Before you can start building Data Analytics, Data Science, Machine Learning, Artificial Intelligence, or Automation projects, you need a properly configured Python development environment. A development environment is a collection of software tools that allow you to write, run, test, debug, and manage Python programs efficiently.
Many beginners think that installing only Python is enough. While Python is the core programming language, professional data analysts and data scientists also use tools such as Visual Studio Code (VS Code), Anaconda, Jupyter Notebook, JupyterLab, and Conda. Each tool has a specific purpose and helps improve productivity during different stages of development.
Throughout this course, you will use Visual Studio Code to learn Python programming concepts such as variables, operators, loops, functions, object-oriented programming, file handling, and modules. Later, when you begin working with NumPy, Pandas, Matplotlib, Seaborn, and Machine Learning, you will primarily use Jupyter Notebook, which is designed for interactive data analysis and experimentation.
In this lesson, you’ll install every tool required for the entire course. By the end of this lesson, your computer will be fully prepared for Python programming, Data Analytics, Data Science, Artificial Intelligence, and Machine Learning projects.
Although the installation process may seem lengthy, it only needs to be completed once. After everything is configured correctly, you’ll be able to focus entirely on learning Python and solving real-world business problems rather than worrying about software installation.
After completing this lesson, you will be able to:
Programming is much more than simply writing code. Professional developers use specialized tools to improve productivity, reduce errors, organize projects, install libraries, debug applications, and collaborate with other developers. Collectively, these tools form a development environment.
A complete Python development environment typically consists of:
Each tool performs a different task. Together, they provide everything required for professional Python programming, Data Analytics, and Machine Learning.
| Tool | Purpose |
|---|---|
| Python | The programming language that executes your code. |
| Visual Studio Code | A professional code editor used for Python programming. |
| Python Extension | Adds Python support inside Visual Studio Code. |
| Pylance | Provides intelligent code completion, error detection, and code navigation. |
| Anaconda | A complete Python distribution for Data Science. |
| Jupyter Notebook | Interactive environment for data analysis and machine learning. |
| JupyterLab | An advanced version of Jupyter Notebook. |
| Spyder | Scientific IDE designed for numerical computing. |
| Conda | Package and environment manager included with Anaconda. |
| pip | Python’s official package manager. |
Python is one of the most popular programming languages in the world because it is simple, readable, and extremely versatile. It is used by startups, multinational companies, research organizations, universities, and government institutions.
Unlike many programming languages that require hundreds of lines of code to perform simple tasks, Python focuses on readability. This makes it an excellent choice for beginners while remaining powerful enough for enterprise-level applications.
Today, Python powers thousands of real-world applications across different industries, including healthcare, finance, education, manufacturing, cybersecurity, transportation, e-commerce, and scientific research.
Visual Studio Code (VS Code) is one of the world’s most popular source code editors. Developed by Microsoft, it is lightweight, fast, free, and supports hundreds of programming languages through extensions.
Although Python programs can be written using simple text editors such as Notepad, professional developers prefer VS Code because it provides intelligent code completion, syntax highlighting, debugging tools, Git integration, and an integrated terminal.
Throughout the first half of this course, all Python programming exercises will be performed using Visual Studio Code because it closely resembles the development environment used by professional software developers.
Anaconda is a free Python distribution designed specifically for Data Science, Data Analytics, Machine Learning, Artificial Intelligence, and Scientific Computing. Instead of installing Python libraries one by one, Anaconda provides hundreds of pre-installed packages that are commonly used in data-driven projects.
In addition to Python, Anaconda includes powerful tools such as Jupyter Notebook, JupyterLab, Spyder, and Conda. These tools simplify package management, environment management, and interactive programming.
Many universities, research institutions, and corporate training programs use Anaconda because it eliminates the complexity of installing and configuring dozens of Python libraries individually.
