Data Analytics

Data collection and cleaning: Techniques for gathering and preparing data for analysis.
Exploratory data analysis: Techniques for summarizing and visualizing data to identify patterns and trends.
Statistical analysis: Techniques for drawing inferences from data, such as hypothesis testing and regression analysis.
Machine learning: Techniques for building predictive models using data, such as supervised and unsupervised learning.
Data visualization: Techniques for creating effective visualizations to communicate insights from data.
Big data and distributed computing: Techniques for working with large data sets, such as Hadoop and Spark.
Tableau, R, Python: Software tools used for data analysis and visualization.
Business use cases: Examples and case studies of how data analytics is used in various industries and organizations.
Data governance and management: Techniques for managing and protecting data assets, such as data quality, data security, and data lineage.
Data warehousing and ETL: Techniques for designing, building, and maintaining data warehouses, as well as the process of extracting, transforming and loading data.
Data mining: Techniques for discovering patterns and knowledge from large data sets, such as association rule mining, clustering, and anomaly detection.
Advanced statistical methods: Techniques for analyzing data such as survival analysis, time series analysis, and multivariate analysis.
Text mining and natural language processing: Techniques for working with unstructured data, such as text, speech, and images.
Streaming data and real-time analytics: Techniques for processing and analyzing data in real-time, such as Apache Kafka, Apache Storm, and Apache Flink.
Cloud computing and data analytics: Techniques for using cloud platforms, such as AWS, Azure, and GCP, to store, process and analyze data.
Ethics and privacy: Understanding the ethical and legal implications of working with data, such as data privacy, bias, and fairness.