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Last updated on: November 3, 2025

System & Software Requirements for Data Science and Analytics Programs (Windows | Mac | Cloud Setup Guide)

System Requirements:

  • Laptop or Desktop with a minimum of 8 GB RAM (16 GB or higher recommended for capstone projects that involve larger datasets)
  • 500 GB hard disk (SSD recommended)
  • Minimum Intel i5 processor (latest generation preferred)
  • Windows Operating System is recommended, as some software is not supported on macOS. However, we can provide alternative approaches for Mac users.
  • Webcam required during assessments (tests and vivas)
  • Administrative access (to install software, packages, etc.)
  • Stable internet connection

 

Note:

For Mac Users: Certain tools like Microsoft SQL Server and Power BI may not be fully compatible with macOS. While the institute cannot provide full installation support for these tools on Mac, we’ll guide you with possible alternatives such as online cloud versions, Mac Bootcamp, or virtualized Windows environments. Please note, however, that these options may not replicate all features exactly.

For Official/Company Provided Laptops: Due to administrative restrictions, security policies, and antivirus controls, installation of some tools may not be possible. The institute will not be responsible for resolving such issues. It is strongly recommended to use a personal laptop for a smoother learning experience and complete access to all required tools.

Technical Support:

AnalytixLabs team will provide step-by-step guidance to help you set up the required tools and virtual environments. However, please note that:

AnalytixLabs will guide trainees through the installation process,  and provide resources; however, trainees should be responsible for installation on their machines.

Support includes:

    • Installation guides for Windows and macOS
    • Guidance for setting up UTM or Parallels
    • Assistance with online / cloud-based alternatives
    • Resource links and demo videos for tool installation

Software used in the course:

  • Data Analytics & Visualization: MS-Excel, MS-SQL Server, Power BI
  • Python for Data Science: Anaconda Distribution (Python) + Jupyter Notebook + VS Code + Google Colab (Python Packages: os, warnings, datetime, numpy, pandas, scipy, matplotlib, seaborn, statsmodels, re, etc…)
  • Machine Learning using Python: Anaconda Distribution (Python) + Jupyter Notebook + VS Code + Google Colab (Python Packages: os, warnings, datetime, numpy, pandas, scipy, matplotlib, seaborn, statsmodels, re, nltk, nlp, keras, sklearn, xgboost, catboost etc…)
  • MLOps: VS Code + Git/Github + Streamlit + Render

Software Requirements

To help you gain hands-on expertise, this Data Analytics Course is designed around three essential tools — Excel, SQL, and Power BI — which together form the foundation of every real-world analytics workflow.

You will learn how to work efficiently with datasets, perform data cleaning, analysis, and build impactful dashboards using these tools across various operating systems.

1. Excel (Pre-installed Requirement)

Microsoft Excel forms the foundation of your learning journey in this course.

You’ll use Excel extensively to perform data cleaning, transformation, visualization, and descriptive analysis before moving to SQL and Power BI for deeper analytics and storytelling.

Microsoft Excel (Part of MS Office Suite):

  • Requirement: Participants should have Microsoft Office (with Excel) installed.
  • No installation steps are provided, as it’s part of the licensed MS Office Suite.
  • Recommended Version: Office 2016 or later / Microsoft 365.

2. SQL (Structured Query Language)

SQL is the backbone of data analytics and one of the most in-demand technical skills for analysts, business analysts, data engineers, and data scientists.You’ll master how to query databases, extract information, and perform data manipulation using Microsoft SQL Server as the core learning tool.
For Windows Users
Recommended Setup: Can directly install:
  • Microsoft SQL Server Express
  • SQL Server Management Studio (SSMS)

This setup provides full control over database creation and management.

For macOS Users
Since MS SQL Server is not directly supported on macOS, alternate platforms can be used for SQL practice.

Recommended Alternatives:
Since MS SQL Server is not natively supported on macOS, there are alternate approaches to ensure seamless learning.
  1. Azure Data Studio: A cross-platform SQL editor by Microsoft that runs on macOS. You can connect it to a Cloud SQL database.
  2. Virtual Machine (VM) software for macOS.
    1. UTM(Free): Free and open-source virtual machines for Apple Silicon and Intel Macs. Organize your UTM (Free) virtual machine to run Windows 11 ARM and install SQL Server Express + SSMS
    2. Parallels Desktop (Paid): Commercial Virtual Machine tool with excellent performance and native Windows integration.
  3. MonsterASP.netUsing MonsterASP.net, you can:
    • Create and host a SQL Server database in the cloud.
    • Upload your own .bak files (database backups) or .sql scripts to restore existing databases.
    • Access the database remotely using tools like: SQL Server Management Studio (SSMS)
  4. Python SQL Integration: For local practice, you can run SQL queries directly using Python packages such as sqlite3 or pandasql
    1. sqlite3 (Built-in, Lightweight Database): Use when: You need a local SQL database file or want to practice SQL queries quickly. Advantages:
      • Comes built-in with Python (no install needed)
      • Works with .db or .sqlite files
      • Fully functional SQL database in a single file
    2. pandasql (Run SQL directly on Pandas DataFrames) Use when: You already have data in Pandas and want to query it using SQL syntax instead of Python functions. Advantages:
      • Super easy for analysts who prefer SQL
      • Works in-memory on Pandas DataFrames
Online SQL Platforms for practice:
These platforms are ideal for quick SQL learning and testing your code without installation.
  1. W3Schools: Quick practice with live SQL, but cannot upload your own datasets
  2. DB Fiddle: Multi-database support (MySQL, PostgreSQL, SQLite), but Temporary sessions only i.e, not suitable for projects or storing your work permanently.
  3. SQLite Online: Fast and browser-based, but no persistent data storage, and Limited file size support
  4. Leetcode: It’s an environment where you can practice many SQL questions

3. Power BI

Power BI Setup Guide

Power BI is a modern Business Intelligence tool that brings your data to life with interactive dashboards and data storytelling. You’ll learn how to connect datasets, model data, and create dynamic visualizations.

