Data Analysis and Visualization Masterclass

Module 1: Introduction to Data Analysis

• Understanding the role of data analysis in decision-making and problem-solving.
• Overview of the data analysis process: data collection, cleaning, exploration, analysis, and visualization.
• Introduction to data analysis tools and techniques.

Module 2: Data Collection and Cleaning

• Methods for collecting and sourcing data from various sources.
• Data cleaning techniques: handling missing values, removing duplicates, and data normalization.
• Exploring data quality issues and best practices for data cleaning.

Module 3: Exploratory Data Analysis (EDA)

• Overview of exploratory data analysis (EDA) techniques.
• Visualizing data distributions: histograms, box plots, and density plots.
• Analyzing relationships between variables: scatter plots, correlation analysis, and heatmap visualization.

Module 4: Statistical Analysis

• Introduction to basic statistical concepts: mean, median, mode, variance, and standard deviation.
• Performing statistical tests for hypothesis testing and inference.
• Understanding probability distributions and their applications in data analysis.

Module 5: Data Wrangling and Transformation

• Data wrangling techniques: reshaping, pivoting, and transforming data for analysis.
• Working with datetime data, text data, and categorical data.
• Introduction to data aggregation, grouping, and summarization.

Module 6: Data Visualization

• Principles of effective data visualization.
• Using visualization libraries (e.g., Matplotlib, Seaborn, Plotly) in Python for creating static and interactive visualizations.
• Designing dashboards and interactive plots for data exploration and storytelling.

Module 7: Machine Learning for Data Analysis

• Introduction to machine learning concepts and algorithms.
• Supervised learning techniques: regression and classification.
• Unsupervised learning techniques: clustering and dimensionality reduction.

Module 8: Big Data Analytics

• Overview of big data technologies: Hadoop, Spark, and distributed computing frameworks.
• Performing data analysis on large datasets using Apache Spark.
• Introduction to cloud-based big data analytics platforms (e.g., Google BigQuery, Amazon Redshift).

Module 9: Time Series Analysis

• Understanding time series data and its characteristics.
• Time series decomposition and trend analysis.
• Forecasting techniques: moving averages, exponential smoothing, and ARIMA models.

Module 10: Real-World Data Analysis Projects

• Applying data analysis skills to real-world datasets and scenarios.
• Working on data analysis projects covering various domains (e.g., finance, marketing, healthcare).
• Presenting findings and insights from data analysis projects.