Many beginners assume that Python and Anaconda are the same thing. They are not. Python is the programming language, whereas Anaconda is a distribution that includes Python along with numerous tools and libraries required for Data Science.
| Python | Anaconda |
|---|---|
| Programming Language | Python Distribution |
| Small Installation Size | Larger Installation Size |
| Uses pip | Uses Conda and pip |
| Ideal for Software Development | Ideal for Data Analytics and Machine Learning |
| Minimal Installation | Includes Hundreds of Scientific Libraries |
| Install Packages Manually | Most Libraries Already Available |
In this course, you will use both tools. Python provides the programming language, while Anaconda provides the ecosystem required for professional data analysis and machine learning.
Before installing the required software, ensure that your computer meets the following minimum requirements. Although these specifications are sufficient for learning Python and Data Analytics, higher specifications are recommended for working with large datasets and machine learning models.
| Component | Minimum Requirement | Recommended |
|---|---|---|
| Operating System | Windows 10 (64-bit) | Windows 11 |
| Processor | Intel Core i3 / AMD Ryzen 3 | Intel Core i5 or Higher |
| RAM | 4 GB | 8 GB or More |
| Storage | 5 GB Free Space | 20 GB SSD |
| Internet | Required | Broadband Connection |
| Administrator Rights | Recommended | Recommended |
In the next section, you’ll download and install Python, configure the Python Interpreter, understand the importance of the Add Python to PATH option, and verify that Python has been installed successfully before moving on to Visual Studio Code and Anaconda.
Python should always be downloaded from its official website to ensure that you receive the latest stable version along with important security updates and bug fixes. Avoid downloading Python from unofficial websites because modified installers may contain outdated or insecure software.
Open your preferred web browser and visit the official Python website. Navigate to the Downloads section, where the website automatically detects your operating system and recommends the latest stable version for Windows.
Click the Download Python button. Depending on your internet connection, the installer may take a few seconds or a few minutes to download.
For this course, always install the latest stable version of Python unless your organization specifically requires another version.
Python currently has only one actively supported major version: Python 3. Python 2 officially reached its end of life in January 2020 and is no longer maintained.
Modern libraries such as NumPy, Pandas, TensorFlow, Scikit-learn, Matplotlib, Flask, Django, and PyTorch all require Python 3. Therefore, you should always install the latest stable Python 3 release.
| Version | Status | Recommendation |
|---|---|---|
| Python 2 | Deprecated | Do Not Install |
| Python 3 | Supported | Recommended |
If multiple installers are available, choose the Windows Installer (64-bit). Almost every modern computer uses a 64-bit operating system.
After downloading the installer, locate it in your Downloads folder and double-click it. If Windows asks for permission to allow the installer to make changes, click Yes.
The Python Setup window will appear. Before clicking Install Now, there is one extremely important option that every beginner must understand.
At the bottom of the installer window, you’ll see a checkbox labeled Add Python to PATH.
Always enable this checkbox before clicking Install Now.
The PATH is an environment variable used by Windows to locate programs from the Command Prompt or Terminal. By adding Python to the PATH, you can execute Python from any folder simply by typing:
python
Without enabling this option, Windows will not know where Python is installed, and commands such as python or pip may display errors.
One of the most common mistakes made by beginners is forgetting to enable the Add Python to PATH option.
After selecting the PATH option, click Install Now.
Python will begin copying files and configuring your computer. This process usually takes one to three minutes depending on your computer’s performance.
Once the installation finishes successfully, you’ll see the following message:
Setup was successful.Click Close to exit the installer.
After the installation is complete, Windows may display an additional option called Disable Path Length Limit.
Older versions of Windows restrict file paths to approximately 260 characters. Some Python libraries create deeply nested directories, which can exceed this limit and cause installation errors.
Clicking Disable Path Length Limit removes this restriction for modern applications and is recommended for Python developers.
After installing Python, you should verify that the installation was completed successfully before proceeding further.
Open the Windows Command Prompt using the following steps:
A Command Prompt window will appear.
Type the following command:
python --versionIf Python has been installed correctly, you’ll see output similar to:
Python 3.13.xThe version number may vary depending on the latest stable release available when you install Python.
