ACLM Institute of Professional Studies

Python Data Science

Python Data Science Course & Certifications

12 MonthsOnline / OfflineIntermediateACLM CertificationACLM Certification

Python Data Science, a placement oriented program, keeping in view of career focus on data scientist or a data engineer. The prime objective is, to make the candidate suitable for handling complex data set over analyzing them through various important tools and make them actionable. Interested in some other courses too?

COURSE OVERVIEW

Course Overview

The ACLM Institute of Professional Studies Python Data Science course is a 12-month, intermediate-level programme covering data analysis, database handling, statistical programming, visualisation and introductory artificial intelligence and machine learning. The curriculum combines SAS Base, Tableau, SQL, Python Core, Pandas, NumPy, SciPy, Matplotlib, Seaborn and related tools through instructor-led, live one-on-one training. Training is available online for learners in India and international markets, and offline in Greater Noida West, Greater Noida, Noida, Ghaziabad and Delhi NCR.

WHY THIS COURSE

Why Learn This Course?

Data science requires a practical understanding of programming, data preparation, databases, statistical analysis and visual communication. This course brings these areas together in a structured learning path, helping students and professionals build the foundation required to work with real-world datasets and present meaningful findings. ACLM combines live instruction with notes, assignments and practical data science projects for consistent practice.

LEARNING OUTCOMES

What You Will Learn

Core programming concepts in Python, including variables, data types, control flow, functions, modules and file handling.
Data manipulation and analysis using Python, Pandas and NumPy.
Numerical and scientific computing with NumPy and SciPy.
Data visualisation using Matplotlib, Seaborn and Tableau.
SQL database concepts, querying, joins, aggregation and data preparation.
SAS Base programming, data steps, procedure steps, libraries, datasets and variable handling.
Notebook-based and interactive development using Jupyter and IDLE.
Foundations of artificial intelligence and machine learning, including data preparation, model concepts, evaluation and responsible interpretation.
How to structure data science projects from problem definition through analysis, visualisation and presentation.

SKILLS

Skills You Will Gain

Python programming for data work
Data cleaning and transformation
Exploratory data analysis
SQL querying and relational data handling
SAS Base data processing
Statistical and numerical analysis
Dashboard and chart creation
Data storytelling and visual communication
Introductory machine learning workflow
Project documentation and analytical presentation

WHO SHOULD JOIN

Who Should Join?

Students and professionals who want to develop practical data science capabilities using Python and complementary analytics tools. The course is suitable for learners at an intermediate level who are prepared to practise programming, databases and analytical problem-solving.

PREREQUISITES

Prerequisites

Basic programming and database knowledge are required. Familiarity with spreadsheets or general data concepts may be helpful, but is not mandatory.

TRAINING APPROACH

Training Methodology

Instructor-led, live one-on-one training delivered through online and offline modes. The learning process includes guided demonstrations, notes, assignments, regular practice and data science projects. Recorded training is not included. Offline training is available in Greater Noida West, Greater Noida, Noida, Ghaziabad and Delhi NCR. Online learning is available for India and international markets.

