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FUNDAMENTALS OF DATA ANALYSIS (BASIC AND ADVANCE) | FDABA

The Foundations of Data Analysis course provides a comprehensive introduction to essential principles and techniques for interpreting data effectively. Designed for beginners, the course covers basic...

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  • English

Course Description

The Foundations of Data Analysis course provides a comprehensive introduction to essential principles and techniques for interpreting data effectively. Designed for beginners, the course covers basic data manipulation with Excel, data visualization using Tableau, and data cleaning with SQL. Participants will delve into statistical analysis, including hypothesis testing and A/B testing, and expl...

The Foundations of Data Analysis course provides a comprehensive introduction to essential principles and techniques for interpreting data effectively. Designed for beginners, the course covers basic data manipulation with Excel, data visualization using Tableau, and data cleaning with SQL. Participants will delve into statistical analysis, including hypothesis testing and A/B testing, and explore advanced techniques like regression analysis, time series analysis, and machine learning. Through a capstone project, participants will apply learned concepts to real-world datasets, crafting comprehensive data analysis reports. Recommended resources and industry case studies supplement learning. Upon completion, participants are prepared for roles such as Business Analyst, Data Scientist, Financial Analyst, and more. No prior experience required, making it ideal for those seeking a foundational understanding of data analysis. TOOLS: • Python • Jupyter Notebooks • SQL Database • Tableau • Git • Machine Learning Libraries • Cloud Services • Kaggle • Browser CERTIFICATIONS: • Oracle Foundation Certificate – paid • Advance Excel Certificate • MySQL Certificate • Tableau certificate – paid • Data scientist Certificate • Data Analyst Certificate • Live Project Certificate

What you’ll learn
  • Introduction to Data Analysis
  • Excel Basics
  • Data Visualization Principles
  • Tableau Usage
  • Data Cleaning Techniques
  • SQL Fundamentals
  • Descriptive Statistics
  • Inferential Statistics
  • Data Visualization Principles
  • A/B Testing
  • Advanced Techniques

Covering Topics

1
Module 1: Introduction to Data Analysis (2 weeks)
2
Module 2: Data Visualization (3 weeks)
3
Module 3: Data Cleaning and Wrangling (2 weeks)
4
Module 4: Statistical Analysis (4 weeks)
5
Module 5: Advanced Data Analysis Techniques (3 weeks)
6
Module 6: Capstone Data Analysis Project (4 weeks)
7
Live Projects

Curriculum

      Introduction to Data Analysis (2 weeks)

• Introduction to Data Analysis
•	Definition and importance in decision-making
•	Applications across industries

• Excel for Data Analysis
•	Basics of Excel
•	Data cleaning and manipulation using Excel functions
      Data Visualization (3 weeks)

• Principles of Data Visualization
•	Best practices in visualizing data
•	Common pitfalls to avoid

• Graphs and Charts with Tableau
•	Introduction to Tableau
•	Creating interactive visualizations
      Data Cleaning and Wrangling (2 weeks)

• Data Cleaning Techniques
•	Identifying and handling missing data
•	Dealing with outliers

• Data Wrangling with SQL
•	Basic SQL queries for data extraction
•	Joining and aggregating data
      Statistical Analysis (4 weeks)

• Introduction to Machine Learning
•	Supervised vs. unsupervised learning
•	Types of machine learning algorithms
•	Model Training and Evaluation
•	Splitting datasets
•	Cross-validation

• Supervised Learning Algorithms
•	Linear regression, logistic regression
•	Decision trees, random forests

• Unsupervised Learning Algorithms
•	Clustering (K-means, hierarchical)
•	Dimensionality reduction (PCA)
      Advanced Data Analysis Techniques (3 weeks)

• Regression Analysis
•	Simple and multiple regression
•	Interpretation of regression coefficients

• Introduction to Machine Learning for Data Analysts
•	Overview of machine learning algorithms
•	Application of machine learning in data analysis

• Time Series Analysis with Python
•	Basics of time series data
•	Forecasting techniques
      Capstone Data Analysis Project (4 weeks)

• Capstone Project
•	Analyzing and interpreting a real-world dataset
•	Creating a comprehensive data analysis report

Frequently Asked Questions

A: Yes, our courses are designed to be accessible both online and offline. You can choose your preferred mode of learning based on your convenience and availability of internet connectivity.

A: The course is tailored for beginners seeking foundational knowledge in data analysis.

A: Participants need a computer with internet access to engage with course materials and complete exercises.

A: No, this course is designed for beginners, so no prior experience in data analysis is required.

A: Basic computer literacy is recommended for navigating the course platform and completing exercises.

A: Enthusiasm for learning and a willingness to engage in hands-on exercises and projects are essential for success.

A: Access to Microsoft Excel and Tableau software is preferred but not mandatory. Alternative tools may be provided for practice if needed.