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Master Data Analysis with Pandas & NumPy: Certification Course | Hands-On Projects Included
Original price was: INR ₹11,000.00.INR ₹5,499.00Current price is: INR ₹5,499.00.
The Advanced Data Manipulation with Pandas and NumPy program is meticulously designed for those in data-intensive fields who seek to leverage the full potential of Python for high-level data analysis. Enroll with NanoSchool (NSTC) to get certified through industry-ready training. Enroll now with NanoSchool (NSTC) to get certified through industry-ready, professional learning built for practical outcomes and career growth.
Aim
Master Data Analysis with Pandas & NumPy teaches practical data analysis in Python using Pandas and NumPy. Learn data cleaning, transformation, aggregation, and analysis workflows through hands-on projects and certification assessment.
Program Objectives
- NumPy Core: arrays, indexing, vector operations.
- Pandas Core: Series/DataFrame, filtering, sorting.
- Data Cleaning: types, nulls, duplicates, outliers (basic).
- Aggregation: groupby, pivot tables, rolling stats (intro).
- Joins: merge, concat, keys, and data integrity checks.
- Time-Series: datetime parsing, resampling, trends.
- Performance: efficient operations and memory basics.
- Projects: complete multiple real datasets end-to-end.
Program Structure
Module 1: Setup + Data Analysis Workflow
- Jupyter/Colab setup; project folders and notebooks.
- Data workflow: import → clean → analyze → report.
- Reading data: CSV/Excel/JSON basics.
- Quick checks: head/info/describe, missing values.
Module 2: NumPy for Fast Computing
- Arrays, shapes, dtypes, broadcasting.
- Indexing, slicing, masking.
- Vectorized math and aggregations.
- Random sampling basics and reproducibility (seed).
Module 3: Pandas Fundamentals
- Series and DataFrame operations.
- Filtering, sorting, selecting columns/rows.
- String operations and categorical data (intro).
- Exporting clean datasets.
Module 4: Cleaning and Data Quality
- Missing data: isna, fillna, dropna.
- Type fixes: numeric/date parsing, coercion.
- Duplicates and consistency checks.
- Outliers: detection basics and safe handling.
Module 5: Aggregation and Reporting
- groupby: counts, means, shares.
- Pivot tables and cross-tabs.
- Window functions: rolling and expanding (intro).
- Building summary tables for KPIs.
Module 6: Combining Datasets
- merge: inner/left/right/outer joins.
- concat and append patterns.
- Key integrity: duplicates, missing keys, join audits.
- Reshaping: melt, pivot, stack/unstack (intro).
Module 7: Time-Series Analysis with Pandas
- Datetime conversion and indexing.
- Resampling (daily/weekly/monthly) and trend summaries.
- Lag features and simple seasonality checks (intro).
- Event-based analysis (basic).
Module 8: Speed, Memory, and Best Practices
- Vectorization vs loops; apply pitfalls.
- Memory-friendly types and categorical optimization.
- Reusable functions and clean notebooks.
- Reproducible outputs and documentation.
Hands-On Projects
- Project 1: Sales KPI analysis (clean → groupby → pivot).
- Project 2: Customer dataset join + cohort summary (intro).
- Project 3: Time-series analysis (resample + rolling trends).
- Project 4: Data quality audit report (missing/duplicates/outliers).
Final Assessment
- Timed practical test: clean + analyze a new dataset.
- Deliverables: notebook + final KPI tables + short insights summary.
Participant Eligibility
- Students and professionals learning analytics
- Beginners with basic Python knowledge
- Anyone working with CSV/Excel datasets
Program Outcomes
- Clean and analyze datasets using Pandas and NumPy.
- Build KPI tables with groupby and pivot workflows.
- Combine datasets safely using joins with integrity checks.
- Deliver multiple projects and a certification assessment notebook.
Program Deliverables
- e-LMS Access: lessons, datasets, notebooks.
- Toolkit: cheat sheets, cleaning checklist, project templates.
- Projects: 4 hands-on projects with review checklist.
- Assessment: certification after final practical test.
- e-Certification and e-Marksheet: digital credentials on completion.
Future Career Prospects
- Data Analyst (Entry-level)
- Reporting Analyst
- Business Analyst (Data)
- Research Data Assistant
Job Opportunities
- IT/Consulting: analytics and reporting projects.
- E-commerce/Retail: sales and customer analytics.
- Finance: KPI reporting and automation.
- Operations: performance dashboards and data quality roles.
| Brand | NSTC |
|---|---|
| Format | Online (e-LMS) |
| Duration | 3 Weeks |
| Level | Advanced |
| Domain | AI, Data Science, Automation, Aggregation |
| Hands-On | Yes – Practical projects with industrial datasets |
| Tools Used | Python, TensorFlow, Pandas, NumPy, Power BI, MLflow |
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What You’ll Gain
- Full access to e-LMS
- Publication opportunity
- Self-assessment & final exam
- e-Certificate
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