Data Analysis

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About Course

Data Analysis is the process of transforming raw data into meaningful insights that help individuals and businesses make smarter decisions. In this course, you will learn how to collect, clean, analyze, and interpret data using modern tools and techniques used by professionals. Whether you’re a beginner or looking to upgrade your skills, this course will guide you step-by-step into the world of data. You’ll gain both theoretical understanding and practical knowledge that can be applied in real-life scenarios, opening doors to job opportunities, freelancing, and career growth in tech

 

What Will You Learn?

  • • How to understand and work with different types of data
  • • The complete data analysis process from start to finish
  • • How to clean and prepare messy data for analysis
  • • How to use tools like Excel, SQL, and Python for data analysis
  • • How to identify patterns, trends, and insights from data
  • • How to visualize data using charts and dashboards
  • • The difference between data-driven decisions and intuition
  • • How to collect data from primary and secondary sources
  • • Basics of databases and how data is stored
  • • How to present findings clearly for decision-making
  • • Real-world problem-solving using analytical thinking

Course Content

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Week 1 (Introduction to Data Analytics)
This week introduces students to the fundamentals of data analytics, including what data is and how it is used to make informed decisions. Learners will understand the importance of data in solving real-world problems and driving business insights. By the end of the week, students will be familiar with the basic concepts of data analysis, the workflow involved, and how analysts approach data to extract meaningful information.

WEEK 2: Data Sourcing & SQL Fundamentals
This week focuses on how data is sourced, collected, and stored, along with an introduction to SQL as a tool for managing and querying data. Students will learn how to identify reliable data sources and understand how databases are structured. By the end of the week, learners will be able to write basic SQL queries to retrieve and manipulate data, giving them a strong foundation for working with real-world datasets.

Week 3 (Data Cleaning & Wrangling (Excel/SQL))
This week focuses on preparing raw data for analysis by cleaning, organizing, and transforming it into a usable format. Students will learn how to identify errors, handle missing values, and structure datasets using tools like Excel and SQL. By the end of the week, learners will be able to perform data wrangling techniques that ensure accuracy and consistency, making data ready for meaningful analysis and insights.

Week 4 (Introduction to Visualization Tools (Power BI/Tableau))
This week introduces students to data visualization tools such as Power BI and Tableau, focusing on how to turn raw data into clear and insightful visual representations. Learners will understand how charts, graphs, and dashboards help communicate data effectively. By the end of the week, students will be able to create basic visualizations and interactive dashboards, enabling them to present data in a way that supports decision-making and storytelling.

Week 5 (Advanced Visualization & Data Storytelling)
This week focuses on creating advanced visualizations and using data storytelling techniques to communicate insights effectively. Students will learn how to design compelling dashboards, choose the right visuals, and highlight key patterns and trends in data. By the end of the week, learners will be able to present data in a clear, engaging, and impactful way, helping stakeholders understand insights and make informed decisions.

Week 6 (Final Analysis, Documentation & Portfolio)
This week focuses on bringing together all the skills learned throughout the course to complete a full data analysis project. Students will analyze datasets, document their process, and organize their findings in a clear and professional manner. By the end of the week, learners will be able to present their analysis as a portfolio-ready project, showcasing their ability to solve real-world problems using data and communicate results effectively.

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