Online course information for New Delhi

Python Data Analytics for learners in New Delhi

Learners in New Delhi can review ACLM’s Python Data Analytics syllabus and practical learning outcomes. Contact ACLM to confirm current delivery options, suitable session timings and enrolment details. This page does not imply a physical ACLM centre in this location.

ACLM offers online learning where available. Confirm current delivery options and suitable timings with the institute; this page does not imply a physical ACLM centre in this location.

ACLM Institute of Professional Studies

Python Data Analytics Online Course in New Delhi

Python Data Analytics Training & Certifications

06 MonthsOnline / OfflineIntermediateACLM CertificationACLM Certification

Explore Course

The Python Data Analytics training is designed to teach engineers, data scientists, statisticians, and other quantitative professionals the Python skills they need to use with the Python programming language to analyze and creating customized chart as per the actual data.

COURSE OVERVIEW

Course Overview

ACLM Institute of Professional Studies presents Python Data Analytics, an intermediate six-month programme delivered through online and offline training modes. The course develops practical capability in Python programming, data handling, statistical analysis and visualisation, with focused work using Pandas, NumPy, SciPy and Matplotlib. Learners progress from Python foundations to data cleaning, dataset integration, statistical methods and introductory machine-learning approaches through instructor-led, live one-to-one sessions, notes, assignments, assessments and projects.

WHY THIS COURSE

Why Learn This Course?

Python Data Analytics brings programming and analytical thinking together. This course helps learners work with structured and unorganised data, identify data-quality issues, prepare datasets, perform statistical analysis and communicate findings through charts, graphs and maps. Its practical sequence is suitable for students and professionals who want to build a stronger foundation in Python-based analytics.

LEARNING OUTCOMES

What You Will Learn

Python versions, similarities and differences across versions, and core language concepts.
Python data types, lists, tuples, dictionaries, mixed data collections and variables.
Global and local variables, reusable code, loops, data flow and exception handling.
Sequential and random number generation, dates, times, binary values and hexadecimal codes.
Python modules, classes, collections, parametric functions and non-parametric functions.
File handling for CSV, TXT, TBDL and other unorganised data formats.
Installation and removal of external Python libraries.
Pandas DataFrames, NumPy ndarrays, SciPy and spreadsheet-oriented data handling with xlrd.
Data identification, missing-data treatment, bad-data detection and cleaning techniques.
Merging and combining multiple datasets for analysis.
Charts, graphs and maps using Python visualisation techniques.
Statistics and modelling concepts including linear regression, differentiation, t-tests, f-tests, sampling and correlations.
Introductory machine-learning approaches and their relationship with statistical analysis.
Practical analytical work using Python, Pandas, SQL, NumPy, SciPy and Matplotlib.

SKILLS

Skills You Will Gain

Write and organise Python code for analytical tasks.
Use core Python data types, collections, variables, loops and functions.
Create reusable modules and work with classes.
Read, process and write common data files.
Use Pandas DataFrames and NumPy arrays for data handling.
Apply SciPy tools and statistical techniques to analytical problems.
Detect missing, inconsistent and poor-quality data.
Clean, transform and merge datasets.
Create analytical charts, graphs and maps with Matplotlib-based workflows.
Interpret basic relationships, samples, tests and regression outputs.
Structure practical data-analysis projects using Python and related tools.
Document work through notes, assignments and assessment activities.

WHO SHOULD JOIN

Who Should Join?

Students and professionals seeking intermediate-level training in Python-based data analytics. The course is relevant to learners interested in programming, data preparation, statistics, visualisation and practical analytical projects.

PREREQUISITES

Prerequisites

Knowledge of any programming language is required. Familiarity with basic programming logic will help learners progress through the Python and analytics exercises.

TRAINING APPROACH

Training Methodology

ACLM Institute of Professional Studies delivers the programme through instructor-led, live one-to-one training in online and offline modes. The learning process combines demonstrations, guided practice, notes, assignments, assessments and practical projects. Offline training is available in Greater Noida West, Greater Noida, Noida, Ghaziabad and Delhi NCR. Online delivery is available for learners in India and international markets.

