Data Science Training Roadmap – Pandas (12 Weeks)
Week 1: Introduction to Data Science and Python for Data Analysis
In Week 1, you will be introduced to the fundamentals of data science, including its applications, workflow, and key concepts such as data, variables, datasets, and types of analysis. You will also set up your Python environment and learn the basics of Python programming. On Friday, the assignment will include simple exercises on Python data structures like lists, dictionaries, and loops.
Week 2: Introduction to Pandas
Week 2 focuses on Pandas, the core library for data manipulation in Python. You will learn about Series and DataFrames, loading datasets from CSV, Excel, and other sources, and inspecting your data using commands like .head(), .tail(), .shape(), and .info(). Friday’s assignment will require loading a dataset and summarizing its structure and content.
Week 3: Data Selection and Indexing
In Week 3, you will learn to select, filter, and index data in Pandas. Topics include selecting columns and rows, boolean indexing, .loc and .iloc, and conditional filtering. The Friday assignment will involve extracting specific data from a dataset based on conditions.
Week 4: Data Cleaning and Handling Missing Values
Week 4 introduces data cleaning techniques, including handling missing values with .fillna(), .dropna(), and replacing incorrect values. You will also learn to remove duplicates and standardize data formats. Friday’s assignment will involve cleaning a messy dataset and preparing it for analysis.
Week 5: Data Transformation
During Week 5, you will learn to manipulate data using operations like sorting, grouping, aggregating, merging, concatenating, and pivot tables. These transformations allow you to summarize and restructure data efficiently. The Friday assignment will involve grouping and summarizing a dataset to extract insights.
Week 6: Exploratory Data Analysis (EDA) with Pandas
Week 6 focuses on performing exploratory data analysis. You will learn to compute summary statistics, identify trends, and visualize data using Pandas and Matplotlib/Seaborn. The Friday assignment will involve analyzing a dataset to identify patterns and outliers.
Week 7: Working with Dates and Time Series Data
In Week 7, you will learn to handle datetime objects, convert string dates, resample data, and perform time-based operations. The Friday assignment will involve analyzing a time-series dataset to extract trends or seasonality.
Week 8: Data Visualization with Pandas
Week 8 emphasizes visualizing data using Pandas built-in plotting functions and integrating with Matplotlib/Seaborn. You will learn to create line plots, bar charts, histograms, scatter plots, and boxplots. The Friday assignment will involve visualizing key features of a dataset to uncover insights.
Week 9: Data Aggregation and Pivot Tables
During Week 9, you will dive deeper into grouping and aggregation functions, creating pivot tables, and summarizing multi-dimensional data. Friday’s assignment will involve creating pivot tables and performing advanced aggregations on a dataset.
Week 10: Data Merging and Joining
Week 10 teaches combining multiple datasets using merge, join, and concatenation operations in Pandas. You will learn inner, outer, left, and right joins to consolidate information from different sources. The Friday assignment will involve merging two datasets and extracting meaningful insights.
Week 11: Advanced Pandas Techniques
In Week 11, you will learn advanced techniques such as applying custom functions with .apply(), using .map(), .replace(), .cut(), and working with categorical data. Friday’s assignment will involve applying transformations and feature engineering on a dataset.
Week 12: Real-World Project and Integration
The final week is dedicated to a real-world data science project, integrating all Pandas skills learned. You might analyze sales data, customer behavior, or stock market trends. Friday will serve as a final assessment, where you submit cleaned data, visualizations, and analysis results along with a short report.
Daily Class Structure
Each class day (Monday–Thursday) includes one hour of theory, one hour of demonstration using Python and Pandas, and one hour of hands-on practice. Friday is dedicated to testing and assignments to reinforce the week’s learning.
This roadmap ensures that by the end of 12 weeks, you will be proficient in data cleaning, analysis, visualization, and real-world problem-solving using Pandas.
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