Data Analysis
( 24 Modules )

Module #1
Introduction to Data Analysis
Overview of data analysis, importance, and applications

Module #2
Data Types and Sources
Types of data, data sources, and data collection methods

Module #3
Data Preparation and Cleaning
Importance of data cleaning, dealing with missing values, and data transformation

Module #4
Data Visualization Basics
Introduction to data visualization, types of plots, and best practices

Module #5
Descriptive Statistics
Measures of central tendency, variability, and data distribution

Module #6
Data Summarization and Aggregation
Data aggregation, grouping, and summarization techniques

Module #7
Data Visualization for Univariate Analysis
Visualizing single variables using histograms, box plots, and more

Module #8
Data Visualization for Bivariate Analysis
Visualizing relationships between two variables using scatter plots and more

Module #9
Introduction to Inferential Statistics
Basics of inferential statistics, sampling distributions, and confidence intervals

Module #10
Hypothesis Testing
Formulating hypotheses, types of tests, and test assumptions

Module #11
Confidence Intervals and Estimation
Constructing confidence intervals and estimating population parameters

Module #12
ANOVA and Regression Analysis
Analysis of variance, simple and multiple regression, and model building

Module #13
Time Series Analysis
Introduction to time series analysis, components, and model building

Module #14
Forecasting Methods
Exponential smoothing, ARIMA, and other forecasting techniques

Module #15
Data Mining and Machine Learning
Overview of data mining, machine learning, and supervised learning

Module #16
Supervised Learning Algorithms
Decision trees, random forests, and other supervised learning algorithms

Module #17
Unsupervised Learning Algorithms
Clustering, k-means, and hierarchical clustering

Module #18
Text Analytics and Natural Language Processing
Introduction to text analytics, NLP, and sentiment analysis

Module #19
Data Analysis with Python
Using Python for data analysis, pandas, NumPy, and Matplotlib

Module #20
Data Analysis with R
Using R for data analysis, data manipulation, and visualization

Module #21
Data Analysis with Excel
Using Excel for data analysis, pivot tables, and charting

Module #22
Big Data Analytics
Introduction to big data, Hadoop, and Spark

Module #23
Data Storytelling and Communication
Effectively communicating insights and results to stakeholders

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Data Analysis career

Ready to Learn, Share, and Compete?