CURRICULUM

Detailed Course Syllabus

Module	Topics / Lessons
Python Foundations for Analytics Build the Python foundation required for structured analytical work, including version awareness, syntax, data types and reusable variables.	Python Versions and the Analytics Environment — Review previous Python versions, note important similarities and differences, and understand the role of Python in data analytics. 60 min Data Types and Core Collections — Work with strings, numbers, lists, tuples, dictionaries and other Python collections through guided exercises. 75 min Variables and Mixed Data Sets — Create variables, reuse values, distinguish local and global scope, and construct mixed data sets using different data types. 75 min Practice Assignment: Python Foundations — Apply core syntax, collections and variables in a small assignment supported by notes and instructor feedback. 60 min
Control Flow, Functions and Python Structures Develop structured programming habits through loops, functions, modules, classes and exception handling.	Looping and Data Flow — Use conditional logic and different loop patterns to process values and control data flow. 75 min Parametric and Non-Parametric Functions — Design functions with and without parameters, return results and organise repeated analytical operations. 75 min Modules, Classes and Reusable Code — Create Python modules and introductory classes, and understand how reusable components support larger projects. 90 min Exception Handling and Assessment — Use try and except blocks to handle common errors and complete an assessment covering the module’s programming concepts. 75 min
Numerics, Dates, Files and Libraries Work with numerical sequences, dates, time values, encoded representations and external Python libraries.	Sequential and Random Number Series — Generate sequential and random number series for numerical exercises and analytical preparation. 60 min Dates, Times, Binary and Hexadecimal Values — Work with date and time values and generate binary and hexadecimal representations through practical exercises. 75 min File Handling for Unorganised Data — Read, write and process CSV, TXT, TBDL and similar data files while considering structure and consistency. 90 min External Library Installation and Management — Practise installing and uninstalling external Python libraries and review the role of Pandas, NumPy, SciPy and xlrd. 60 min
NumPy, Pandas and Data Handling Introduce the principal data-handling tools used in the course, with emphasis on arrays, DataFrames and practical transformations.	NumPy Arrays and Analytical Data Structures — Create and work with NumPy ndarrays, inspect dimensions and apply suitable operations to numerical data. 90 min Pandas DataFrames and Series — Create, inspect, select and transform Pandas Series and DataFrames for analytical workflows. 90 min Reading, Writing and Exploring Data — Import data from supported files, review structure and summary information, and prepare data for further analysis. 90 min Data Handling Assignment — Complete a guided assignment that combines file input, DataFrame operations and NumPy-based preparation. 75 min
Data Cleaning and Dataset Integration Develop a disciplined process for identifying data-quality issues, cleaning records and combining multiple sources.	Identifying Bad and Missing Data — Detect missing values, inconsistent entries, unsuitable data types and other common data-quality problems. 75 min Cleaning and Standardising Data — Apply practical cleaning techniques, standardise selected values and retain a clear record of transformations. 90 min Merging Various Data Sets — Join and merge datasets using common fields, review the resulting structure and check for unintended duplication. 90 min Data Quality Review Project — Prepare a multi-source dataset, perform quality checks and submit a documented cleaned-data project. 90 min
Visualisation with Python Turn prepared analytical data into clear graphical outputs for exploration and communication.	Charts and Graphs for Data Analysis — Create and interpret suitable charts and graphs for categorical, numerical and comparative data. 90 min Matplotlib Workflows — Build and customise Matplotlib visualisations with titles, labels, legends and readable layouts. 90 min Maps and Visual Representation — Explore map-oriented representations and select visual forms that support the data and analytical question. 75 min Visual Reporting Assignment — Create a concise visual report from a prepared dataset and explain the choices made for each representation. 90 min
Statistics and Analytical Modelling Apply core statistical concepts and modelling techniques to support evidence-based interpretation of data.	Sampling, Descriptive Measures and Correlations — Review sampling concepts, summarise data and examine relationships between variables using correlations. 90 min T-Tests and F-Tests — Study the purpose and interpretation of t-tests and f-tests within practical analytical examples. 90 min Linear Regression and Data Modelling — Build an introductory linear-regression workflow, examine model relationships and discuss interpretation of results. 105 min Differentiation and Statistical Practice — Connect differentiation and related mathematical ideas with analytical exercises, then complete a statistics practice assessment. 90 min
Integrated Python Data Analytics Project Consolidate Python, data handling, statistics and visualisation skills through an end-to-end project and final review.	Introductory Machine-Learning Approaches — Understand how introductory machine-learning approaches relate to statistical analysis and identify suitable analytical workflows. 75 min Project Planning and Data Ingestion — Define a project question, identify required data, load source files and establish a reproducible working structure. 90 min End-to-End Analysis and Visualisation — Clean, merge and analyse the project data, then create visual outputs and apply selected statistical techniques. 120 min Project Presentation and Final Assessment — Present the completed Python analytics project, review decisions and complete a final assessment with instructor feedback. 90 min