The Python Interpreter is the program responsible for reading and executing your Python code. Every time you run a Python program, the interpreter converts your source code into instructions that the operating system understands.
You can start the interactive Python interpreter by typing:
pythonYou’ll see something similar to:
Python 3.13.x
>>>
The >> symbol is called the Python Prompt. It indicates that Python is waiting for your commands.
Try typing:
print("Hello Python")Output:
Hello PythonTo exit the interpreter, type:
exit()Along with Python, another important tool is automatically installed called pip.
pip is Python’s official package manager. It allows you to download, install, update, and remove thousands of third-party Python libraries from the Python Package Index (PyPI).
Without pip, installing libraries manually would be extremely difficult. Fortunately, Python includes pip by default.
Open Command Prompt and run:
pip --versionIf pip is installed correctly, you’ll see output similar to:
pip 25.x from ...
(python 3.13)One of the strengths of Python is its enormous ecosystem of libraries. Using pip, installing a library requires only one command.
For example, to install NumPy:
pip install numpySimilarly, you can install other popular libraries:
| Library | Command |
|---|---|
| NumPy | pip install numpy |
| Pandas | pip install pandas |
| Matplotlib | pip install matplotlib |
| Seaborn | pip install seaborn |
| Scikit-learn | pip install scikit-learn |
Although pip is powerful, later in this lesson you’ll learn why Data Scientists often prefer using Conda, which comes with Anaconda and provides advanced package and environment management capabilities.
Sometimes, after installation, you may receive the following error:
'python' is not recognized as an internal or external command.This usually means that Python was not added to the Windows PATH during installation.
The easiest solution is to reinstall Python and make sure the Add Python to PATH checkbox is selected before clicking Install Now.
Alternatively, advanced users can manually configure the PATH environment variable, but reinstalling Python is generally faster and easier for beginners.
| Problem | Possible Solution |
|---|---|
| Python command not recognized | Reinstall Python and enable Add Python to PATH. |
| pip command not found | Reinstall Python or repair the installation. |
| Wrong Python version | Remove older versions and install the latest Python 3 release. |
| Permission denied | Run the installer as Administrator. |
| Multiple Python versions installed | Select the correct interpreter later in VS Code. |
Now that Python and pip are successfully installed and verified, the next step is to install Visual Studio Code, configure the Python development environment, install the required extensions, and prepare the editor for professional Python programming.
Now that Python has been installed successfully, the next step is to install a professional code editor. Although Python programs can be written using simple text editors such as Notepad, professional developers use Integrated Development Environments (IDEs) or source code editors because they provide features that make coding faster, easier, and less error-prone.
Throughout this course, we will use Visual Studio Code (VS Code). It is a free, lightweight, and powerful source code editor developed by Microsoft. VS Code supports hundreds of programming languages and can be customized using extensions.
Whether you are writing a simple Python program or building a Machine Learning project, VS Code provides an excellent development experience.
Visual Studio Code has become one of the most widely used code editors in the world because it combines simplicity with professional features. It is suitable for beginners while also providing advanced capabilities required by experienced developers.
Some of the major advantages of VS Code include:
Open your web browser and visit the official Visual Studio Code website. The website automatically detects your operating system and recommends the correct installer.
Click the download button and wait for the installer to finish downloading.
After the download is complete, open the installer from your Downloads folder.
When the installer starts, follow the installation wizard.
During installation, you will see several optional checkboxes.
It is recommended to enable the following options:
These options make VS Code easier to access while working on Python projects.
After installation, open Visual Studio Code.
The first screen usually contains:
Do not worry if these sections seem unfamiliar. You will gradually learn each component throughout this course.
| Component | Purpose |
|---|---|
| Explorer | Displays project files and folders. |
| Editor | Where you write Python code. |
| Terminal | Runs Python commands and programs. |
| Extensions | Install additional features. |
| Run & Debug | Execute and debug applications. |
| Status Bar | Shows interpreter and project information. |
VS Code does not understand Python automatically. To enable Python programming features, you must install the official Python extension provided by Microsoft.
