Data Cleaning: Identifying and correcting errors, removing duplicates, and dealing with missing values to ensure data quality.
Data Transformation: Converting data into a suitable format for analysis, such as normalization and feature scaling.

Data Visualization:Creating charts, graphs, and plots to visually explore the data and identify patterns, trends, and outliers.
Descriptive Statistics:Summarizing and describing data using measures like mean, median, and standard deviation.

Inferential Statistics: Making inferences and predictions about populations based on sample data.
Hypothesis Testing: Evaluating hypotheses and determining the statistical significance of relationships

Supervised Learning: Training models with labeled data to make predictions on new, unseen data.
Learning: Finding patterns and structure in unlabeled data without explicit guidance.
Learning: Utilizing neural networks for complex tasks like image and speech recognition.

Model Building: Developing and training predictive models using machine learning algorithms.
Model Evaluation: Assessing model performance and generalization using metrics like accuracy, precision, and recall.

Hadoop:Distributed storage and processing framework for handling massive datasets.
Spark: In-memory data processing engine for fast and scalable data analysis.

Dashboards: Creating interactive visualizations and dashboards for presenting insights to stakeholders.
Reporting Tools: Generating automated reports to communicate findings effectively.

Analyzing and processing human language data, enabling tasks like sentiment analysis and language translation.

Cloud Platforms: Utilizing cloud services (e.g., AWS, Azure, GCP) for scalable and cost-effective data storage and computation.