HANDS-ON EXPERIENCE

Practical Projects

Python Data Preparation and Quality Review — Load mixed datasets, inspect data types, identify missing and bad data, apply cleaning rules and produce a documented prepared dataset. Tools: Python, Pandas, NumPy, CSV and TXT file handling
Multi-Source Dataset Integration — Read datasets from multiple files, standardise selected fields, merge the sources and validate the resulting DataFrame for analysis. Tools: Python, Pandas DataFrames, NumPy and SQL
Exploratory Analysis and Visual Reporting — Analyse a prepared dataset, calculate descriptive measures and create charts, graphs and map-oriented visual representations to communicate findings. Tools: Python, Pandas, Matplotlib, NumPy and SciPy
Statistical Relationship and Modelling Study — Use sampling, correlations, hypothesis-test concepts and linear regression on a suitable dataset, then document assumptions and interpretations. Tools: Python, SciPy, Pandas, NumPy and Matplotlib
Integrated Python Analytics Portfolio Project — Complete an end-to-end analytical workflow covering data ingestion, cleaning, merging, analysis, visualisation and a concise project report. Tools: Python, Pandas, SQL, NumPy, SciPy and Matplotlib

CAREER APPLICATION

Career Opportunities

The course supports the development of practical skills relevant to Python programming, data preparation, data analysis, statistical analysis and data visualisation work. Career direction depends on the learner’s existing education, experience, portfolio and further development; the course does not guarantee employment or placement.

CERTIFICATION

Certification

Learners may receive an ACLM Certificate of Participation, subject to the applicable course requirements and completion process. The certification available for this course is an ACLM Certificate of Participation.

FAQ

Frequently Asked Questions

What is the duration of the Python Data Analytics course?

The course duration is 06 months.

Who is this course for?

The course is designed for students and professionals at an intermediate level who want practical Python data-analytics skills.

What prerequisite is required?

Knowledge of any programming language is required.

What technologies and topics are covered?

The course covers Python from core to advanced concepts, Pandas, NumPy, SciPy, Matplotlib, file handling, data cleaning, dataset merging, statistics, introductory machine-learning approaches and practical work with SQL.

Is the training available online and offline?

Yes. ACLM delivers the course in online and offline modes through instructor-led, live one-to-one training. Offline training is available in Greater Noida West, Greater Noida, Noida, Ghaziabad and Delhi NCR.

Is recorded training available?

No. The supplied course details specify live online and offline training and do not list recorded training.

What practical work is included?

Learners complete assignments, assessments and projects involving Python, Pandas, SQL, NumPy, SciPy and Matplotlib, including data cleaning, merging, analysis and visualisation.

What certification is available?

An ACLM Certificate of Participation is available, subject to the applicable course requirements and completion process.

Is employment or placement guaranteed after the course?

No employment, placement or salary guarantee is stated for this course. Outcomes depend on the learner’s background, portfolio and continued development.

START YOUR JOURNEY

Ready to Start Learning?

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