Follow these steps:
After installation, VS Code gains several Python-specific features that make programming easier.
The next extension you should install is Pylance.
Pylance is Microsoft’s advanced language server for Python. It provides intelligent code analysis and significantly improves the programming experience.
Search for Pylance in the Extensions Marketplace and install it.
As you type Python code, Pylance automatically suggests variables, functions, modules, and parameters, reducing typing effort and helping prevent mistakes.
If your computer has multiple Python installations, VS Code needs to know which interpreter should be used to run your programs.
To select the interpreter:
After selecting the interpreter, VS Code will use that version whenever you run Python programs.
Organizing your files into projects is a good programming practice.
Create a folder named:
Python_BasicsInside this folder, create a new file named:
hello.pyYour project structure should look like this:
Python_Basics/
│
└── hello.pyOpen hello.py and type:
print("Welcome to Python Programming!")Save the file using Ctrl + S.
There are several ways to run Python programs in VS Code.
Click the Run button located in the upper-right corner of the editor.
Open the terminal using:
Ctrl + `Navigate to your project folder and execute:
python hello.pyOutput:
Welcome to Python Programming!| Shortcut | Purpose |
|---|---|
| Ctrl + S | Save File |
| Ctrl + N | New File |
| Ctrl + Shift + P | Command Palette |
| Ctrl + ` | Open Terminal |
| Ctrl + / | Comment or Uncomment Code |
| F5 | Run with Debugger |
| Shift + Alt + F | Format Document |
| Problem | Solution |
|---|---|
| Python interpreter not found | Select the correct interpreter using Python: Select Interpreter. |
| Python extension missing | Install the official Microsoft Python extension. |
| Pylance disabled | Install or enable the Pylance extension. |
| Terminal cannot find Python | Verify Python is installed and added to the PATH. |
| Program does not run | Ensure the file is saved with a .py extension. |
At this point, your Python programming environment is ready. However, Data Analysts and Machine Learning engineers use additional tools that make working with datasets much easier. In the next section, you will install Anaconda, explore Anaconda Navigator, understand Conda, and learn why Jupyter Notebook is the preferred environment for Data Analytics and Data Science.
Now that Python and Visual Studio Code have been successfully installed, the next step is to install Anaconda. While Python is sufficient for general programming, Data Analytics, Data Science, Artificial Intelligence, and Machine Learning projects require many additional libraries and tools. Installing these libraries one by one can be time-consuming and confusing for beginners.
Anaconda simplifies this process by providing a complete Python ecosystem that includes hundreds of pre-installed scientific libraries along with powerful development tools such as Jupyter Notebook, JupyterLab, Spyder, and Conda.
Because this course focuses on Data Analytics and Machine Learning, Anaconda will become one of your primary tools throughout the remainder of this course.
Professional Data Scientists often work with dozens of Python libraries in a single project. These libraries must be compatible with each other. Installing incompatible versions manually may lead to dependency conflicts and runtime errors.
Anaconda solves these problems by packaging compatible versions of popular libraries together and providing an easy-to-use package manager called Conda.
Instead of spending hours configuring software, you can focus on analyzing data and building Machine Learning models.
Open your preferred web browser and visit the official Anaconda website. Navigate to the Downloads section and select the installer appropriate for your operating system.
Most modern computers use the Windows 64-bit Installer. Download the latest stable release.
The installer is significantly larger than the standard Python installer because it includes Python, Jupyter Notebook, Conda, scientific libraries, and several additional development tools.
After downloading the installer, open it from the Downloads folder.
Follow these installation steps:
The installation may take several minutes because many packages are copied to your computer.
During installation, you may notice an option labeled Add Anaconda to my PATH environment variable.
Unlike the standard Python installer, Anaconda recommends leaving this option unchecked. Adding Anaconda to the system PATH may interfere with other Python installations already installed on your computer.
