About BrightLight Data Analytics

Data Analytics is the process of examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It's used to uncover patterns, trends, and relationships within data, allowing businesses and organizations to make informed, data-driven decisions. 

In today’s data-driven world, mastering the fundamentals of data analytics is key to making informed business decisions. This comprehensive course will guide you through the core principles of data analysis, focusing on essential skills like data collection, cleaning, statistical analysis, and visualization. Designed for beginners to intermediate learners, the course covers key tools and techniques, helping you develop practical, real-world data analytics skills that can be immediately applied in a business setting.

Through a blend of theory and hands-on practice, you will learn to work with real data sets, utilizing tools like Microsoft Excel, Power BI, and SQL to uncover insights. By the end of the course, you'll be equipped to create compelling data visualizations, manage and manipulate databases, and communicate findings effectively to stakeholders.

 Module 1: Introduction to Data Analytics

• Overview of data analytics and its role in business
• Types of data (structured vs unstructured)
• The data analytics process: Collect, clean, analyze, visualize, and interpret
• Key tools in data analytics

Module 2: Data Collection and Storage

• Methods for collecting data
• Understanding databases and data storage solutions
• Introduction to relational databases
• Overview of data types and their relevance in business applications

Module 3: Data Wrangling and SQL Fundamentals

• Introduction to SQL
• Data extraction, cleaning, and manipulation with SQL
• Writing basic SQL queries: SELECT, JOIN, GROUP BY, and filtering
• Advanced data wrangling techniques in SQL

Module 4: Statistical Analysis in Data Analytics

• Introduction to statistical concepts
• Descriptive vs inferential statistics
• Measures of central tendency (mean, median, mode)
• Basic hypothesis testing and probability
• Correlation and regression analysis

Module 5: Data Privacy and Ethics

• Understanding data privacy laws (GDPR, CCPA)
• Ethical considerations in data analysis
• Data anonymization and de-identification techniques
• Best practices for responsible data use

Module 6: Data Visualization with Microsoft Excel

• Basic chart types and when to use them
• Data manipulation and visualization techniques in Excel
• Creating dynamic dashboards
• Advanced Excel functions for analysis (VLOOKUP, Pivot Tables, etc.)

Module 7: Advanced Data Visualization in Power BI

• Overview of Power BI and its capabilities
• Connecting and transforming data in Power BI
• Designing and publishing interactive reports and dashboards
• Introduction to DAX functions for advanced calculations

Module 8: Developing a Data Analytics Portfolio

• Building a portfolio to showcase your data analysis projects
• Best practices for presenting data insights to business stakeholders
• Leveraging your portfolio for career growth
• Tips for continuous learning and staying current in the field of data analytics

• Data analysis and interpretation
• Data cleaning, wrangling, and preparation
• Data aggregation and summarization techniques
• SQL for data manipulation and extraction
• Creating data visualizations in Excel and Power BI
• Building interactive dashboards and reports
• Developing a professional portfolio to demonstrate your skills

By completing this course, you’ll have the essential knowledge and practical experience to confidently apply data analytics techniques and tools in real-world business environments. Whether you are looking to upskill in your current job or transition to a new data-centric role, this course will set you up for success. 

 Data analytics is a rapidly growing field, and the demand for skilled data professionals continues to increase. As businesses and organizations become more data-driven, the career opportunities for data analysts are expanding across various industries  

• Role: Data analysts interpret and analyze data to provide actionable insights. They clean, organize, and visualize data using tools like Excel, Power BI, or SQL to help businesses make informed decisions.
• Industries: Finance, Healthcare, Retail, Telecommunications,  Marketing, e-commerce, Government, and more.
• Skills Needed: SQL, Excel, data visualization, basic statistics, data wrangling.

This program is designed for beginners with no prior experience in coding or statistics. It is suitable for people from all backgrounds, and you do not need any previous knowledge of Computer Science or advanced Mathematics to enroll.