Diploma in Data Analysis

Whats Covered

This comprehensive online course provides a thorough foundation in data analytics, equipping you with the skills to collect, clean, analyze, and interpret data for informed decision-making.

Course Structure:

The course is divided into 15 modules, progressively building your analytical skillset:

Module 1: Introduction to Data Analytics

• What is data analytics?
• Applications of data analytics across industries
• The data analysis lifecycle

Module 2: Understanding Data Types

• Categorical vs. quantitative data
• Levels of measurement (nominal, ordinal, interval, ratio)
• Identifying data types in real-world scenarios

Module 3: Data Collection Methods

• Various data collection techniques (surveys, interviews, web scraping)
• Data extraction from different sources (databases, APIs)
• Ethical considerations in data collection

Module 4: Exploring and Cleaning Data

• Introduction to data cleaning tools (e.g., spreadsheets, SQL)
• Identifying and handling missing values
• Dealing with outliers and inconsistencies

Module 5: Data Visualization Fundamentals

• Choosing the right chart type for different data types
• Creating effective visual representations using tools like bar charts, scatter plots, and heatmaps

Module 6: Introduction to Statistics

• Descriptive statistics (mean, median, standard deviation)
• Understanding probability distributions (normal, binomial, Poisson)
• Introduction to hypothesis testing concepts

Module 7: Correlation and Regression Analysis

• Identifying relationships between variables using correlation coefficients
• Linear regression modeling to predict future values

Module 8: Working with Big Data

• Introduction to big data concepts (volume, variety, velocity)
• Big data storage and processing techniques (e.g., Hadoop)

Module 9: Data Wrangling with Python

• Introduction to Python programming for data analysis
• Using libraries like Pandas for data manipulation and cleaning

Module 10: SQL for Data Analysis

• Writing SQL queries to retrieve data from relational databases
• Joining tables and filtering data for specific needs

Module 11: Introduction to Machine Learning

• Understanding the core concepts of machine learning
• Exploring different machine learning algorithms (supervised, unsupervised)
• Real-world applications of machine learning

Module 12: Data Storytelling

• Communicating insights effectively through data visualizations and reports
• Tailoring presentations for different audiences
• Best practices for data storytelling

Module 13: Business Intelligence (BI) Tools

• Introduction to popular BI tools (e.g., Tableau, Power BI)
• Creating interactive dashboards and reports for informed decision-making

Module 14: Data Ethics and Privacy

• Understanding data privacy regulations (e.g., GDPR)
• Ethical considerations in data collection, analysis, and usage
• Ensuring responsible data practices

Module 15: Capstone Project

• Apply your learned skills to a real-world data analysis project
• Choose a dataset, analyze it, and present your findings

Course Access