Instead, Anaconda provides the Anaconda Prompt, which automatically configures the required environment whenever you open it.
| Option | Recommendation |
|---|---|
| Add Anaconda to PATH | No |
| Register Anaconda as Default Python | Yes (if using Anaconda as your primary Python) |
Anaconda installs much more than just Python. It includes a complete collection of software used by Data Scientists.
| Software | Purpose |
|---|---|
| Python | Programming Language |
| Conda | Package and Environment Manager |
| Jupyter Notebook | Interactive Programming |
| JupyterLab | Advanced Notebook Environment |
| Spyder | Scientific Python IDE |
| NumPy | Numerical Computing |
| Pandas | Data Analysis |
| Matplotlib | Data Visualization |
| Scikit-learn | Machine Learning |
| Seaborn | Statistical Visualization |
| SciPy | Scientific Computing |
These libraries form the foundation of most Data Analytics and Machine Learning projects.
After installation, you can launch Anaconda Navigator from the Windows Start Menu.
Anaconda Navigator provides a graphical interface for launching applications, managing environments, and installing packages without using command-line commands.
It is especially useful for beginners because it eliminates the need to remember Conda commands during the early stages of learning.
| Application | Purpose |
|---|---|
| Jupyter Notebook | Create interactive notebooks. |
| JupyterLab | Advanced notebook environment. |
| Spyder | Scientific Python programming. |
| VS Code | Professional code editor (if installed). |
| CMD.exe Prompt | Windows Command Prompt. |
| Anaconda Prompt | Run Conda commands. |
Conda is the package and environment manager included with Anaconda. While pip installs Python packages, Conda can install both Python packages and non-Python dependencies. It also allows you to create isolated environments where different projects can use different versions of Python and libraries without conflicts.
For example, one project may require Python 3.11 while another requires Python 3.13. Conda allows both environments to exist on the same computer independently.
In later lessons, you’ll learn how to create and manage Conda environments for real-world Data Science projects.
Anaconda installs a special terminal called the Anaconda Prompt. Unlike the regular Command Prompt, it automatically configures Python and Conda so that all installed packages are immediately available.
Many Data Scientists prefer using the Anaconda Prompt when installing packages, creating environments, or launching Jupyter Notebook.
Open the Anaconda Prompt and type the following command:
conda --versionIf Anaconda has been installed successfully, you’ll see output similar to:
conda 25.x.xYou can also verify the installed Python version by running:
python --versionBoth commands confirm that Anaconda is correctly configured and ready for use.
In the next section, you’ll learn how to use Jupyter Notebook, create your first notebook, understand notebook cells, execute Python code interactively, and discover why Jupyter is the preferred environment for Data Analytics, Data Science, and Machine Learning.
Now that Anaconda has been installed successfully, you have access to one of the most powerful tools used in Data Analytics, Data Science, Artificial Intelligence, and Machine Learning—Jupyter Notebook.
Unlike a traditional Python program stored in a .py file, a Jupyter Notebook allows you to combine Python code, explanations, mathematical equations, tables, charts, and visualizations in a single interactive document. This makes it an ideal environment for exploring datasets, performing analysis, documenting findings, and sharing results with others.
Today, Jupyter Notebook is widely used by Data Analysts, Data Scientists, AI Engineers, Researchers, Financial Analysts, Universities, and Technology Companies because it supports interactive programming and rapid experimentation.
Jupyter Notebook is an open-source web-based application that allows you to create and run interactive notebooks. A notebook consists of individual cells where you can write Python code, explanatory text, formulas, charts, images, and outputs.
Each notebook is saved with the .ipynb extension, which stands for Interactive Python Notebook.
Unlike traditional programming where the entire program is executed from top to bottom, Jupyter Notebook allows you to execute one cell at a time. This makes debugging, experimenting, and analyzing data significantly easier.
Jupyter Notebook has become the standard environment for Data Analytics and Machine Learning because it provides an interactive workflow. Instead of repeatedly editing and running an entire program, you can execute only the cells you are currently working on.
