In this twenty-one-week course, students will learn the fundamental concepts and techniques for data analytics using the Python programming language. Students will be introduced to powerful libraries and tools available in Python for data analysis, including NumPy, Pandas, and Matplotlib. Students will gain hands-on experience with data cleaning, transformation, exploration, visualization, and basic statistical analysis.
The Data Analytics Pathway will cover the following topics:
Introduction to Python for Data Analysis
- Python basics for data analysis
- Overview of Python Libraries for data analysis
- Installing and setting up Anaconda and Jupyter Notebook
Data Cleaning and Transformation
- Handling missing data
- Dealing with outliers
- Data manipulation with Pandas
Data Exploration and Visualization
- Data visualization with Matplotlib, Seaborn & Tableau
- Exploratory data analysis
- Data summarization and aggregation
Basic Statistical Analysis
- Probability and distributions
- Hypothesis testing
- Regression analysis
Projects Suited for Data Analytics with Python
Time Series Analysis:
Dive into time series analysis, exploring patterns and trends in sequential data. Python’s libraries like Pandas and Statsmodels are essential for this project.
Exploratory Data Analysis (EDA):
Conduct EDA on datasets, examining patterns, trends, and relationships within the data. Python’s Pandas and Matplotlib can be utilized for this project.
Data Dashboard:
Create interactive data dashboards using tools like Plotly or Dash, allowing users to dynamically explore and interact with visualized data.
Data Cleaning Automation:
Develop scripts or programs to automate the data cleaning process, enhancing efficiency and ensuring consistency in data quality.
Interactive Data Reports:
Create interactive data reports using tools like Jupyter Notebooks or Streamlit, allowing stakeholders to explore and understand the analytical results interactively.
