Introduction to Data Science - For Beginners (2024)

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[img]https://i.postimg.cc/vZNh1zyZ/e64jlngvg2gezhtykox9.jpg[/img]
Published 1/2024
Created by Xavier Chelladurai
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 17 Lectures ( 4h 36m ) | Size: 4.77 GB





[i]Understanding the technologies that define the future[/i]

[b][i]What you'll learn:[/i][/b]
Learn the importance of Data and how it is evolving into the important area of decision making
Understand the foundations of Data Science, Roles and resonsibilities of various roles in the field of Data Science
Understand the Data Science Project Cycle
Understand the principles of Data Preprocessing, Data Analytics, Data Visualization and Data Normalization
[b][i]Requirements:[/i][/b]
Understanding of Basic Mathematical Concepts
Simple Python Programming
[b][i]Description:[/i][/b]
Introduction to Data Science:Data Science is a multidisciplinary field that combines statistical analysis, machine learning, and domain expertise to extract valuable insights and knowledge from data. It encompasses a wide range of techniques and tools to uncover hidden patterns, make predictions, and drive informed decision-making. The field has gained immense importance in the era of big data, where vast amounts of information are generated daily, creating opportunities to derive meaningful conclusions.Data Science Processes:The Data Science process typically involves several stages, starting with data collection and preparation, followed by exploration and analysis, and concluding with interpretation and communication of results. These stages form a cyclical and iterative process, as insights gained may lead to further refinement of hypotheses or data collection strategies. Rigorous methodologies such as CRISP-DM (Cross-Industry Standard Process for Data Mining) guide practitioners through these stages, ensuring a systematic and effective approach.Preprocessing:Data preprocessing is a crucial step in the Data Science pipeline, involving cleaning and transforming raw data into a suitable format for analysis. This phase addresses issues like missing values, outliers, and irrelevant information, ensuring the quality and integrity of the dataset. Techniques such as normalization and feature scaling may also be applied to enhance the performance of machine learning algorithms and improve the accuracy of predictions.Visualization:Data visualization plays a key role in Data Science by providing a means to represent complex information in a visually accessible format. Graphs, charts, and dashboards aid in understanding patterns, trends, and relationships within the data. Visualization not only facilitates exploration and interpretation but also serves as a powerful tool for communicating findings to non-technical stakeholders.Analytics:Analytics in Data Science involves the application of statistical and mathematical techniques to extract meaningful insights from data. Descriptive analytics summarizes historical data, diagnostic analytics identifies the cause of events, predictive analytics forecasts future outcomes, and prescriptive analytics suggests actions to optimize results. These analytical approaches empower organizations to make data-driven decisions, optimize processes, and gain a competitive edge in today's data-driven world.
[b][i]Who this course is for:[/i][/b]
Beginners in the field of Computer Science, Data Science and Artificial Intelligence
Software Engineers
Homepage

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