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