Introduction to Data Analysis

• What is Data Analysis?

• The Role of Data Analysts

• Types of Data and Data Sources

• Overview of Data Analysis Tools

Data Collection and Data Cleaning

• Methods for Data Collection

• Understanding Data Formats (CSV, Excel, JSON, SQL)

• Data Cleaning Techniques: Handling Missing Values, Removing Duplicates

• Introduction to Data Preprocessing

Exploratory Data Analysis (EDA)

• Visualizing Data with Charts and Graphs

• Descriptive Statistics (Mean, Median, Mode, Standard Deviation)

• Identifying Trends and Outliers

• Using Python and R for EDA

Data Visualization

• Principles of Data Visualization

• Creating Effective Data Visualizations

• Tools for Visualization: Tableau, Power BI, and Python Libraries (Matplotlib, Seaborn)

• Storytelling with Data

Statistical Analysis

• Introduction to Probability

• Hypothesis Testing

• Regression Analysis

• Correlation and Causation

Data Modeling

• Introduction to Data Modeling Techniques

• Linear and Logistic Regression

• Decision Trees and Random Forests

• Model Evaluation Metrics (Accuracy, Precision, Recall)

Data Analytics with Excel and SQL

• Advanced Excel Functions for Data Analysis

• SQL for Data Extraction and Querying

• Filtering, Aggregating, and Joining Data

• Database Management Fundamentals

Advanced Data Analysis Techniques

• Time Series Analysis

• Cluster Analysis

• Predictive Analytics and Machine Learning Basics

Big Data and Cloud Computing for Data Analytics

• Working with Big Data (Hadoop, Spark)

• Introduction to Cloud Platforms (AWS, Google Cloud, Azure)

• Using Cloud Tools for Data Analysis

Capstone Project

• Real-world Data Analysis Project

• Presenting Insights to Stakeholders