Analysis

Days 21-25: Master essential Excel functions for data analysis.

Days 26-30: Explore advanced Excel features for data manipulation and visualization.

Days 31-40: Data Cleaning and Preprocessing

Days 31-35: Explore data cleaning techniques and handle missing data.

Days 36-40: Learn about data preprocessing techniques (scaling, encoding, etc.).

Days 41-50: Exploratory Data Analysis (EDA)

Days 41-45: Understand statistical concepts and techniques for EDA.

Days 46-50: Apply data visualization tools (Matplotlib, Seaborn) for EDA.

Days 51-60: Statistical Analysis

Days 51-55: Deepen your understanding of statistical concepts.

Days 56-60: Learn hypothesis testing and regression analysis.

Days 61-70: Advanced Data Visualization

Days 61-65: Explore advanced data visualization with tools like Plotly and Tableau.

Days 66-70: Create interactive dashboards for data storytelling.

Days 71-80: Time Series Analysis and Forecasting

Days 71-75: Understand time series data and basic analysis.

Days 76-80: Implement time series forecasting models.

Days 81-90: Capstone Project and Specialization

Work on a practical data analysis project incorporating all learned concepts.

Choose a specialization (e.g., domain-specific analysis) and explore advanced techniques.

Days 91-100: Additional Tools

Days 91-95: Introduction to big data concepts (Hadoop, Spark).

• Days 96-100: Hands-on experience with distributed computing using Spark.

Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope this helps you 😊