Wednesday 12 June 2024

Data Science Basics to Advance Course Syllabus

 


Week 1: Introduction to Data Science and Python Programming

  • Overview of Data Science
    • Understanding what data science is and its importance.
  • Python Basics
    • Introduction to Python, installation, setting up the development environment.
  • Basic Python Syntax
    • Variables, data types, operators, expressions.
  • Control Flow
    • Conditional statements, loops.
  • Functions and Modules
    • Defining, calling, and importing functions and modules.
  • Hands-on Exercises
    • Basic Python programs and assignments.

Week 2: Data Structures and File Handling in Python

  • Data Structures
    • Lists, tuples, dictionaries, sets.
  • Manipulating Data Structures
    • Indexing, slicing, operations.
  • File Handling
    • Reading from and writing to files, file operations.
  • Error Handling
    • Using try-except blocks.
  • Practice Problems
    • Mini-projects involving data structures and file handling.

Week 3: Data Wrangling with Pandas

  • Introduction to Pandas
    • Series and DataFrame objects.
  • Data Manipulation
    • Indexing, selecting data, filtering.
  • Data Cleaning
    • Handling missing values, data transformations.
  • Data Integration
    • Merging, joining, concatenating DataFrames.
  • Hands-on Exercises
    • Data wrangling with real datasets.

Week 4: Data Visualization

  • Introduction to Matplotlib
    • Basic plotting, customization.
  • Advanced Visualization with Seaborn
    • Statistical plots, customization.
  • Interactive Visualization with Plotly
    • Creating interactive plots.
  • Data Visualization Projects
    • Creating visualizations for real datasets.

Week 5: Exploratory Data Analysis (EDA) - Part 1

  • Importance of EDA
    • Understanding data and deriving insights.
  • Descriptive Statistics
    • Summary statistics, data distributions.
  • Visualization for EDA
    • Histograms, box plots.
  • Correlation Analysis
    • Finding relationships between variables.
  • Hands-on Projects
    • Conducting EDA on real-world datasets.

Week 6: Exploratory Data Analysis (EDA) - Part 2

  • Visualization for EDA
    • Scatter plots, pair plots.
  • Handling Missing Values and Outliers
    • Techniques for dealing with incomplete data.
  • Feature Engineering
    • Creating new features, transforming existing features.
  • Hands-on Projects
    • Advanced EDA techniques on real datasets.

Week 7: Data Collection and Preprocessing Techniques

  • Data Collection Methods
    • Surveys, web scraping, APIs.
  • Data Cleaning
    • Handling missing data, outliers, and inconsistencies.
  • Data Transformation
    • Normalization, standardization, encoding categorical variables.
  • Hands-on Projects
    • Collecting and preprocessing real-world data.

Week 8: Database Management and SQL

  • Introduction to Databases
    • Relational databases, database design.
  • SQL Basics
    • SELECT, INSERT, UPDATE, DELETE statements.
  • Advanced SQL
    • Joins, subqueries, window functions.
  • Connecting Python to Databases
    • Using libraries like SQLAlchemy.
  • Hands-on Exercises
    • SQL queries and database management projects.

Week 9: Introduction to Time Series Analysis

  • Time Series Concepts
    • Understanding time series data, components of time series.
  • Time Series Visualization
    • Plotting time series data, identifying patterns.
  • Basic Time Series Analysis
    • Moving averages, smoothing techniques.
  • Hands-on Exercises
    • Working with time series data.

Week 10: Advanced Time Series Analysis

  • Decomposition
    • Breaking down time series into trend, seasonality, and residuals.
  • Forecasting Methods
    • Introduction to ARIMA and other forecasting models.
  • Model Evaluation
    • Assessing forecast accuracy.
  • Practical Application
    • Time series forecasting projects.

Week 11: Advanced Data Wrangling with Pandas

  • Advanced Data Manipulation
    • Pivot tables, groupby operations.
  • Time Series Manipulation
    • Working with date and time data in Pandas.
  • Merging and Joining DataFrames
    • Advanced techniques for combining datasets.
  • Practical Exercises
    • Complex data wrangling tasks.

Week 12: Advanced Data Visualization Techniques

  • Interactive Dashboards
    • Creating dashboards with Dash and Tableau.
  • Geospatial Data Visualization
    • Mapping data with libraries like Folium.
  • Storytelling with Data
    • Effective communication of data insights.
  • Practical Projects
    • Building interactive and compelling data visualizations.

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