Data Science & Analytics – Practical | DS
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
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
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.
Career Map – Your Analytics Journey
Beginner Level
Basic Computer Knowledge
Data Science & Analytics – Practical (6 Weeks)
Data Analyst
Business Analyst
Reporting Analyst
BI Executive
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
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 |
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 |
Frequently Asked Questions
This course is ideal for students, fresh graduates, working professionals, and career switchers who want to enter the data analytics or business intelligence domain. It is beginner-friendly and does not require advanced technical knowledge.
No prior programming experience is mandatory. Basic computer knowledge is sufficient. The course includes Python fundamentals required for data analysis.
You will learn Python, Pandas, Matplotlib, Seaborn, and Jupyter Notebook — widely used industry tools for data analysis and visualization.
Yes. The course includes hands-on practice using real-world datasets and concludes with a Mini Data Analytics Project that you can showcase in your portfolio.
Yes. The course includes resume building support, portfolio guidance, interview preparation sessions, and job referral assistance where applicable.
After completing this course, you can apply for roles such as Junior Data Analyst, Reporting Analyst, Business Analyst (Entry-Level), MIS Executive, or BI Executive depending on your skill strength and project quality.
Freshers in Data Analytics roles in India typically earn between ₹3 LPA to ₹6 LPA. Salaries may vary based on city, company type, and practical skill level.
With 1–3 years of experience, professionals can earn ₹6–10 LPA. Mid-level analysts (3–5 years) often earn ₹10–18 LPA, while senior analytics professionals and managers can earn ₹18–30+ LPA depending on expertise and domain knowledge.
Yes. Data Analytics is one of the fastest-growing domains across IT, fintech, healthcare, retail, e-commerce, and startups in India. Organizations increasingly rely on data-driven decision-making.
Yes. With consistent practice and project completion, this course provides the foundational skills required to transition into entry-level analytics, reporting, or business intelligence roles.
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