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Anaconda for Data Science: Features, Setup, Projects, Use Cases

26th Apr, 2024
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    Anaconda for Data Science: Features, Setup, Projects, Use Cases

    In this century, as Technology is growing, so is Data, and to use it, we need to understand the pattern and the science of it, and so is Data Science, one of the fastest and most crucial techs every product and organization needs. Getting started with Data Science can be made smooth if you get a proper toolkit and environment set up on your machine. So, Anaconda will help you with this. If you need to know how to integrate Data Science and Anaconda Features, check out the Data Science Course in India, taught by industry experts. Let’s start with the anaconda for data science features, how to install the anaconda library, and create a project step-by-step. 

    What is Anaconda in Data Science?

    Before going on to what Anaconda is, we need to understand what data science is and the major requirements in this field. Data Science is the field of analyzing the patterns or behavior of the data collected in the form of text, images, audio or video through statistical or machine learning algorithms, which include visualizing, preprocessing, and modeling the data. 

    Anaconda (or Anaconda Navigator) is an open-source platform that provides data science toolkits and inbuilt packages for performing various data science tasks. This provides an extra advantage of inter-dependencies of packages, which means it provides you with an environment where each package is compatible with the other. 

    Reasons to include in your Data Science Journey: 

    • It provides you with more than 8000+ DM/ML Packages. 
    • It is easy to use. 
    • It helps you to maintain production-ready projects. 
    • It provides tested and regularly updated packages. 

    Some examples of the best python packages for data science available on anaconda are: 

    • Programming Language: Python and R 
    • Data Processing: Numpy and Pandas 
    • Data Visualization: Matplotlib and Seaborn 
    • Data Modelling: Scikit-learn and Tensorflow 
    • IDE: Spyder and Jupyter Notebook 

    Features of Anaconda Data Science

    1. Anaconda Repository

    Anaconda Repository is like a package marketplace where you can get Installers, Packages, and tools for Free. As per official statistics, they provide more than 100 installers with more than 8000+ packages for Data Science and Machine Learning.  

    2. Anaconda Navigator

    Anaconda Navigator is a UI Interface, as shown above, which allows you to access different tools like Jupyter Notebook, VS Code, and Spyder directly by clicking on the launching button.

    It also allows you to maintain the environment and packages through the Environments tab in the left-side panel. As shown in the above figure, the base is the default environment, with all packages installed in the environment along with versions and descriptions 

    3. Conda

    Conda is the command-line tool for Anaconda, which means you can control anaconda and python packages through CLI, which most developers prefer. In the further tutorial, we will see how we can manage packages through conda. 

    How to Install Anaconda for Data Science? [Step-by-Step]

    1. Graphical Installation of Anaconda

    In this section, we will be going through the steps of installing the Anaconda Navigator. We are installing it for MacOSx, but you can also go for the same steps by downloading Anaconda Navigator for Windows. 

    • Step 1: Visit here, and it will auto-detect the OS and click Download

    • Step 2: Go to Explorer and click downloaded package once the package is downloaded. It will show its installer home page.

    • Step 3: Click on Continue; it will move to the Read Me section, then click continue till the Installation Type Section; here, you can change your install location if you want by clicking Change Install Location.

    • Step 4: Click on Install, and Installation will start.

    • Step 5: After it gets installed, you can have a look at your applications; you will see the logo of Anaconda Navigator.

    2. Command Line Installation of Anaconda

    • Step 1: Download Miniconda based on your system, and click on the downloaded package.

    • Step 2: Click on Continue, till Installation Type, and select Install; it will install the command line anaconda to the system.

    • Step 3: Verify Installation by typing `conda` in Terminal. 


    1. Installing Packages

    To install or add a package in the conda environment, the basic syntax is: 

    conda install <package name> 

    For example, if we need to install matplotlib (A Visualizing Library), head over to the terminal and type the below code and press enter. Using the above command, you can install any data science package. 

    conda install matplotlib 

    2. Remove Package

    To remove the package from the conda environment, the basic syntax is

    conda uninstall <package name> 

    For example, if we need to remove matplotlib, head over to the terminal and type the below code and press enter. 

    conda uninstall matplotlib 

    3. Update Package

    To update a package in the conda environment, the basic syntax is

    conda update <package name> 

    For example, if we need to update matplotlib (A Visualizing Library), head over to the terminal and type the below code and press enter. 

    conda update matplotlib 

    4. Search Package

    To search a package in the conda environment, the basic syntax is: 

    conda search <package name> 

    For example, if we need to update matplotlib (A Visualizing Library), head over to the terminal and type the below code and press enter. It will provide all different versions of matplotlib available in conda. 

    conda search matplotlib

    How to Set Up and Create an Anaconda Project?

    Setting up an Anaconda Project provides you with a clear project structure, package dependencies and ready-deployed architecture. Before starting every project, creating Conda Environment is a good practice. The environment is like a container that contains all packages and versions relevant to the project; it helps you to maintain reproducibility, i.e., it helps you to run that particular project anywhere without concern about the system or cloud you are running on. 

    If you want different python or package versions, you can resolve this issue by creating environments, as different packages require different versions for compatibility. Before going on further to create a project, let’s see various commands related to the environment. 

