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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.