🧑‍💻 100 Days to Data Analyst Roadmap

Unlock your path to becoming a data analyst in just 100 days! This roadmap covers foundational skills, hands-on projects, and advanced techniques to build a solid knowledge base and skill set in data analytics.

📅 Overview

• Phase 1: Foundations of Data Analysis (Days 1-30)
• Phase 2: Data Visualization & Advanced Analysis (Days 31-60)
• Phase 3: Machine Learning Basics & Case Studies (Days 61-90)
• Phase 4: Real-World Projects & Portfolio Building (Days 91-100)

🗓️ Day-by-Day Breakdown

Phase 1: Foundations of Data Analysis (Days 1-30)

Days 1-5: Introduction to Data Analytics

• Day 1: Understand the role of a Data Analyst and the skills/tools needed.
• Day 2: Learn basic data concepts (data types, data processing steps).
• Day 3: Overview of data collection methods and data sources.
• Day 4: Install and explore Excel basics.
• Day 5: Practice basic Excel formulas (SUM, AVERAGE).

Days 6-10: Excel for Data Analysis

• Day 6: Learn Excel sorting, filtering, and conditional formatting.
• Day 7: Understand data validation and error-checking techniques in Excel.
• Day 8: Work with pivot tables to summarize data.
• Day 9: Create basic data visualizations (charts) in Excel.
• Day 10: Complete a Mini-project in Excel using a small dataset.

Days 11-15: Basic Statistics

• Day 11: Study descriptive statistics (mean, median, mode, range).
• Day 12: Learn about probability concepts.
• Day 13: Explore probability distributions and normal distribution.
• Day 14: Introduction to hypothesis testing.
• Day 15: Complete a Mini-project on statistical analysis.

Days 16-20: SQL Basics

• Day 16: Learn SQL fundamentals (SELECT, WHERE clauses).
• Day 17: Practice filtering data in SQL.
• Day 18: Understand GROUP BY, Date and Time and aggregate functions.
• Day 19: Work with JOINs in SQL.
• Day 20: Complete a Mini-project: Analyze data using SQL queries.

Days 21-25: Python Basics

• Day 21: Set up Python environment and learn data types.
• Day 22: Explore lists, dictionaries, and basic Python operations.
• Day 23: Write basic functions and loops.
• Day 24: Install and explore pandas for data analysis.
• Day 25: Complete a Mini-project: Basic data manipulation in Python.

Days 26-30: Data Cleaning with Python

• Day 26: Handle missing values and data imputation.
• Day 27: Learn data transformations and normalization.
• Day 28: Understand and handle outliers.
• Day 29: Clean data with Python using pandas.
• Day 30: Complete a Mini-project: Data cleaning with a real dataset.

Phase 2: Data Visualization & Advanced Analysis (Days 31-60)

Days 31-35: Introduction to Data Visualization

• Day 31: Learn principles of effective data visualization.
• Day 32: Introduction to Matplotlib.
• Day 33: Plot line charts, bar charts, and histograms.
• Day 34: Explore data using scatter plots.
• Day 35: Mini-project: Visualize data using Matplotlib.

Days 36-40: Advanced Visualization with Seaborn

• Day 36: Introduction to Seaborn and aesthetic customizations.
• Day 37: Explore categorical plots in Seaborn.
• Day 38: Use heatmaps for correlation analysis.
• Day 39: Plot time series data.
• Day 40: Complete a Mini-project: Create an advanced visualization.

Days 41-45: Exploratory Data Analysis (EDA)

• Day 41: Learn the purpose and steps of EDA.
• Day 42: Study summary statistics and distributions.
• Day 43: Identify patterns and trends.
• Day 44: Perform feature engineering.
• Day 45: Complete a Mini-project on EDA with a complex dataset.

Days 46-50: SQL Advanced Techniques

• Day 46: Study subqueries and nested queries.
• Day 47: Explore window functions in SQL.
• Day 48: Work with advanced joins.
• Day 49: Analyze large datasets using SQL.
• Day 50: Complete a Mini-project: Advanced SQL queries.

Days 51-55: Advanced Statistics

• Day 51: Study correlation and covariance.
• Day 52: Learn linear regression basics.
• Day 53: Explore ANOVA and chi-square tests.
• Day 54: Study different statistical tests.
• Day 55: Complete a Mini-project: Statistical analysis.

Days 56-60: Python for Data Manipulation

• Day 56: Work with data frames in pandas.
• Day 57: Use pandas for data grouping and aggregation.
• Day 58: Merge, join, and concatenate data in pandas.
• Day 59: Perform time series analysis.
• Day 60: Complete a Mini-project on data manipulation.

Phase 3: Machine Learning Basics & Case Studies (Days 61-90)

Days 61-65: Introduction to Machine Learning

• Day 61: Learn about supervised and unsupervised learning.
• Day 62: Study train-test split and model evaluation.
• Day 63: Introduction to linear regression.
• Day 64: Apply linear regression on a dataset.
• Day 65: Complete a Mini-project: Regression model.

Days 66-70: Classification Basics

• Day 66: Study classification basics and logistic regression.
• Day 67: Learn evaluation metrics (accuracy, precision, recall).
• Day 68: Apply logistic regression to classify data.
• Day 69: Experiment with different datasets.
• Day 70: Complete a Mini-project: Classification problem.

Days 71-75: Clustering Basics

• Day 71: Study clustering methods (K-means, hierarchical).
• Day 72: Implement K-means clustering in Python.
• Day 73: Explore clustering evaluation metrics.
• Day 74: Apply clustering to a real dataset.
• Day 75: Complete a Mini-project: Clustering analysis.

Days 76-80: Time Series Analysis

• Day 76: Learn about time series components.
• Day 77: Study moving averages and trends.
• Day 78: Forecasting basics and models.
• Day 79: Apply time series analysis on stock data.
• Day 80: Complete a Mini-project: Time series forecasting.

Days 81-90: End-to-End Case Study

• Day 81: Select a dataset for a complete analysis.
• Day 82-85: Clean and prepare the data.
• Day 86-88: Explore, analyze, and visualize the data.
• Day 89: Model the data and generate insights.
• Day 90: Document the case study and insights.

Phase 4: Real-World Projects & Portfolio Building (Days 91-100)

Days 91-95: Real-World Project 1

• Day 91-93: Choose a dataset, clean, analyze, and visualize it.
• Day 94: Document findings, and create visualizations.
• Day 95: Upload the project to GitHub.

Days 96-100: Real-World Project 2 & Portfolio Setup

• Day 96-98: Complete a second project and document it.
• Day 99: Organize all projects in a GitHub portfolio.
• Day 100: Write and upload a summary to LinkedIn and showcase your portfolio.

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🎉 Ready to Start?

Commit to daily learning, hands-on projects, and enjoy the journey of becoming a data analyst!