Course Overview – Data Science & Analytics – Practical
The Data Science & Analytics – Practical course is a structured 6-week hands-on training program designed to help students and professionals build strong foundations in data handling, analysis, and visualization using industry-relevant tools.
With practical exposure to Python, Pandas, Matplotlib, Seaborn, and Jupyter Notebook, this course focuses on transforming raw data into meaningful insights and visual reports that support data-driven decision making.
This program is ideal for beginners entering the analytics domain as well as professionals seeking to enhance their analytical and reporting capabilities with practical, job-oriented skills.
What Is This Course All About?
This course trains you to work with structured datasets, clean raw information, perform analytical operations, and present results in a clear, visual format suitable for business environments.
You’ll learn to:
- Understand different types of datasets and data structures
- Clean and preprocess raw data effectively
- Use Pandas for structured data manipulation and analysis
- Create professional charts using Matplotlib and Seaborn
- Interpret data patterns and generate actionable insights
"Strong data skills are no longer optional. This course helps you convert raw numbers into insights that businesses can act upon."
Tools & Technologies Covered
| Tool |
Purpose |
| Python |
Core programming language for data analysis |
| Pandas |
Data manipulation and structured analysis |
| Matplotlib |
Chart creation and visual reporting |
| Seaborn |
Statistical data visualization |
| Jupyter Notebook |
Interactive analysis and reporting environment |
Real-World Applications
- Sales and revenue analysis
- Customer behavior analytics
- Business performance reporting
- Operational dashboards
- Market trend analysis
Outcome: By the end of the program, learners will confidently clean, analyze, visualize, and interpret real-world datasets using Python-based analytics tools.
Why Choose Data Science & Analytics – Practical?
The Data Science & Analytics – Practical course prepares you for one of the
fastest-growing career domains in India. Organizations today rely heavily on data-driven decisions,
and professionals who can analyze and visualize data effectively are in high demand across industries.
Data is the new business currency — and analytics professionals are the decision-makers behind it.
Career Map – Your Analytics Journey
Beginner Level
Basic Computer Knowledge
Data Science & Analytics – Practical (6 Weeks)
Analytics roles exist in every industry — IT, fintech, healthcare, e-commerce, retail, and startups.
Skill Wheel – What You Master
| Skill |
Application |
| Python for Analytics |
Data manipulation & scripting |
| Data Cleaning |
Handling missing & inconsistent data |
| Data Analysis |
Trend identification & insights |
| Data Visualization |
Charts, reports & dashboards |
| Data Interpretation |
Business decision support |
Placement Assistance & Career Support
- Resume building for Data Analyst & BI roles
- Portfolio development guidance (Mini Project)
- Interview preparation sessions
- LinkedIn profile optimization
- Job referral assistance where applicable
Students are guided to apply for entry-level analytics and reporting roles across startups, IT services, and corporate analytics teams.
India-Focused Salary Insights
| Experience Level |
Role Example |
Average Salary (INR) |
| Fresher (0–1 Year) |
Junior Data Analyst |
₹3 – 6 LPA |
| 1–3 Years |
Data Analyst / BI Executive |
₹6 – 10 LPA |
| 3–5 Years |
Senior Data Analyst |
₹10 – 18 LPA |
| 5+ Years |
Analytics Manager / Lead |
₹18 – 30+ LPA |
Analytics professionals experience strong salary growth as they combine technical skills with business understanding.
Course Curriculum: Data Science & Analytics – Practical
The Data Science & Analytics – Practical program is a structured 6-week hands-on training course designed to help learners master data handling, analysis, and visualization using Python-based tools.
This curriculum emphasizes practical implementation, real datasets, and business-focused analytics to ensure learners gain job-ready skills.
Week-Wise Curriculum Breakdown
Week 1 – Data Fundamentals & Dataset Understanding
Build a strong foundation in data types, structures, and dataset concepts.
| Topic |
Focus Area |
| Introduction to Data Science |
Role of analytics in business |
| Types of Data |
Structured vs unstructured data |
| Understanding Datasets |
CSV, Excel, JSON basics |
| Python Refresher |
Data types, loops, functions |
Week 2 – Data Cleaning & Preparation Techniques
Learn how to clean messy data and prepare it for analysis.
| Topic |
Focus Area |
| Handling Missing Values |
Fill, drop, replace strategies |
| Removing Duplicates |
Ensuring data consistency |
| Data Formatting |
Type conversion & normalization |
| Feature Preparation |
Creating usable analytical columns |
Week 3 – Data Analysis using Python (Pandas)
Perform structured data analysis using Pandas.
| Topic |
Focus Area |
| DataFrames & Series |
Core Pandas structures |
| Filtering & Sorting |
Extracting relevant information |
| Grouping & Aggregation |
Summarizing data |
| Descriptive Statistics |
Mean, median, correlation |
Week 4 – Creating Charts & Visual Reports
Visualize data insights using professional charts and reports.
| Tool |
Application |
| Matplotlib |
Line charts, bar charts, histograms |
| Seaborn |
Statistical visualizations |
| Customization |
Titles, labels, styling |
| Report Preparation |
Creating insight-driven visuals |
Week 5 – Data Interpretation & Business Insights
Convert analysis into meaningful business conclusions.
| Topic |
Focus Area |
| Trend Analysis |
Identifying patterns |
| Correlation Analysis |
Understanding relationships |
| Insight Generation |
Drawing conclusions from data |
| Business Recommendations |
Decision-making support |
Week 6 – Mini Data Analytics Project
Apply all learning to a real-world dataset and create a portfolio-ready project.
| Project Stage |
Deliverable |
| Problem Statement |
Business case selection |
| Data Cleaning |
Prepared dataset |
| Analysis |
Key findings & trends |
| Visualization |
Professional charts |
| Final Report |
Insight-driven presentation |
Outcome: By the end of the course, learners will confidently clean datasets, perform analysis using Pandas, create compelling visualizations, and generate business insights using Python.