Data Analytics - Notes, Syllabus & Materials | YNetStudyHub
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Data Analytics

Comprehensive curriculum, topic notes, practice questions, and past examination papers for Data Analytics on YNetStudyHub.

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Topics & Syllabus 40

#1 Introduction to Data Analytics
An overview of data analytics, including its definition, importance, and applications...
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#2 Data Collection and Cleaning
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#3 Data Visualization Techniques
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#4 Descriptive Analytics
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#5 Predictive Analytics
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#6 Prescriptive Analytics
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#7 Machine Learning Algorithms
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#8 Big Data Analytics
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#9 Data Ethics and Privacy
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#10 Real-world Case Studies
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#11 Introduction to Data Analytics
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#12 Data Collection and Preparation
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#13 Exploratory Data Analysis (EDA)
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#14 Statistical Analysis for Data Analytics
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#15 Machine Learning Fundamentals
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#16 Predictive Analytics
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#17 Text Mining and Sentiment Analysis
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#18 Time Series Analysis
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#19 Data Visualization
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#20 Big Data Analytics
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#21 Introduction to Data Analytics
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#22 Data Collection and Preprocessing
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#23 Data Visualization
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#24 Descriptive Analytics
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#25 Inferential Statistics
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#26 Predictive Analytics
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#27 Machine Learning Fundamentals
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#28 Text Analytics
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#29 Big Data Analytics
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#30 Ethical Considerations in Data Analytics
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#31 Introduction to Data Analytics
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#32 Data Collection and Cleaning
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#33 Data Visualization
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#34 Descriptive Analytics
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#35 Predictive Analytics
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#36 Prescriptive Analytics
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#37 Data Mining
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#38 Text Analytics
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#39 Ethical and Privacy Considerations in Data Analytics
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#40 Case Studies in Data Analytics
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Unit Outline 60h

Learning Objectives

5 objectives
  • Understand the fundamental concepts, importance, and applications of data analytics across various industries.
  • Develop skills in data collection, cleaning, preprocessing, and visualization techniques for effective data analysis.
  • Apply statistical, predictive, prescriptive, and machine learning methods to analyze and interpret data.
  • Explore advanced topics including big data analytics, text mining, time series analysis, and data ethics.
  • Analyze real-world case studies to solve practical problems using data analytics tools and methodologies.

Content Outline

Preview

Unit 775: Comprehensive Data Analytics

1. Introduction to Data Analytics

  • Definition and scope of data analytics
  • Importance in decision-making processes
  • Applications across industries (healthcare, finance, marketing, etc.)
  • Types of data analytics: Descriptive, Inferential, Predictive, Prescriptive

2. Data Collection and Preparation

2.1 Data Collection Methods

  • Sources of data: databases, web, sensors, social media, surveys
  • Data acquisition techniques

2.2 Data Cleaning and Preprocessing

  • Handling missing values and outliers
  • Data transformation and normalization
  • Ensuring data quality and consistency

3. Exploratory Data Analysis (EDA)

  • Techniques for summarizing and visualizing data
  • Identifying patterns, trends, and outliers
  • Use of statistical measures: mean, median, mode, variance, standard deviation
  • Tools for EDA (e.g., pandas, matplotlib, seaborn)

4. Data Visualization Techniques

  • Principles of effective visualization
  • Types of visualizations: bar charts, histograms, scatter plots, heatmaps, dashboards
  • Visualization tools: Tableau, Power BI, matplotlib, ggplot2
  • Storytelling with data

5. Descriptive and Inferential Analytics

5.1 Descriptive Analytics

  • Summarizing data characteristics
  • Measures of central tendency and dispersion

5.2 Inferential Statistics

  • Hypothesis testing
  • Confidence intervals
  • Correlation and regression analysis
  • Significance testing

6. Predictive Analytics

  • Introduction to predictive modeling
  • Regression analysis and time series forecasting
  • Building and evaluating predictive models
  • Applying predictions to real-world scenarios

7. Prescriptive Analytics

  • Definition and role in decision-making
  • Optimization techniques
  • Recommender systems and scenario analysis

8. Machine Learning Fundamentals

8.1 Overview of Machine Learning

  • Supervised vs unsupervised learning
  • Common algorithms: regression, classification, clustering

8.2 Model Evaluation Techniques

  • Cross-validation
  • Performance metrics: accuracy, precision, recall, F1-score

9. Advanced Machine Learning Techniques

  • Text mining and sentiment analysis
    • Natural Language Processing (NLP) basics
    • Sentiment extraction from social media and reviews
  • Data mining techniques
    • Association analysis, clustering, classification algorithms

10. Time Series Analysis

  • Characteristics of time-dependent data
  • Decomposition of time series: trend, seasonality, noise
  • Forecasting methods and applications

11. Big Data Analytics

  • Introduction to big data concepts
  • Technologies and tools: Hadoop, Spark
  • Challenges in handling large datasets
  • Distributed computing frameworks

12. Data Ethics and Privacy

  • Ethical considerations in data collection and analysis
  • Data privacy laws and regulations (e.g., GDPR)
  • Bias, fairness, and responsible data use
  • Security practices and data governance

13. Real-world Case Studies

  • Industry-specific examples (finance, healthcare, marketing, etc.)
  • Problem-solving using data analytics techniques
  • Lessons learned and best practices
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Parent Course
Bachelor of Science in Information Systems

This unit is an integral part of the Bachelor of Science in Information Systems syllabus.

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