Module 1: Introduction to Data Analytics
Duration: 1 week
Objective: Understand the basics of data analytics and its role in
decision-making.
Topics Covered:
1. What is Data Analytics?
- Definition, types of data (structured vs unstructured), and applications.
2. The Data Analytics Lifecycle
- Steps from data collection to actionable insights.
3. Introduction to Key Tools:
- Overview of Excel, Python, SQL, and Tableau/Power BI.
4. The Importance of Data in Business Decision-Making.
Activities:
- Activity 1: Research and identify 3 companies that rely heavily on
data analytics and describe how they benefit from it.
Quiz: Short quiz on data analytics definitions and basic concepts.
Module 2: Data Collection and Preparation
Duration: 2 weeksObjective: Learn how to collect, clean, and organize data for analysis.
Topics Covered:
1. Data Collection Methods:
- Surveys, web scraping, APIs, and databases.
2. Introduction to Excel:
- Data entry, formatting, sorting, and filtering.
3. Data Cleaning Techniques:
- Handling missing values, duplicates, outliers, and data normalization.
4. Introduction to Python for Data Cleaning:
- Using Pandas to clean and preprocess datasets.
Datasets:
- Sales data, customer feedback, and product pricing datasets.
Activities:
- Activity 1: Use Excel to clean a raw dataset (e.g., sales data) and perform basic descriptive analysis.
- Activity 2: Use Python (Pandas) to clean a dataset by removing missing values and normalizing data.
Quiz: Multiple-choice and hands-on questions covering data preparation techniques.
Module 3: Exploratory Data Analysis (EDA)
Duration: 2 weeksObjective: Understand how to explore data using statistical techniques and data visualization.
Topics Covered:
1. Descriptive Statistics:
- Mean, median, mode, standard deviation, and variance.
2. Data Visualization in Excel & Python:
- Creating bar charts, line graphs, histograms, and scatter plots.
3. Correlation and Covariance:
- Finding relationships between variables.
4. Introduction to Data Visualization Tools (Tableau/Power BI).
Datasets:
- Financial performance data, customer segmentation data.
Activities:
- Activity 1: Use Excel to perform basic statistical analysis (mean, median, mode).
- Activity 2: Visualize trends using Python’s Matplotlib and Seaborn libraries.
- Activity 3: Create a sales dashboard using Tableau/Power BI.
Quiz: Statistical analysis and EDA concepts.
Module 4: Introduction to Databases and SQL
Duration: 2 weeksObjective: Learn to extract and analyze data using SQL.
Topics Covered:
1. Introduction to Relational Databases:
- Tables, keys, and relationships.
2. SQL Basics:
- SELECT, WHERE, JOIN, GROUP BY, HAVING, and aggregate functions.
3. Advanced SQL Queries:
- Subqueries, window functions, and optimizing queries.
4. Connecting Python with SQL Databases:
- Extracting data from SQL databases for analysis in Python.
Datasets:
- E-commerce transactional data, employee database.
Activities:
- Activity 1: Write SQL queries to extract, join, and filter data from a sample database.
- Activity 2: Use SQL queries in combination with Python (Pandas) to analyze a dataset.
Quiz: SQL commands and relational database concepts.