This allows you to inspect intermediate results, visualize data immediately, and document your analysis alongside the code.
There are several ways to start Jupyter Notebook after installing Anaconda.
Your default web browser will automatically open the Jupyter Notebook dashboard.
Open the Anaconda Prompt and type:
jupyter notebookPress Enter.
The notebook dashboard will open in your default browser.
After Jupyter Notebook opens, navigate to the folder where you want to store your project.
Click:
New → Python 3A new notebook will be created.
Rename the notebook by clicking its default title (usually Untitled) and entering:
Hello_DataScienceThe notebook will now be saved as:
Hello_DataScience.ipynbEvery Jupyter Notebook is composed of cells. Each cell can contain different types of content.
| Cell Type | Purpose |
|---|---|
| Code Cell | Execute Python code. |
| Markdown Cell | Write formatted text, headings, and documentation. |
| Raw Cell | Store unformatted text. |
Most of your work in this course will involve Code Cells and Markdown Cells.
Click inside the first code cell and type:
print("Welcome to Data Analytics with Python!")Press:
Shift + EnterOutput:
Welcome to Data Analytics with Python!Unlike a traditional Python file, the output appears immediately below the code cell.
There are several ways to execute code inside Jupyter Notebook.
| Shortcut | Action |
|---|---|
| Shift + Enter | Run the current cell and move to the next. |
| Ctrl + Enter | Run the current cell and stay in the same cell. |
| Alt + Enter | Run the current cell and create a new cell. |
One of the greatest advantages of Jupyter Notebook is the ability to organize programs into multiple cells.
For example:
Cell 1
x = 100
y = 50Cell 2
print(x + y)Output:
150Each cell can build upon the variables and functions created in previous cells, making experimentation much easier.
Jupyter Notebook is not limited to writing code. You can also document your work using Markdown cells.
Markdown cells allow you to create:
This makes notebooks ideal for creating reports and documenting your analysis.
JupyterLab is the next-generation version of Jupyter Notebook. It provides a modern interface with additional productivity features.
Instead of opening only one notebook, JupyterLab allows you to work with multiple notebooks, terminals, text files, and datasets in a single workspace.
| Jupyter Notebook | JupyterLab |
|---|---|
| Simple Interface | Modern Workspace |
| Single Notebook | Multiple Tabs |
| Easy for Beginners | More Advanced Features |
| Basic File Management | Integrated File Browser |
| Lightweight | Supports Extensions |
Both VS Code and Jupyter Notebook are important tools, but they serve different purposes.
| Use VS Code For | Use Jupyter Notebook For |
|---|---|
| Learning Python Programming | Data Analysis |
| Object-Oriented Programming | NumPy |
| Building Applications | Pandas |
| Automation Scripts | Data Visualization |
| Flask and Django | Machine Learning |
| Large Python Projects | Experimentation |
In this course, we will use Visual Studio Code while learning core Python programming concepts such as variables, loops, functions, object-oriented programming, file handling, and modules. Once we begin working with datasets, NumPy, Pandas, data visualization, and Machine Learning, we will primarily use Jupyter Notebook because it provides a more interactive environment for data analysis.
In the next and final section of this lesson, you will learn about creating Conda environments, organizing Python projects, best practices, troubleshooting common issues, lesson summary, frequently asked questions, multiple-choice questions, and key takeaways.
As you continue learning Data Analytics and Machine Learning, you will eventually work on multiple Python projects. Each project may require different versions of Python or different library versions. Installing everything into one global Python installation can create dependency conflicts.
To solve this problem, Anaconda provides Conda Environments. A Conda environment is an isolated workspace containing its own Python interpreter and installed packages. Each environment is independent, allowing projects to use different library versions without interfering with one another.
Although you will learn Conda environments in detail in a later lesson, it is helpful to understand the basic concept now.