CURRICULUM

Detailed Course Syllabus

Module	Topics / Lessons
Orientation to Data Science and Analytical Workflows Introduces the data science lifecycle, analytical problem-solving, data types, project structure and the role of Python, SAS, SQL and visualisation tools in a complete workflow.	Data Science Roles, Use Cases and Lifecycle — Understand the stages from problem definition and data collection to analysis, communication and review. 120 min Types of Data and Analytical Questions — Distinguish structured and unstructured data, categorical and numerical variables, and descriptive versus predictive questions. 120 min Setting Up the Learning Environment — Become familiar with Jupyter and IDLE, files, folders, notebooks, scripts and reproducible working practices. 120 min Project Documentation and Analytical Ethics — Learn to record assumptions, data limitations, analytical decisions and responsible interpretation of results. 120 min
SAS Base Programming and Data Management Build a foundation in SAS Base, including the SAS environment, libraries, data steps, procedure steps, datasets, variables and data-flow concepts.	Overview of the SAS System and SAS Tasks — Explore the SAS system, common analytical tasks, generated output and the purpose of SAS programming components. 150 min SAS Program Structure: DATA and PROC Steps — Write and read basic SAS programs using DATA steps for data preparation and PROC steps for analysis and reporting. 180 min SAS Libraries, Datasets and Options — Work with SAS data libraries, dataset naming conventions, system options and dataset options. 180 min Variables, Attributes and Data-Step Processing — Handle numeric and character variables, inspect attributes and understand how observations move through the DATA step. 180 min SAS Data Access and Summary Practice — Import or access a structured dataset, inspect its contents, apply basic transformations and generate summary output. 180 min
SQL Database Fundamentals for Data Science Develop the database knowledge needed to retrieve, combine, summarise and prepare data for analytical work.	Relational Databases and Table Design — Understand databases, tables, rows, columns, keys, relationships and the importance of consistent data types. 150 min SELECT Statements and Filtering — Retrieve required fields, apply conditions, sort results and use aliases for readable analytical queries. 180 min Aggregations, GROUP BY and HAVING — Calculate counts, sums, averages and other summaries while grouping records for business analysis. 180 min Joins and Multi-Table Analysis — Use inner and outer join concepts to combine related tables and identify unmatched records. 210 min SQL Data Preparation and Query Practice — Build a sequence of queries that cleans, filters and prepares a dataset for Python or Tableau analysis. 210 min
Python Core for Data Analysis Establish the programming foundation required to write readable Python scripts and analytical notebooks.	Python Syntax, Variables and Data Types — Work with numbers, strings, Boolean values, type conversion, operators and basic input-output operations. 180 min Conditions, Loops and Comprehensions — Use if statements, for and while loops, range, list comprehensions and logical expressions for data-oriented tasks. 210 min Collections and Structured Data — Practise with lists, tuples, sets and dictionaries, including indexing, iteration and nested structures. 210 min Functions, Scope and Reusable Code — Define functions, use parameters and return values, manage scope and organise repeated analytical operations. 210 min Modules, Files and Error Handling — Import modules, read and write text or structured files, handle exceptions and create maintainable scripts. 210 min
NumPy, Pandas and Data Preparation Learn the principal Python tools for tabular data manipulation, numerical operations, cleaning and transformation.	NumPy Arrays and Vectorised Operations — Create arrays, understand dimensions and shapes, select values and perform efficient numerical calculations. 180 min Pandas Series, DataFrames and Importing Data — Create DataFrames, inspect columns and indexes, and load common structured data sources for analysis. 210 min Selecting, Filtering and Transforming Data — Use indexing, conditions, sorting, calculated columns, mapping and type conversion to prepare data. 210 min Missing Values, Duplicates and Data Quality — Identify missing and duplicate records, apply suitable treatment strategies and validate the cleaned output. 210 min Grouping, Merging and Reshaping — Perform groupby analysis, joins, concatenation, pivoting and reshaping for practical analytical questions. 240 min Data Preparation Project Workshop — Complete a guided cleaning and transformation workflow and document each decision in a Jupyter notebook. 240 min
Scientific Computing and Data Visualisation Use SciPy, Matplotlib and Seaborn to analyse numerical patterns, create effective charts and communicate findings.	Descriptive Statistics and Analytical Summaries — Calculate and interpret central tendency, spread, distributions and comparative summaries. 180 min SciPy for Scientific and Statistical Operations — Explore relevant SciPy functionality for scientific calculations, distributions and selected analytical procedures. 210 min Matplotlib Fundamentals — Create line, bar, scatter, histogram and box plots with titles, labels, legends and appropriate scales. 210 min Seaborn for Exploratory Visualisation — Use Seaborn to examine distributions, relationships, categories, correlations and patterns in tabular data. 210 min Visual Design and Data Storytelling — Select suitable charts, avoid misleading presentation, annotate important findings and build a coherent analytical narrative. 180 min
Tableau for Interactive Reporting Translate prepared data into visual analysis and dashboards using Tableau, with emphasis on clarity, comparison and user interaction.	Tableau Interface, Data Connections and Fields — Understand workbooks, worksheets, dimensions, measures, data types and the process of connecting prepared data. 150 min Charts, Filters and Calculated Fields — Build common visualisations, apply filters and create calculated fields for meaningful comparisons. 210 min Dashboards and Interactive Views — Combine worksheets into dashboards, arrange visual hierarchy and add practical interactions. 210 min Dashboard Design and Analytical Communication — Use suitable layouts, labels, colour choices and annotations to make findings understandable to the intended audience. 180 min Tableau Reporting Project — Create and review a dashboard based on a prepared dataset and present the analytical conclusions. 240 min
Artificial Intelligence and Machine Learning Foundations Introduce the concepts and workflow of AI and machine learning while connecting them to data preparation, evaluation and project practice.	AI, Machine Learning and Data Science Concepts — Differentiate artificial intelligence, machine learning, supervised learning, unsupervised learning and analytical reporting. 180 min Problem Definition and Dataset Preparation — Frame an analytical problem, identify target and input variables, prepare data and consider leakage and limitations. 210 min Introductory Regression and Classification — Understand the purpose and workflow of regression and classification models through guided examples. 240 min Clustering and Pattern Discovery — Explore the basic idea of grouping observations, selecting useful features and interpreting discovered segments. 210 min Model Evaluation and Interpretation — Study suitable evaluation concepts, validation, error analysis, comparison and the responsible communication of model results. 240 min End-to-End Project Review — Bring together data preparation, exploration, modelling, visualisation and documentation in a guided data science project. 300 min
Integrated Portfolio and Assessment Practice Consolidates the course into practical work, revision, project presentation and a structured review of the major tools and techniques.	Selecting a Data Science Project Question — Choose a manageable question, identify the required data and define the intended analytical output. 150 min Integrated Data Workflow Execution — Complete a workflow using suitable combinations of SQL, Python, SAS, visualisation and introductory machine learning techniques. 300 min Quality Review, Debugging and Validation — Check data quality, code clarity, calculations, visual accuracy, assumptions and reproducibility. 210 min Project Presentation and Feedback — Present the project method and findings clearly, respond to review questions and record improvement points. 210 min Final Revision and Skills Assessment — Review core concepts across all modules and complete a structured assessment of practical data science skills. 240 min

