Data Analyst Roadmap

Roadmap to become a data analyst

These are the roadmap to become a data analyst:

1. Foundation Skills:

• Strengthen Mathematics: Focus on statistics relevant to data analysis

  • Descriptive Statistics

  • Inferential Statistics: Hypothesis Testing,..

  💡 Book Reference: https://drive.google.com/file/d/142tPq9LRyu3cU9D2rNz301gdOQPotHVJ/view?usp=sharing

• Excel Basics: Master fundamental Excel function and formulas.

1. SQL Proficiency:

• Learn SQL Basics: Understand SELECT statements, Joins, and Filtering

• Practice Database Queries: Work with database to retrieve and manipulate data

1. Excel Advanced Techniques:

• Data Cleaning in Excel: Learn to handle missing data and outliers, duplicated data.

• PivotTables and Pivot Charts: Master these powerful tools for data summarization.

1. Data Visualization with Excel:

• Create Visualizations: Learn to build charts and graphs in Excel

• Dashboard creation: Understand how to design effective dashboards.

1. Power BI Introduction:

• Install and Explore Power BI: Familiarize yourself with the interface.

• Import Data: Learn to import and transform data using Power Bl.

1. Power Bl Data Modeling:

*Relationships: Understand and establish relationships between tables.

• DAX (Data Analysis Expressions): Learn the basics of DAX for calculations.

1. Advanced Power Bl Features:

• Advanced Visualizations: Explore complex visualizations in Power BI.

• A Custom Measures and Columns: Utilize DAX for customized data calculations.

1. Integration of Excel, SQL, and Power BI:

• Importing Data from SQL to Power BI: Practice connecting and importing data.

• Excel and Power Bl Integration: Learn how to use Excel data in Power BI.

1. Python for Data Analyst:

• Basic Syntax of Python

• Exploratory Data Analysis: Understand how important of EDA and how to do EDA:

  

  What is EDA?

  

  EDA Tools

  

  Type of EDA

💡 Reference: https://www.kaggle.com/discussions/general/329404

💡Reference of EDA: https://www.kaggle.com/code/chemistahmedkamel/eda-diabetes-prediction-with-lowest-error/notebook

• NumPy Array

• Jupyter Notebook

• Data Loading, Storage, File Format

• Data Cleansing and Preparation with Pandas

• Data Wrangling: Join, Combine, and Reshape

• Plotting and Visualization,

• Time Series

• Machine Learning

  

  💡Book References: https://drive.google.com/file/d/1oY5GWO8YxWt1cPJdXWn-aIHQxvhMn7j-/view?usp=sharing

1. Case Study: Try real world case studies project with examples

   1. Walmart:

      dataset: https://www.kaggle.com/datasets/yasserh/walmart-dataset

      Apply Prediction: https://www.kaggle.com/code/yasserh/walmart-sales-prediction-best-ml-algorithms
   2. Netflix:

      dataset: https://www.kaggle.com/datasets/shivamb/netflix-shows

      Visualization, Recommendation, EDA: https://www.kaggle.com/code/niharika41298/netflix-visualizations-recommendation-eda

   YouTube channel for machine learning project with deployment: https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7clOTtwsp94PcHbzqpAdg

   Project Cover:

   1. Data cleaning

   2. Feature engineering

   3. Model Building

   4. Building Website for price prediction

   5. Deployment to AWS

1. Business Intelligence Best Practices:

• Data Storytelling: Develop skills in presenting insights effectively.

• Performance Optimization: Optimize reports and dashboards for efficiency.

1. Build Portfolio

• Showcase Excel Projects: Highlight your data analysis skills using Excel.

• Power BI Projects: Feature Power BI dashboards and reports in your portfolio.

• Showcase Data Analysis Project with Python