The learning paths

Data Science:

- Foundations of Data Science: Ensuring data quality by identifying and addressing errors, inconsistencies, missing values, and outliers, this first step involves understanding your data, cleaning data, standardization, and transformation.
- Understand Exploratory Data Analysis concepts.
- Learn about probability distributions and statistical inference - Applying statistical techniques such as hypothesis testing, regression analysis, correlation analysis, and time series analysis to uncover relationships, associations, and trends in the data.
- Data Modeling and Predictive Analytics: Building models to predict future outcomes or trends based on historical data. This may involve machine learning algorithms, regression models, decision trees, or other predictive modeling techniques.
- Explore supervised, unsupervised, and reinforcement learning algorithms
- Gain hands-on experience in model training and evaluation
- Learn about regression, classification, and clustering techniques
- Understand model evaluation and validation
- Data Visualization: Presenting data visually through charts, graphs, and interactive dashboards to communicate findings effectively and facilitate better understanding.
- Feature Engineering and Selection
- Explore techniques for feature extraction and transformation
- Understand the importance of feature selection in model building

Data Engineering:

- Introduction to Data Engineering
- Understand the role of data engineering in the data lifecycle
- Learn about data storage and retrieval systems
- Data Processing and Transformation
- Explore data cleaning and preprocessing techniques
- Gain proficiency in ETL (Extract, Transform, Load) processes
- Database Management
- Learn about relational and non-relational databases
- Gain hands-on experience in SQL and NoSQL database management
- Big Data Technologies
- Explore distributed computing frameworks such as Hadoop and Spark
- Understand data processing at scale

Data Analysis:

- Exploratory Data Analysis (EDA)
- Learn techniques for data visualization and exploration
- Understand descriptive statistics and data summarization
- Data Mining and Pattern Recognition
- Explore association rule mining and pattern recognition techniques
- Learn about anomaly detection and outlier analysis
- Statistical Analysis and Interpretation
- Understand inferential statistics and hypothesis testing
- Gain proficiency in statistical modeling and interpretation techniques
-Real-world Data Projects
- Work on hands-on projects involving real-world datasets
- Apply learned concepts in data analysis and interpretation to solve practical problems