HANDS-ON EXPERIENCE

Practical Projects

Retail Sales Data Analysis with Python — Clean and analyse a retail dataset using Python, Pandas and NumPy. Learners calculate summary metrics, identify trends, handle missing values and produce visual findings using Matplotlib and Seaborn. Tools: Python, Pandas, NumPy, Matplotlib, Seaborn, Jupyter
SQL-to-Tableau Business Reporting Project — Work with relational business data by writing SQL queries, preparing an analysis-ready dataset and creating Tableau charts and dashboards to communicate key patterns and comparisons. Tools: SQL Database, Tableau
SAS Data Preparation and Summary Project — Create a structured SAS workflow using libraries, data steps and procedure steps. Learners inspect variables, transform records, generate summaries and document the processing decisions. Tools: SAS Base
End-to-End Data Science and Machine Learning Study — Complete a guided project covering problem definition, data inspection, preprocessing, exploratory analysis, feature preparation, introductory model training, evaluation and presentation of results. The project emphasises interpretation and clear documentation rather than unsupported predictions. Tools: Python, Pandas, NumPy, SciPy, Matplotlib, Seaborn, Jupyter, introductory machine learning libraries

CAREER APPLICATION

Career Opportunities

The skills developed in this course can support further learning and entry-level work aligned with data analysis, Python-based analytics, reporting, business intelligence and foundational machine learning. Specific employment, placement, salary or role outcomes depend on the learner's prior experience, portfolio, assessment performance and relevant hiring requirements.

CERTIFICATION

Certification

Successful course participation is supported by an ACLM Certificate of Participation, as specified for this programme. The certificate is issued by ACLM Institute of Professional Studies and does not represent an external accreditation or employment guarantee.

FAQ

Frequently Asked Questions

What is the duration of the ACLM Python Data Science course?

The course duration is 12 months.

Who is this course designed for?

The course is designed for students and professionals with basic programming and database knowledge who want to develop intermediate-level data science skills.

What technologies are covered?

The curriculum covers SAS Base, Tableau, SQL Database, Python Core and Pandas, NumPy, SciPy, Matplotlib, Jupyter, IDLE, AI and machine learning, Seaborn and multiple supporting libraries.

Is the course available online and offline?

Yes. Training is available online and offline. Online learning is available for India and international markets. Offline training is available in Greater Noida West, Greater Noida, Noida, Ghaziabad and Delhi NCR.

What is the training format?

The training is instructor-led and live one-on-one. It includes notes, projects and assignments. Recorded training is not included.

Does ACLM provide a certificate?

The course includes an ACLM Certificate of Participation, subject to the programme's participation requirements. It is not described as an external accreditation or a placement guarantee.

What practical work is included?

Learners work on data science projects involving Python analysis, SQL and Tableau reporting, SAS data preparation, and an integrated introductory machine learning workflow.

What fee is listed for the course?

The supplied course facts list the fee as INR 72,000. Learners should confirm current commercial terms with ACLM Institute of Professional Studies before enrolment.

START YOUR JOURNEY

Ready to Start Learning?

Contact ACLM Institute of Professional Studies for course guidance, demo scheduling and registration.

Contact ACLM

Want the complete syllabus?

Get the detailed module-wise ACLM syllabus in PDF after mobile verification.