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Data Analytics Curriculum


Module 1: Introduction to Data Analytics

  • Understanding the role of data analytics in decision-making
  • Differentiating between data analytics, data science, and business intelligence
  • Overview of the data analytics process and its components
  • Introduction to key tools and technologies in data analytics

Module 2: Data Collection and Preprocessing

  • Types of data (structured, unstructured, semi-structured)
  • Data sources and acquisition methods
  • Data cleaning and data quality assessment
  • Handling missing values and outliers
  • Data transformation and feature engineering

Module 3: Exploratory Data Analysis (EDA)

  • Importance of EDA in understanding data
  • Summary statistics and data visualization
  • Univariate, bivariate, and multivariate analysis
  • Identifying patterns, trends, and correlations in data
  • Data visualization using libraries like Matplotlib and Seaborn

Module 4: Descriptive and Inferential Statistics

  • Measures of central tendency and dispersion
  • Probability distributions (normal, binomial, etc.)
  • Hypothesis testing and p-values
  • Confidence intervals and effect size
  • Understanding correlation and causation

Module 5: Data Visualization and Interpretation

  • Principles of effective data visualization
  • Choosing the right visualization for different types of data
  • Creating interactive visualizations using tools like Plotly
  • Telling a story with data through visualization
  • Dashboard creation using tools like Tableau or Power BI

Module 6: Data Mining and Machine Learning Basics

  • Introduction to machine learning and its applications in data analytics
  • Types of machine learning algorithms (supervised, unsupervised, etc.)
  • Feature selection and model evaluation techniques
  • Hands-on practice with simple machine learning algorithms (e.g., linear regression, k-means clustering)

Module 7: Time Series Analysis

  • Understanding time series data
  • Time series components: trend, seasonality, noise
  • Time series visualization and decomposition
  • Forecasting techniques (moving average, exponential smoothing, ARIMA)
  • Building time series models using Python libraries like statsmodels

Module 8: Text Analytics and Sentiment Analysis

  • Introduction to natural language processing (NLP)
  • Text preprocessing: tokenization, stemming, lemmatization
  • Sentiment analysis techniques using Python and libraries like NLTK or spaCy
  • Extracting insights from textual data

Module 9: Big Data and Data Warehousing

  • Introduction to big data concepts and challenges
  • Overview of data warehousing and data lakes
  • Using SQL for querying and managing large datasets
  • Introduction to distributed computing frameworks like Hadoop and Spark

Module 10: Case Studies and Real-World Projects

  • Working on real-world data analytics projects
  • Applying learned concepts to solve practical problems
  • Collaborative analysis and teamwork
  • Presentation and communication of results