Data Analytics is a critical subject for Computer Science students that involves exploring and analyzing data to find meaningful patterns, relationships, and insights. In this section, we cover key concepts, tools, techniques, and algorithms used in Data Analytics.
Start ExploringIn Data Analytics, some of the most important concepts include data preprocessing, exploratory data analysis, machine learning models, time series analysis, and big data technologies.
Data preprocessing is a crucial step that involves cleaning and transforming raw data into a usable format for analysis. This includes handling missing values, outliers, and applying transformations such as normalization or standardization.
Learn MoreEDA involves using statistical tools and visualizations to understand the structure, distribution, and relationships within the data. It helps uncover patterns and anomalies.
Learn MoreMachine learning models are used to make predictions or decisions based on data. These include supervised learning, unsupervised learning, and reinforcement learning.
Learn MoreTime series analysis is a technique for analyzing data that is collected over time. It is used for forecasting and understanding trends, seasonality, and cycles in the data.
Learn MoreBig data technologies such as Hadoop, Spark, and NoSQL databases are designed to process and analyze large-scale data that cannot be handled by traditional methods.
Learn MoreClick below to download the previous year's exam papers for **Data Analytics**. These papers will help you prepare for exams and understand the typical format of questions.
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Access essential reference materials and notes to understand key topics in **Data Analytics**, such as data preprocessing, machine learning models, and big data technologies.
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