Data Analyst

Overview

The Data Analyst course provides a comprehensive foundation in data analysis techniques and tools. Learners will explore SQL for database querying and management, delve into Python for data manipulation and automation, and gain proficiency in data visualization using Matplotlib and Seaborn. The curriculum also introduces machine learning basics and covers advanced analytics through tools like Power BI and VBA macros. Through structured modules, students will build practical skills needed for real-world data analysis tasks.

Objectives

• Master core SQL concepts including joins, subqueries, and performance optimization for data extraction and manipulation.
• Apply Python fundamentals and libraries such as NumPy and Pandas to clean, transform, and analyze datasets effectively.

Key topics

• SQL Queries and Database Management
• Python for Data Manipulation
• Data Visualization with Matplotlib and Seaborn
• Machine Learning Fundamentals

Expected outcomes

• Create interactive dashboards using Power BI and generate actionable business insights from data.
• Perform statistical analysis and apply probability distributions to support data-driven decision-making.

How the AI Tutor can help

How the Skillama AI Tutor helps with the listed lectures: explanations, follow-up questions, and running or debugging practice code.

Who this is for

This course is designed for individuals seeking to develop practical skills in data analysis using tools like SQL, Python, and Power BI.

Curriculum

1. 1. SQL

   • Introduction to SQL
   • Database Normalization
   • Entity-Relationship Model
   • SQL Operators
   • Joins, Tables & Variables
   • SQL Functions
   • Subqueries
   • Views & Stored Procedures
   • User-Defined Functions
   • SQL Performance & Optimization
   • Advanced SQL Concepts
   • Correlated Subqueries
   • Grouping Sets

2. 2. Statistics

   • Descriptive Statistics
   • Mean, Median & Mode
   • Charts
   • Introduction to Probability
   • Probability in Business Analytics
   • Probability Distributions
   • Binomial Distribution
   • Poisson Distribution
   • Normal Distribution

3. 3. Python Fundamentals

   • Python Introduction
   • Features & Advantages of Python
   • Python Installation
   • Anaconda & Python IDE
   • Basic Python Commands
   • Data Types & Variables
   • Keywords
   • Functions
   • Lambda Expressions
   • Classes & Objects
   • Loops

4. 4. Python Operators & Data Structures

   • Arithmetic Operators
   • Relational Operators
   • Logical Operators
   • Identity Operators
   • Bitwise Operators
   • Assignment Operators
   • Operator Precedence
   • Type Casting
   • Lists
   • Tuples
   • Sets & Frozensets
   • Dictionaries

5. 5. NumPy & Pandas

   • NumPy Arrays
   • Array Functions
   • Array Indexing
   • Array Operations
   • Pandas Series
   • Pandas DataFrames
   • Missing Data
   • GroupBy Operations
   • Merging DataFrames

6. 6. Data Visualization

   • Matplotlib Basics
   • Figure & Axes
   • Plot Customization
   • Seaborn
   • Distribution Plots
   • Bar Charts
   • Scatter Plots
   • Box Plots
   • Heatmaps

7. 7. Big Data & Spark

   • Big Data Introduction
   • Distributed Computing
   • Hadoop
   • Hive
   • HiveQL
   • Spark Introduction
   • Spark Architecture
   • RDD
   • Spark SQL

8. 8. Machine Learning

   • Machine Learning Introduction
   • ML Process
   • Supervised Learning
   • Unsupervised Learning
   • Reinforcement Learning
   • Scikit-Learn
   • Predictive Analytics
   • Text Mining
   • Sentiment Analysis

9. 9. Generative AI

   • Generative AI Introduction
   • Generative Models
   • Introduction to GPT
   • GPT Evolution
   • Transformers
   • Attention Mechanism
   • Introduction to ChatGPT
   • ChatGPT Use Cases
   • Building Chatbots

10. 10. Data Preparation

    • Importing Data
    • Data Types
    • Data Cleaning
    • Filtering Data
    • Missing Data
    • Data Transformation
    • Joining Data
    • Data Blending
    • Combining Data Sources

11. 11. Power BI

    • Power BI Introduction
    • Power BI Desktop
    • Data Sources
    • Data Connections
    • Query Editor
    • Data Transformation
    • Creating Datasets
    • Reports
    • Dashboards

12. 12. Advanced Data Preparation & Analytics

    • Creating & Using Macros
    • Batch Macros
    • Iterative Macros
    • Parsing & Restructuring Data
    • Advanced Joins
    • Blending Large Datasets
    • R & Python Integration
    • Advanced Predictive Modeling
    • Text Mining
    • Sentiment Analysis

13. 13. VBA

    • VBA Overview
    • Excel Macros
    • VBA Terms
    • Comments
    • Operators
    • Decision Statements
    • Loops
    • Strings
    • Arrays
    • Date & Time