Roadmap to learn Data Analysis ||
Roadmap to Learn Data Analysis:👉
Introduction to Data Analysis
What is Data Analysis?
Importance and applications of Data Analysis.
Essential Tools and Technologies
Introduction to Excel for data manipulation.
Basics of Python programming.
Libraries: Pandas, NumPy for data manipulation.
Data Cleaning and Preprocessing
Handling missing data.
Removing duplicates.
Data transformation and normalization.
Exploratory Data Analysis (EDA)
Summary statistics. - Data visualization using Matplotlib and Seaborn.
Univariate and bivariate analysis.
Statistical Concepts
Probability distributions. - Hypothesis testing.
Correlation and causation.
Data Visualization
Advanced data visualization techniques.
Interactive visualizations using libraries like Plotly.
SQL for Data Analysis
Basic SQL queries for data extraction. - Joins and subqueries.
Aggregating and summarizing data.
Introduction to Machine Learning
Supervised vs. unsupervised learning.
Classification and regression.
Overfitting, underfitting, and model evaluation.
Introduction to Time Series Analysis
-Handling time-stamped data.
Seasonality and trends.
Time series forecasting.
Advanced Topics
Dimensionality reduction (PCA, t-SNE). - Text mining and sentiment analysis.
Introduction to clustering.
👉BONUS
⏺️Projects to Add to Resume:
1. Exploratory Data Analysis of a Dataset 2. Sales Analysis and Forecasting
3. Customer Segmentation 4. Movie Recommendations System
5. Financial Data Analysis 6. Social Media Sentiment Analysis
7. Healthcare Data Analysis
8. A/B Testing for Website Optimization
9. Stock Price Prediction (Time Series)
10. Crime Pattern Analysis (Spatial Analysis)
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