Python for Data Analysis (NumPy, Pandas, Matplotlib) Course in Hyderabad
Analyse and visualise data with Python, NumPy, pandas and Matplotlib
The Python for Data Analysis course teaches you to clean, explore, analyse and visualise data using the core Python data stack. You start with Python fundamentals, then learn NumPy arrays and vectorised computation, pandas Series and DataFrames, reading and writing CSV, Excel, JSON and SQL data, selecting and filtering, handling missing values and duplicates, data types and dates, merging and joining, grouping and aggregation, reshaping with pivots and melts, string processing and time series. You create plots with Matplotlib and Seaborn, build exploratory data analysis reports, work with APIs and web data, write reusable functions and use Jupyter notebooks and Git. Performance tips such as vectorisation and efficient data types are included, with an introduction to Polars at an overview level. The course suits graduates, analysts and professionals who want to move beyond Excel; no prior programming is required. You practise on real datasets and finish with a capstone analysis. Classes run in classroom batches at the GloryTecks centre in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Python Fundamentals
- •Installing Python, VS Code and Jupyter
- •Data types, operators, conditions and loops
- •Functions, modules and error handling
- •Lab: Write small programs that process lists and strings
- •Outcome: Write basic Python confidently
Data Structures & Files
- •Lists, tuples, dictionaries and sets
- •List and dictionary comprehensions
- •Reading and writing text, CSV and JSON files
- •Lab: Parse a JSON file into tabular data
- •Outcome: Choose and use the right data structure
NumPy Essentials
- •ndarray creation, shapes and data types
- •Indexing, slicing, boolean masks and broadcasting
- •Vectorised math, aggregation and random numbers
- •Lab: Replace loops with vectorised NumPy code
- •Outcome: Do fast numerical work without loops
pandas Basics
- •Series and DataFrame structures; reading CSV, Excel, JSON and SQL
- •Selecting rows and columns with loc, iloc and boolean filters
- •Sorting, ranking, unique values and counting
- •Lab: Explore a sales dataset with pandas
- •Outcome: Load and inspect any tabular dataset
Data Cleaning & Preparation
- •Detecting and handling missing data
- •Removing duplicates, fixing types and renaming columns
- •String methods, regular expressions and categorical data; outlier handling
- •Lab: Clean a messy customer dataset
- •Outcome: Turn raw data into analysis-ready data
Merging, Joining & Reshaping
- •Merge, join and concat; join types and key problems
- •Pivot tables, melt and wide versus long data
- •MultiIndex basics
- •Lab: Combine orders, customers and products into one table
- •Outcome: Combine and reshape data correctly
Grouping, Aggregation & Time Series
- •groupby, aggregation, transform and apply
- •Date and time handling, resampling and rolling windows
- •Cohort and period-over-period calculations
- •Lab: Compute monthly growth and rolling averages
- •Outcome: Summarise data across groups and time
Data Visualisation with Matplotlib & Seaborn
- •Matplotlib figure and axes model
- •Seaborn statistical plots and styling
- •Choosing charts and annotating insights
- •Lab: Build a visual dashboard of key charts
- •Outcome: Communicate findings with clear visuals
Exploratory Data Analysis
- •Systematic EDA workflow and checklist
- •Correlations, distributions and group comparisons
- •Automated profiling tools and writing findings
- •Lab: Write an EDA report on a business dataset
- •Outcome: Find and explain patterns in data
SQL, APIs & Web Data
- •Reading from databases with SQLAlchemy and pandas
- •Calling REST APIs with requests and flattening JSON
- •Web scraping basics and legal and ethical considerations
- •Lab: Build a dataset from an API and a database
- •Outcome: Collect data from several sources
Performance, Automation & Polars Overview
- •Efficient dtypes, chunked reading and avoiding apply
- •Packaging analysis into scripts and functions; scheduling basics
- •Polars overview and when to use it
- •Lab: Speed up a slow analysis
- •Outcome: Handle larger data and repeat analyses automatically
Capstone Project & Portfolio
- •Capstone: acquire, clean, analyse and visualise a real dataset
- •Git, README and notebook best practices
- •Presentation of findings and interview preparation
- •Outcome: A portfolio-ready analysis project
Tools Covered
Projects Included
- Sales Data Cleaning and Summary: Clean a messy sales file and summarise it by region and month; deliverable is a notebook and cleaned dataset.
- Customer Orders Analysis: Merge orders, customers and products and compute customer metrics; deliverable is a notebook with findings.
- Time Series Dashboard in Charts: Build rolling and growth charts for monthly data; deliverable is a chart pack and insights.
- API to Insight Pipeline: Pull data from a public API and analyse it; deliverable is a script and a short report.
- Capstone EDA Report: Perform and present a full analysis of a real dataset; deliverable is a GitHub repository and report.
Start Your Journey Today
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the Python for Data Analysis (NumPy, Pandas, Matplotlib) course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on Python for Data Analysis (NumPy, Pandas, Matplotlib).
Certificate of Completion
Python for Data Analysis (NumPy, Pandas, Matplotlib)
This certifies that
[Your Name]
has successfully completed the
Python for Data Analysis (NumPy, Pandas, Matplotlib)
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured Python for Data Analysis (NumPy, Pandas, Matplotlib) course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Which Python version and tools are used?
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Best Python for Data Analysis (NumPy, Pandas, Matplotlib) Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Data Analyst, Python Developer (data), Business Analyst, Reporting Analyst; support is guidance with the job search, not a guarantee of a job.