Data Analyst Course

Module 1: Introduction to Data Analysis

Lesson 1: Overview of Data Analysis

• Definition of Data Analysis
• Importance of Data Analysis in decision-making
• Role of a Data Analyst in various industries

Lesson 2: Types of Data

• Understanding structured and unstructured data
• Different data sources: databases, spreadsheets, APIs
• Overview of data formats: CSV, JSON, Excel

Lesson 3: Basics of Statistics

• Descriptive statistics: mean, median, mode
• Dispersion measures: range, variance, standard deviation
• Inferential statistics: hypothesis testing, confidence intervals

Module 2: Data Cleaning and Preprocessing

Lesson 1: Data Cleaning Techniques

• Identifying and handling missing data
• Dealing with outliers
• Removing duplicate records

Lesson 2: Data Transformation

• Data normalization and standardization
• Handling categorical data: encoding techniques
• Feature scaling for machine learning models

Lesson 3: Exploratory Data Analysis (EDA)

• Visualizing data distributions
• Correlation analysis
• Uncovering patterns and trends

Module 3: Data Visualization

Lesson 1: Introduction to Data Visualization

• Importance of visualization in data analysis
• Types of charts and graphs
• Choosing the right visualization for different scenarios

Lesson 2: Tools for Data Visualization

• Overview of popular visualization tools (e.g., Tableau, Matplotlib, Seaborn)
• Creating interactive visualizations
• Customizing visualizations for effective communication

Module 4: Introduction to SQL for Data Analysts

Lesson 1: Basics of SQL

• Understanding databases and tables
• SELECT statements and querying data
• Filtering, sorting, and aggregating data

Lesson 2: Advanced SQL Queries

• JOIN operations for combining data from multiple tables
• Subqueries and nested queries
• Data manipulation using UPDATE, INSERT, DELETE statements

Module 5: Introduction to Python for Data Analysis

Lesson 1: Python Basics

• Introduction to Python programming language
• Data types, variables, and basic operations
• Control flow and loops

Lesson 2: Data Manipulation with Pandas

• Introduction to Pandas library
• DataFrames and Series
• Data cleaning and manipulation using Pandas

Lesson 3: Data Visualization with Matplotlib and Seaborn

• Creating static and interactive plots
• Customizing visualizations in Python
• Combining data analysis and visualization in Python

Module 6: Introduction to Machine Learning

Lesson 1: Basics of Machine Learning

• Overview of machine learning concepts
• Types of machine learning: supervised, unsupervised, and reinforcement learning
• Applications of machine learning in data analysis

Lesson 2: Building and Evaluating Models

• Splitting data into training and testing sets
• Model training and evaluation
• Common machine learning algorithms for data analysis