Open the Anaconda Prompt and run the following command:
conda create --name dataanalytics python=3.13Activate the environment:
conda activate dataanalyticsTo deactivate the environment:
conda deactivateYou do not need to create environments for every exercise in this course. However, for real-world projects, using separate environments is considered a best practice.
Maintaining a well-organized folder structure makes it easier to manage projects as they grow in size. Instead of storing Python files randomly across your computer, create a dedicated folder for your learning journey.
Python-Projects/
│
├── Python_Basics/
├── NumPy/
├── Pandas/
├── Data_Visualization/
├── Statistics/
├── Machine_Learning/
├── Deep_Learning/
├── Projects/
└── Datasets/Each topic should have its own folder containing related programs, datasets, notebooks, and project files.
Most Data Analytics projects involve working with datasets. Instead of downloading the same dataset repeatedly, maintain a dedicated folder where you store CSV, Excel, JSON, and other data files.
For example:
Datasets/
│
├── students.csv
├── sales.xlsx
├── employees.csv
├── population.csv
└── weather.csvKeeping datasets organized makes it easier to reuse them throughout different exercises and projects.
Professional developers follow certain practices that improve code quality, readability, and maintainability. Developing these habits early will help you write cleaner Python programs.
| Problem | Possible Cause | Solution |
|---|---|---|
| Python command not recognized | Python not added to PATH | Reinstall Python and enable “Add Python to PATH”. |
| pip not recognized | Python installation incomplete | Repair or reinstall Python. |
| VS Code cannot find Python | Interpreter not selected | Select the correct interpreter. |
| Pylance not working | Extension missing or disabled | Install or enable Pylance. |
| Conda command not found | Anaconda Prompt not used | Open Anaconda Prompt. |
| Jupyter Notebook does not open | Installation incomplete | Reinstall Anaconda. |
| Notebook kernel unavailable | Wrong Python environment | Select the appropriate kernel. |
| Package installation failed | Internet or dependency issue | Retry installation or update Conda. |
In this lesson, you successfully prepared your computer for Python programming and Data Analytics by installing the complete development environment. You learned the purpose of Python, Visual Studio Code, Anaconda, Jupyter Notebook, JupyterLab, and Conda, and understood how each tool contributes to the software development and data analysis workflow.
You also learned how to verify Python and pip installations, configure Visual Studio Code with the Python and Pylance extensions, launch Jupyter Notebook, create your first notebook, understand notebook cells, and organize projects using a professional folder structure.
Your computer is now fully configured for the remainder of this course. In the next lesson, you will begin writing Python programs and explore the language syntax in greater detail.
Yes. In this course, Python provides the programming language, while Anaconda provides tools such as Jupyter Notebook, Conda, and scientific libraries for Data Analytics.
Use VS Code for learning Python programming and building applications. Use Jupyter Notebook for Data Analytics, visualization, and Machine Learning.
Anaconda offers a free edition suitable for learning, education, and many development scenarios. Check Anaconda’s licensing terms if you plan to use it in a commercial environment.
pip installs Python packages, whereas Conda manages both packages and isolated environments, including non-Python dependencies.
Yes. Libraries can be installed using pip, but Anaconda simplifies package management for Data Analytics and Machine Learning.
Python source files use the .py extension.
Jupyter Notebook files use the .ipynb extension.
We will use VS Code while learning Python fundamentals and Jupyter Notebook extensively during Data Analytics, NumPy, Pandas, visualization, and Machine Learning lessons.
Which software is the programming language used in this course?
Which tool is primarily used for interactive data analysis?
Which package manager comes with standard Python?
Which package manager is included with Anaconda?
Which VS Code extension provides intelligent code completion for Python?
What is the file extension for a Jupyter Notebook?
Which command checks the installed Python version?
python --versionWhich command checks the installed Conda version?
conda --versionWhich command activates a Conda environment named dataanalytics?
conda activate dataanalyticsWhich development environment will primarily be used when working with datasets, Pandas, NumPy, and Machine Learning?
Congratulations! You have successfully completed the setup of your Python development environment. Your computer is now ready for the rest of this course.