**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.

## 0 Comments:

## Post a Comment