For Windows Users

Download Power BI Desktop:

🔗 https://powerbi.microsoft.com/desktop/

Run the installer and complete setup.

For macOS Users

Since Power BI Desktop is not available for macOS, learners can use:

  1. Power BI Service (Web Version)
    Cloud-based Power BI is accessible through any browser. Create and share reports directly online.
    The Power BI web version has a few restricted features compared to the desktop version.
    Restrictions when it comes to data modeling, transformation, and creation that only Power BI Desktop supports.
    Access Power BI online: 🔗 https://app.powerbi.com
    Requires a Microsoft account.
  2. Virtual Machine (VM) software for macOS.
    1. UTM(Free)
      Free and open-source virtual machines for Apple Silicon and Intel Macs.
      Organize your UTM (Free) virtual machine to run Windows 11 ARM and install Power BI Desktop.
    2. Parallels Desktop (Paid)
      Commercial Virtual Machine tool with excellent performance and native Windows integration.

4. Python (Anaconda Distribution)

Python is the core programming language for data science, known for its simplicity, flexibility, and strong community support.

In this course, you will use Anaconda, a free, open-source Python distribution that comes pre-installed with essential data science libraries like NumPy, Pandas, Matplotlib, Scikit-learn, Seaborn, Jupyter Notebook, and many more.

For Windows Users & macOS (Intel or M-series chip):
  1. Visit: 🔗 https://www.anaconda.com/products/distribution
  2. Download Anaconda Individual Edition (for your OS).
  3. Run installer → select “Add to PATH” if prompted.
  4. Verify installation:
    conda --version
  5. Launch Anaconda Navigator → Access Jupyter Notebook and Spyder.

After installation, you can launch:

  • Jupyter Notebook – for interactive Python coding
  • Spyder IDE – for script-based development
  • Anaconda Navigator – for managing environments and packages easily

 

5. Visual Studio Code (VS Code)

VS Code Setup Guide

Purpose: Code editing for Python, Streamlit, and Git integration.

Windows & macOS Installation
  1. Download from 🔗 https://code.visualstudio.com/
  2. Install and open VS Code.
  3. Add the following extensions:
    • Python
    • Jupyter
    • GitHub Copilot (optional)
  4. Verify Python integration:
    Open VS Code Terminal → type: python --version

6. Git & GitHub

Purpose: Version control, collaboration, and code management.
For Windows
  1. Download Git: 🔗 https://git-scm.com/download/win
  2. Install with default settings.
  3. Verify: git --version
For macOS
  1. Check if Git is already installed: git --version
  2. If not, it will prompt you to install Command Line Tools.
  3. Or download manually: 🔗 https://git-scm.com/download/mac

GitHub Setup
  1. Create an account: 🔗 https://github.com/join
  2. (Optional) Install GitHub Desktop: 🔗 https://desktop.github.com/
 

7. OneRender

OneRender Setup Guide

Purpose: 3D rendering, design, and visualization.

For Windows
  1. Visit: 🔗 https://onerender.com/
  2. Create an account → Download the Windows installer.
  3. Follow on-screen setup.
For macOS
  1. Visit the same site → Download the macOS installer (.dmg).
  2. Install by dragging to the Applications folder.

8. Streamlit

Purpose: Build and deploy interactive data web apps in Python. Installation (After Python/Anaconda Setup)
  1. Open Terminal (Mac) / Command Prompt (Windows)
  2. Run: pip install streamlit
  3. Test installation: streamlit hello
  4. A demo app should open in your browser.
Online Python Environments (Alternatives to Anaconda (if not able to install))
If you face issues installing Anaconda due to system restrictions or limited storage, online alternatives can be used to learn and practice Python seamlessly.
Google Colab (Purpose: Run Python notebooks without installation)
Free, cloud-based Jupyter Notebook environment by Google. Pre-installed with all major Python libraries for data science. It provides a ready-to-use Python workspace in the cloud — perfect for learning, experimenting, and collaborating on data science projects without installing anything on your system. It requires only a Google account and offers full functionality without installation.
For Both Windows & macOS
  1. Visit: 🔗 https://colab.research.google.com/
  2. Sign in with your Google account.
  3. You can upload or create .ipynb files and run code in the browser.

Disclaimer: The institute strives to keep all information, software links, and setup instructions up to date and reviews them periodically. However, the institute cannot be held responsible for any changes, updates, or deprecations in the third-party software, tools, or packages that may alter system requirements or installation procedures. In such cases, the institute will provide the best possible guidance and support to help learners identify and follow the most practical course of action.
 
Last updated on: November 3, 2025
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