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Data Analysis for AI Engineering 🧠

Here is a complete roadmap to handle all the data Analysis task in the AI Engineering.

  1. Types of the data analysis:

    • Descriptive: Descriptive Analytics is one of the fundamental types of Data Analytics that provides insight into the past. As a Data Analyst, utilizing Descriptive Analytics involves the technique of using historical data to understand changes that have occurred in a business over time. Primarily concerned with the “what has happened” aspect, it analyzes raw data from the past to draw inferences and identify patterns and trends. This helps companies understand their strengths, weaknesses and pinpoint operational problems, setting the stage for accurate Business Intelligence and decision-making processes.
    • Diagnostic: Diagnostic analytics, as a crucial type of data analytics, is focused on studying past performance to understand why something happened. This is an integral part of the work done by data analysts. Through techniques such as drill-down, data discovery, correlations, and cause-effect analysis, data analysts utilizing diagnostic analytics can look beyond general trends and identify the root cause of changes observed in the data. Consequently, this enables businesses to address operational and strategic issues effectively, by allowing them to grasp the reasons behind such issues. For every data analyst, the skill of performing diagnostic data analytics is a must-have asset that enhances their analysis capability.
    • Predictive: Predictive analysis is a crucial type of data analytics that any competent data analyst should comprehend. It refers to the practice of extracting information from existing data sets in order to determine patterns and forecast future outcomes and trends. Data analysts apply statistical algorithms, machine learning techniques, and artificial intelligence to the data to anticipate future results. Predictive analysis enables organizations to be proactive, forward-thinking, and strategic by providing them valuable insights on future occurrences. It’s a powerful tool that gives companies a significant competitive edge by enabling risk management, opportunity identification, and strategic decision-making.
    • Prescriptive: Prescriptive analytics, a crucial type of data analytics, is essential for making data-driven decisions in business and organizational contexts. As a data analyst, the goal of prescriptive analytics is to recommend various actions using predictions on the basis of known parameters to help decision makers understand likely outcomes. Prescriptive analytics employs a blend of techniques and tools such as algorithms, machine learning, computational modelling procedures, and decision-tree structures to enable automated decision making. Therefore, prescriptive analytics not only anticipates what will happen and when it will happen, but also explains why it will happen, contributing to the significance of a data analyst’s role in an organization.

  2. Key concepts for data analysis:

    • Collection
    • Cleaning
    • Exploration
    • Visulization: See Magic tableau
    • Statistical analysis: simplilearn
    • Machine Learning: IBM

  3. Python:

Data Analysis part is completed.