    1. Environment Commands

    a. Creating Environment 

    The environment can be created in anaconda from the base or using configuration files. We will be using Anaconda CLI for both processes. To create an environment from the base, type:

    conda create --name env_name python 

    To create an environment from a configuration file, type and press Enter:

    conda env create --file environment.YAML 

    b. Activate or Deactivate Environment 

    After creating environments, we need to activate them to take them into effect. To activate the environment 

    conda activate env_name 

    To deactivate the environment, need to be in that environment.

    conda deactivate env_name 

    2. Setup the Anaconda Project

    In this section, we will setup up the demo project, which will be created in the following section. Here are the steps: 

    1. Create a Folder named demo_project. 
    2. Open the terminal and change location to dir by typing cd demo_project 
    3. Create a new environment as described in the above section. 
    4. Activate the environment you created. 
    5. Open the Anaconda Navigator. 
    6. After launching it, click on Spyder or VS Code for code editing. 
    7. Create a New File and rename it as demo.py 

    3. First Anaconda Project

    In this section, we will create a demo project based on the iris dataset; we will be loading and visualizing the data.

    import pandas as pd
    import seaborn as sns
    df = pd.read_csv("iris.csv")
    g = sns.pairplot(df,hue="variety")


    Anaconda Data Science Projects with Source Code

    In this section, we will be discussing various beginner projects in data science. If you want to work on more complex professional projects, check out the Data Science Bootcamp program

    1. Credit Card Fraud Detection

    This project deals with the problem statement of detecting whether a particular card transaction is a fraud or not based on features. It has significant features, as it combines Imbalance with Classification Dataset. Dataset can be downloaded from here

    2. Walmart Store’s Sales Forecasting

    This dataset requires forecasting future sales across various departments within various Walmart locations for different holidays. It helps you to understand time series. Dataset and Sample Code can be found here

    3. Fake News Detection

    To combat the spread of fake news, it is critical to understand the veracity of the information, which this project will help with. Python would be used to accomplish this, and a model would be created using TfidfVectorizer. Sample Dataset and Code can be found here. 

    Real-World Anaconda Data Science Use Cases

    1. Neural Networks

    Using Anaconda, we can build and deploy Neural Networks using various compatible libraries like Tensorflow and Keras. It will help you to model CNNs, GANs, and RNNs. 

    2. Machine Learning

    Scale your machine learning pipeline operations horizontally and vertically using GPUs. Store and process data beyond the RAM of a single computer with ease and cut model training time by up to 100x. Parallelize algorithms and accelerate iteration cycles throughout the development phase. 

    3. Predictive Analytics

    With Anaconda and open-source data science, more firms are taking a proactive approach to tackle challenges across the organization. We can assist you in becoming more proactive by anticipating customer churn, consumer demand levels, stock pricing, maintenance requirements, and outage probability. 

    4. Data Visualization

    From factory production to seismic activity, there is a visualization tool for any data set. With our one-click deployment solution, they will be able to swiftly design and deploy beautiful dashboards and get them into the hands of decision-makers. 

    5. Bias Mitigation

    The Explainability of models is critical for conducting an ethical AI programme. LIME and InterpretML are two Python utilities that can be used with Anaconda. These tools assist you in explaining black box model decisions as well as creating "glassbox" models that are designed to be explainable from the start. 

    Pros and Cons of Anaconda

    The advantages and Disadvantages of Anaconda Data Science are 

    Advantages of Anaconda

    • Multiple environments, all separate from each other and your system Python. 
    • Easy package install, installing binaries when available (no compiling needed). 
    • You can still fall back on pip to install into the environment if the package isn't available to conda. 
    • Anaconda Python is very fast than vanilla Python. They bundle Intel MKL, which makes NumPy computations faster. 
    • Packages installed in multiple environments are hard-linked, saving space (i.e., if you have two environments with the same packages, the second one takes up no space). 

    Disadvantages of Anaconda

    • It comes with a lot of packages by default, so it takes up a lot of space. To avoid this, install a miniconda instead of an anaconda. You can later install whatever packages you want. 
    • Conda package manager is fragile and slow.

    How is Anaconda Different from Other Platforms?

    Anaconda is different from other data science platforms in various ways: 

    AnacondaData Science Platform
    It is Open SourceSome platforms are proprietary.
    It runs on a Local ServerThey provide their own server to run codes.
    It can be used by multiple teams (like data visualizing team and data analysis)Other platforms focus on particular teams, e.g., Big Data or Deployment.


    In this article, we have seen how Anaconda is useful for Data Science and how it can be installed with its most useful command with a demo project. I would like you to now implement what we have discussed in the projects suggested in the article. Check out KnowledgeHut’s Data Science Course in India, which includes highly professional courses along with high-impact projects. 

    Frequently Asked Questions (FAQs)

    1Is Anaconda good for Data Science?

    Yes, it is good for Data Science, as it provides you with an advantage of package management, tools, and deployment from a single platform. It also helps in project structure for production-ready projects. 

    2Do I Need To Install Python before Anaconda?

    No, you won’t require to install Python before Anaconda; it comes with a python package. If you want a specific Python version, then you have to look for particular Anaconda versions supporting that version. 

    3Who Uses Anaconda?

    Anaconda is getting into the work of Python or R Developers, Data Visualization experts, Data Analysts, Data Scientists, Machine Learning Engineers, and Deep Learning Researchers, also integrated into MNCs to carry out their daily tasks related to Data. 

    4Do Companies Use Anacondas?

    Yes, almost every company which are dealing with data in their day-to-day tasks is using Anaconda. It is because of its advantage of tools, packages, and deployment functionality under one platform. 

    5What is the Difference Between Anaconda And Jupyter?

    Anaconda is a platform that contains tools, and packages under one platform, like spyder, jupyter notebook, and other tools, whereas Jupyter is the original web application for creating and sharing computational documents. It provides a straightforward, streamlined, document-centric experience. 

    6What is the Difference Between a Python and Anaconda?

    Anaconda is an open-source platform supporting various packages of Python and R, while Python is a language that runs on the toolkits provided by it.


    Ashish Gulati

    Data Science Expert

    Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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