# Data Analyst Roadmap

These are the roadmap to become a data analyst:

1. <mark style="color:blue;">Foundation Skills:</mark>

* Strengthen Mathematics: Focus on statistics relevant to data analysis

  * Descriptive Statistics
  * Inferential Statistics: Hypothesis Testing,..

  <mark style="color:purple;">**💡**</mark> Book Reference: <https://drive.google.com/file/d/142tPq9LRyu3cU9D2rNz301gdOQPotHVJ/view?usp=sharing>
* Excel Basics: Master fundamental Excel function and formulas.

2. <mark style="color:blue;">SQL Proficiency:</mark>

* Learn SQL Basics: Understand SELECT statements, Joins, and Filtering
* Practice Database Queries: Work with database to retrieve and manipulate data

3. <mark style="color:blue;">Excel Advanced Techniques:</mark>

* Data Cleaning in Excel: Learn to handle missing data and outliers, duplicated data.
* PivotTables and Pivot Charts: Master these powerful tools for data summarization.

4. <mark style="color:blue;">Data Visualization with Excel:</mark>

* Create Visualizations: Learn to build charts and graphs in Excel
* Dashboard creation: Understand how to design effective dashboards.

5. <mark style="color:blue;">Power BI Introduction:</mark>

* Install and Explore Power BI: Familiarize yourself with the interface.&#x20;
* Import Data: Learn to import and transform data using Power Bl.

6. <mark style="color:blue;">Power Bl Data Modeling:</mark>&#x20;

\*Relationships: Understand and establish relationships between tables.&#x20;

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

7. <mark style="color:blue;">Advanced Power Bl Features:</mark>&#x20;

* Advanced Visualizations: Explore complex visualizations in Power BI.&#x20;
* A Custom Measures and Columns: Utilize DAX for customized data calculations.

8. <mark style="color:blue;">Integration of Excel, SQL, and Power BI</mark>:

* Importing Data from SQL to Power BI: Practice connecting and importing data.&#x20;
* Excel and Power Bl Integration: Learn how to use Excel data in Power BI.

9. <mark style="color:blue;">Python for Data Analyst</mark>:

* Basic Syntax of Python
* **Exploratory Data Analysis**: Understand how important of EDA and how to do EDA:

  <figure><img src="/files/bdbjcOjsQcyEY6dq9dOS" alt=""><figcaption><p>What is EDA?</p></figcaption></figure>

  <figure><img src="/files/A0WgsCO1hotJBuEuJwD8" alt=""><figcaption><p>EDA Tools</p></figcaption></figure>

  <figure><img src="/files/OY3SQWTrl7Zd1H80ADaF" alt=""><figcaption><p>Type of EDA</p></figcaption></figure>

<mark style="color:purple;">**💡**</mark> Reference: <https://www.kaggle.com/discussions/general/329404>

<mark style="color:purple;">**💡**</mark>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,&#x20;
* Time Series
* Machine Learning&#x20;

  <figure><img src="/files/r169XMQ5ro37qZzYGFu9" alt=""><figcaption></figcaption></figure>

  <mark style="color:purple;">**💡**</mark>Book References: <https://drive.google.com/file/d/1oY5GWO8YxWt1cPJdXWn-aIHQxvhMn7j-/view?usp=sharing>

10. &#x20;<mark style="color:blue;">Case Study: Try real world case studies project with examples</mark>

    1. Walmart:&#x20;

       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:&#x20;

       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

11. <mark style="color:blue;">Business Intelligence Best Practices:</mark>&#x20;

* Data Storytelling: Develop skills in presenting insights effectively.&#x20;
* Performance Optimization: Optimize reports and dashboards for efficiency.

12. <mark style="color:blue;">Build Portfolio</mark>

* Showcase Excel Projects: Highlight your data analysis skills using Excel.&#x20;
* Power BI Projects: Feature Power BI dashboards and reports in your portfolio.
* Showcase Data Analysis Project with Python


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