Chartered Data Analyst Training is a comprehensive program for professionals to gain the knowledge and skills necessary to become a successful data analyst. The course covers topics such as data mining, data analysis, data visualization, and predictive analytics, as well as practical skills such as writing effective queries and building data-driven models.
The course is divided into 12 modules.
Comprehensive curriculum covering data collection, analysis techniques, data visualization, ethical data management, and a global perspective on data analysis in diverse industries and markets.
Module 1: Introduction to Data Analysis
This module will provide an overview of what data analysis is, how it works, and the different types of analysis available. It will cover topics such as data types, data sources, data mining, and data visualization.
Module 2: Data Cleaning and Preparation
This module will cover the fundamentals of data cleaning and preparation, such as data wrangling, data sampling, and data pre-processing.
Module 3: Data Warehousing and Business Intelligence
This module will cover topics such as data warehousing, ETL, OLAP, data mart, and data cube. It will also cover business intelligence tools and techniques, such as decision trees, neural networks, and predictive analytics.
Module 4: Statistics and Probability
This module will cover the basics of statistics and probability, such as descriptive statistics, inferential statistics, hypothesis testing, and regression analysis. Target audience:
Module 5: Machine Learning
This module will cover topics such as supervised learning, unsupervised learning, reinforcement learning, and deep learning. It will also cover the different types of machine learning algorithms and techniques.
Module 6: Data Visualization
This module will cover the fundamentals of data visualization, such as creating charts and graphs, using different chart types, and applying design principles to make data easier to understand.
Module 7: Natural Language Processing
This module will cover the fundamentals of natural language processing, including text analysis, sentiment analysis, and topic modeling.
Module 8: Big Data
This module will cover the fundamentals of big data, including distributed computing, data lakes, and streaming analytics. It will also cover big data tools and techniques, such as Hadoop and Spark.
Module 9: Data Governance and Data Security
This module will cover the fundamentals of data governance and data security, such as data privacy, data protection, and data compliance.
Module 10: Data Mining
This module will cover the fundamentals of data mining, such as data exploration, data mining algorithms, and data mining techniques.
Module 11: Artificial Intelligence
This module will cover the fundamentals of artificial intelligence, such as machine learning, deep learning, and natural language processing.
Module 12: Data Science
This module will cover topics such as data exploration, data wrangling, data visualization, and predictive analytics. It will also cover the different types of data science tools and techniques.
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