Data Analysis & Visualisation with Python
Comprehensive course is designed to help learners unlock the full potential of Python for
data analysis, visualization, and storytelling.
Overview
Participants will gain practical skills in manipulating, cleaning, and analyzing datasets, while building
engaging visualizations that deliver
actionable insights.
Through hands-on modules, guided exercises, and real-world case studies, learners will master
industry-standard libraries such as NumPy, pandas, Matplotlib, Seaborn, Plotly, and Bokeh.
The course emphasizes both technical foundations and practical application, preparing you to tackle
data-driven challenges in business, research, and technology.
- Data manipulation with NumPy and pandas
- Data cleaning and preprocessing techniques
- Exploratory Data Analysis (EDA)
- Static and interactive visualizations (Matplotlib, Seaborn, Plotly, Bokeh)
- Time series analysis and advanced aggregations
- Dashboard creation and effective data storytelling
Learners will also complete an end-to-end data project that extracts, transforms, and loads data
from multiple sources, automated for scheduled execution.
Total Duration: 120 hours over 10 weeks
- 70 hours of instructor-led sessions
- 20 hours of tests and assignments
- 30 hours of project work
Learning Outcomes
By the end of this program, participants will be able to:
- Analyze structured datasets using NumPy and pandas
- Clean, preprocess, and transform raw data for insights
- Perform exploratory data analysis to detect patterns, trends, and anomalies
- Create compelling static and interactive visualizations for reports and dashboards
- Use Matplotlib & Seaborn for statistical plots and multi-layout charts
- Design interactive dashboards with Plotly and Bokeh
- Apply time series techniques and advanced aggregations
- Work with real-world datasets across multiple domains
- Deliver a complete analysis project with clear visual storytelling
Course Modules
1. Introduction to Data Analysis
Duration: 05 Hours
- What is data analysis?
- Role of Python in analytics
- Overview of core tools (pandas, NumPy, Matplotlib, Seaborn, Plotly)
- Setting up Jupyter Notebook / JupyterLab
2. Python & NumPy Foundations
Duration: 05 Hours
- NumPy arrays and operations
- Vectorization and broadcasting
- Indexing, slicing, reshaping data
- Statistical & mathematical functions
3. Working with Pandas
Duration: 10 Hours
- Series and DataFrame essentials
- Importing/exporting data (CSV, Excel, JSON, SQL)
- Data selection, filtering, sorting
- Aggregations, groupby, pivot tables
4. Data Cleaning & Preprocessing
Duration: 10 Hours
- Handling missing values & duplicates
- String manipulation & categorical encoding
- Date/time operations
- Data normalization & scaling
5. Exploratory Data Analysis (EDA)
Duration: 10 Hours
- Descriptive statistics & summary functions
- Correlation & covariance analysis
- Outlier detection methods
- Automated profiling with pandas-profiling
6. Data Visualisation with Matplotlib & Seaborn
Duration: 10 Hours
- Line, bar, scatter, histogram plots
- Customizing plots (titles, labels, legends, colors)
- Subplots & multi-chart layouts
- Seaborn statistical plots (boxplots, heatmaps, pairplots)
7. Interactive Visualisation
Duration: 10 Hours
- Getting started with Plotly and Bokeh
- Interactive charts & dashboards
- Exporting plots for web and reports
8. Advanced Analysis Techniques
Duration: 05 Hours
- Time series basics (resampling, rolling averages)
- Cross-tabulations & advanced aggregations
- Merging, joining, concatenating datasets
9. Working with Real-World Datasets
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Duration: 05 Hours
- Case study: Sales & marketing dataset
- Case study: Financial data analysis
- Case study: Social media analytics
Weekly Online Tests & Assignments
- Weekly online tests to reinforce concepts
- 20 questions | 45-minute duration
- Programming assignment due every Weekend
Final Project (30 Hours)
- Select a dataset (publicly available or provided)
- Perform complete EDA workflow
- Build meaningful visualizations
- Prepare an insights-driven report or dashboard
- Present project findings for peer and instructor feedback
- Evaluation based on clarity, insightfulness, and visual appeal
Course Delivery and Fee Structure
1 Person
Mode: Online - Google Classroom
Fee: Rs. 15,000
Lecture Hours/Week: 8 Hrs (4 Days/Week)
Duration: 10 Weeks
Group of 3
Mode: Online - Google Classroom
Fee: Rs. 14,000
Lecture Hours/Week: 8 Hrs (4 Days/Week)
Duration: 10 Weeks
Group of 5
Mode: Online - Google Classroom
Fee: Rs. 12,000
Lecture Hours/Week: 8 Hrs (4 Days/Week)
Duration: 10 Weeks
Explore. Enroll. Elevate.
Please review our course offerings and contact us to schedule your first class.
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