M1.1 Understanding the role of a Data Analyst
M1.2 Key concepts in data analysis
M1.3 Data analysis process and workflow
M1.4 Ethical considerations in data analysis

M2.1 Types of data: structured and unstructured
M2.2 Data collection methods and sources
M2.3 Data quality assessment and cleaning techniques
M2.4 Dealing with missing and inconsistent data

M3.1 Exploratory data analysis (EDA)
M3.2 Summary statistics and data distributions
M3.3 Data visualization principles and best practices
M3.4 Using visualization tools and libraries (e.g., Matplotlib, Power BI, Tableau)

M4.1 Descriptive and inferential statistics
M4.2 Hypothesis testing and confidence intervals
M4.3 Correlation and regression analysis
M4.4 Probability distributions and sampling techniques

M5.1 Data transformation and feature engineering
M5.2 Working with categorical and numerical data
M5.3 Reshaping and pivoting data
M5.4 Introduction to SQL for data manipulation

M6.1 Introduction to machine learning concepts
M6.2 Supervised vs. unsupervised learning
M6.3 Model training, validation, and testing
M6.4 Overfitting, underfitting, and model evaluation metrics

M7.1 Linear and logistic regression
M7.2 Decision trees and random forests
M7.3 Support vector machines
M7.4 Model selection and hyperparameter tuning

M8.1 K-means clustering · Hierarchical clustering
M8.2 Principal Component Analysis (PCA)
M8.3 t-SNE (t-Distributed Stochastic Neighbor Embedding)

M9.1 Time series data characteristics
M9.2Time series decomposition
M9.3Forecasting techniques (e.g., ARIMA, exponential smoothing)
M9.4Anomaly detection in time series data

M10.1 Introduction to big data technologies
M10.2Distributed computing frameworks (e.g., Hadoop, Spark)
M10.3Cloud computing platforms and services (e.g., AWS, Azure, GCP)
M10.4Scalable data processing and storage solutions

M11.1 Effective communication of analytical findings
M11.2Data storytelling and visualization
M11.3Creating clear and compelling reports
M11.4Presenting technical information to non-technical stakeholders

M12.1 Real-world data analysis project
M12.2Applying concepts and techniques learned throughout the program
M12.4Data collection, cleaning, analysis, and presentation