Hey guys I got ChatGPT to make me this syllabus for learning data analysis while I'm in high school, I wanted to see what you think of it and is it feasible, etc

------Month 1: Foundations of Data Analysis & Excel

Goals:

Build foundational knowledge in data analysis and master essential Excel skills.

Learn data cleaning, basic analysis, and pivot tables.

Weekly Breakdown:

Week 1:

Introduction to data analysis concepts (types of data, data life cycle).

Overview of Excel basics: Interface, navigating spreadsheets, basic formulas.

Week 2:

Data cleaning in Excel: Removing duplicates, handling missing data, text-to-columns.

Data formatting and conditional formatting.

Week 3:

Excel functions: SUM, AVERAGE, COUNT, IF, VLOOKUP, and HLOOKUP.

Week 4:

Introduction to pivot tables and pivot charts for data summarization.

Practice with sample datasets, creating reports and basic visualizations.

Suggested Resources:

Excel tutorials (YouTube, LinkedIn Learning, or Microsoft Learn).

Practice datasets from Kaggle or public datasets (e.g., Google Dataset Search)\\

------Month 2: Introduction to SQL

Goals:

Develop basic SQL skills for querying databases.

Understand relational databases, data filtering, and joining tables.

Weekly Breakdown:

Week 1:

Introduction to relational databases and SQL structure.

Basic SQL commands: SELECT, FROM, WHERE.

Week 2:

Filtering and sorting data with WHERE, ORDER BY, and LIMIT.

Basic aggregation functions: COUNT, SUM, AVG, MIN, MAX.

Week 3:

SQL JOINs: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

Combining tables and handling NULL values.

Week 4:

Practice SQL queries on sample datasets.

Building small queries to answer business questions.

Suggested Resources:

SQLZoo, Mode Analytics SQL tutorials, or W3Schools SQL tutorials.

Free SQL practice tools (e.g., Mode Analytics SQL editor or SQLFiddle).

------Month 3: Data Visualization (Excel & Power BI)

Goals:

Learn to visualize data using Excel and Power BI.

Create basic dashboards and understand data visualization principles.

Weekly Breakdown:

Week 1:

Introduction to data visualization concepts (clarity, simplicity, and relevance).

Advanced Excel charts: Scatter plots, histograms, bar charts, line charts.

Week 2:

Basics of Power BI: Connecting to Excel and other data sources.

Creating basic visuals in Power BI (charts, tables).

Week 3:

Power BI: Filters, slicers, and formatting visuals for dashboards.

Building a simple dashboard with sample data.

Week 4:

Practicing Power BI with sample data sets.

Final project: Create a dashboard to showcase insights from data.

Suggested Resources:

Power BI tutorials (Microsoft Learn, LinkedIn Learning).

Sample datasets for practice (Kaggle, Power BI community datasets).

------Month 4: Python for Data Analysis (Pandas & Data Visualization)

Goals:

Get familiar with Python and Pandas for data manipulation.

Use Matplotlib and Seaborn for data visualization.

Weekly Breakdown:

Week 1:

Introduction to Python basics (variables, data types, loops, functions).

Setting up a development environment (Jupyter Notebook or Google Colab).

Week 2:

Introduction to Pandas: DataFrames, Series, reading/writing files (CSV, Excel).

Basic data manipulation: Filtering, selecting, sorting.

Week 3:

Aggregating and grouping data in Pandas.

Handling missing data and data cleaning.

Week 4:

Introduction to Matplotlib and Seaborn for data visualization.

Creating line charts, bar charts, scatter plots, and heatmaps.

Suggested Resources:

"Python for Data Analysis" by Wes McKinney.

Python and Pandas tutorials (Kaggle Learn, DataCamp, or freeCodeCamp).

------Month 5: SQL and Python Project-Based Learning

Goals:

Reinforce SQL and Python skills through projects.

Apply your knowledge to solve real-world data analysis problems.

Weekly Breakdown:

Week 1:

Review and practice SQL queries with sample datasets.

Project 1: Analyzing a sales dataset with SQL (e.g., customer behavior analysis).

Week 2:

Project 2: Data cleaning and analysis with Pandas (e.g., analyze a movie dataset).

Practice manipulating and aggregating data.

Week 3:

Project 3: Visualization project with Matplotlib or Seaborn (e.g., visualize trends in a weather dataset).

Week 4:

Combining SQL and Python: Extract data with SQL, analyze in Python.

Create a mini-report summarizing findings.

Suggested Resources:

Practice datasets (Kaggle, data.world, or other open data sources).

Review SQL and Python documentation for syntax reference.

------Month 6: Advanced Data Analysis & Portfolio Building

Goals:

Develop advanced analysis skills and build a portfolio.

Apply knowledge to a final project that showcases all key skills.

Weekly Breakdown:

Week 1:

Introduction to relational database design (basic ER diagrams).

Overview of ETL (Extract, Transform, Load) processes.

Week 2:

Final Project Planning: Identify a dataset, outline analysis steps.

Work on data cleaning and preparation (SQL or Python).

Week 3:

Conduct analysis and visualization (Excel, Power BI, or Python).

Interpret results and compile insights.

Week 4:

Create a final portfolio with your projects (GitHub or personal website).

Practice presenting your analysis and explaining your insights.

Suggested Resources:

Portfolio creation platforms: GitHub, Tableau Public (for dashboards).

Final project resources: Public datasets, personal blog or LinkedIn for sharing.

------Completion Goals

By the end of 6 months, you should:

Have a solid foundation in Excel, SQL, Power BI, and Python.

Be able to perform basic data analysis, visualization, and reporting.

Have a small portfolio showcasing your projects, which is key for entry-level job applications.