🟢 What is data analytics? Roles and responsibilities

🟢 Tools overview: Excel, SQL, Python, BI tools

🟢 Career paths: Analyst vs Scientist vs Engineer

🟢 Project::

1. Write a 500-word paper presenation explaining the difference between a Data Analyst, Data Scientist, and Data Engineer.
2. List at least 5 tools used in data analytics and mention one use-case for each.

🟢 Key functions: VLOOKUP, IF, INDEX-MATCH

🟢 Pivot tables, charts, and dashboards

🟢 Basic data cleaning

🟢 Case study: Sales report analysis

🟢 Project:

1. Build a Sales Dashboard in Excel using sample or dummy sales data.
2. Use formulas like VLOOKUP, IF, and INDEX-MATCH.
3. Add pivot tables and charts to show region-wise performance and monthly trends.
4. Perform basic data cleaning on the Excel file.



🟢 SELECT, WHERE, GROUP BY, JOIN

🟢 Subqueries, CTEs, Window Functions

🟢 Real-world datasets (HR, e-commerce, etc.)



🟢 Project:

1. Design and query a small database:
2. Run analytical queries using JOINs
3. Group data and calculate KPIs
4. Rank top products/customers using window functions

🟢 Python basics: variables, loops, functions

🟢 Libraries: pandas, numpy, matplotlib, seaborn

🟢 Reading/writing files (CSV, JSON, Excel)

🟢 Automating tasks

🟢 Project:

1. Create a script to:
2. Read and clean a dataset
3. Use dictionaries and loops to calculate summaries
4. Plot a simple graph using matplotlib
5. Save a cleaned version of the file

🟢 Handling missing values, duplicates, outliers

🟢 Data type conversion

🟢 Date/time parsing

🟢 Performance tricks in pandas

🟢 Project:

1. Clean a messy dataset (e.g., housing or sales data):
2. Remove or impute missing values
3. Convert data types and clean columns
4. Save and document the cleaned dataset

🟢 Principles of effective visualization

🟢 Seaborn and Matplotlib basics

🟢 Dashboards with Plotly or Streamlit

🟢 Data storytelling techniques

🟢 Project:

1. Create visualizations (bar, scatter, heatmap) for key metrics
2. Build a mini dashboard with filters using Plotly or Streamlit
3. Annotate charts to support decision-making

🟢 Descriptive statistics: mean, median, std deviation

🟢 Probability distributions

🟢 Hypothesis testing: t-test, chi-square

🟢 Correlation & linear regression

🟢 Project:

1. Analyze a dataset statistically:
2. Calculate key statistics
3. Perform a hypothesis test (e.g., compare groups)
4. Run simple linear regression and interpret results

🟢 Univariate and bivariate analysis

🟢 Detecting patterns and relationships

🟢 Summary insights and visual reporting

🟢 Real-world datasets (e.g., Titanic, COVID-19)

🟢 Project:

1. Perform full EDA on a real dataset:
2. Explore variables, identify trends
3. Create summary charts
4. Write a short insight report with visuals

🟢 Connecting to data sources

🟢 Interactive dashboards

🟢 Drill-downs, filters, calculated fields

🟢 Dashboard design for storytelling

🟢 Project:

1. Build a business dashboard with at least:
2. 3 charts
3. Filters/slicers
4. A KPI card or calculated field
5. Publish or export a presentation-ready version

🟢 End-to-end data analysis project

🟢 Resume preparation & GitHub portfolio

🟢 LinkedIn profile setup

🟢 Mock interviews and feedback

🟢 Capstone Project:

1. Choose a real dataset
2. Clean, analyze, and visualize data
3. Build a final dashboard or web app (Power BI, Tableau, or Streamlit)
4. Upload to GitHub with project documentation
5. Submit updated resume and LinkedIn profile
6. Participate in a mock interview session