Data Analysis and Visualization Masterclass

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About Course

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.
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