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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 5 Essential Data Science Tools

N
Neelu Last Updated : 28 May, 2021
5 min read

This article was published as a part of the Data Science Blogathon

Introduction

Data is a collection of facts and information like numbers, words, measurements, observations, etc that computers can process and provide results. The collection of data allows us to store, manipulate, and analyze important information about our existing and potential customers and find out meaningful insights. Today, gathering data can assist us for better understanding of our customers and business is became comparatively easy.

And most of the tech giants like Google, Facebook, Microsoft, IBM, Amazon Web Services, etc. and so many other major and minor companies are heavily investing their valuable time and precious resources into data and therefore the subject of data science. The rapid climb of the recognition of data Science has resulted in the creation of an array of various tools and technologies for the profit and benefit of Data scientists.

Data science is an emerging field that uses various methods, processes, algorithms, and techniques to bring out meaningful knowledge and insights from an enormous amount of structured and unstructured data. Data science also includes data mining, machine learning, and big data. It combines the study of domain expertise and programming skills using techniques and theories drawn from many fields within the context of mathematics, statistics, computing, domain knowledge, and information science.

In this blog, we will discuss and understand deep into the fantastic tools that are extremely helpful in developing and growing Data Science skills and for creating unique and practical projects also. These tools can be utilized for model building, processing, analyzing results, deployment purposes, and so much more.

Let’s get started:

1. GitHub

GitHub is a platform where developers can host their code for version control and collaboration. The primary benefit of GitHub is its version control system, which allows developers to uninterrupted collaborate with other developers without compromising the integrity of the original project. The projects hosted over GitHub are open-source software. GitHub is a platform where more than 65 million developers shape the future of software, together. GitHub is the best place for developers to manifest their codes and discuss projects with an exquisite community.

Now, Knowledge of GitHub has become one of the basic requirements for a Data Scientist. Data scientists got to use Github for an equivalent reason that software engineers do for collaboration, making changes to projects, and having the ability to trace and roll back changes over time. Traditionally Data Scientists didn’t have to use GitHub, as often the method of putting models into production was handled by software or data engineering teams. It is free and will open up one of the best places for developers to showcase their projects and collaborate with other amazing Data Scientists from the community.

Data Science Tools GitHub

Image source:  Developer community dev.to

2. IDEs

An integrated development environment (IDE) is a software platform that provides developers with comprehensive facilities to code and develops. It’s a coding tool that allows writing, testing, and debugging code more efficiently, as these IDEs typically offer code completion or code insights by highlighting them. IDEs help develop integrating the different aspects of a computer program. IDE plays an essential role in the development of Data Science (DS) and Machine Learning (ML) due to its vast libraries. Choosing the right IDE that suits our needs is often a most significant task. Here is the list of some IDEs suited for Data Science and Machine learning:

  • Google Colab
  • Jupyter Notebook
  • Spyder
  • Pycharm
  • Visual Studio Code
  • Thonny
  • Atom
  • Sublime Text

A good IDE like an assistant to Data Scientists to compile, debug, test code, and make it error-free.

Data Science Tools IDE

Image source: analyticsvidhya.com

3. Amazon Web Services (AWS) 

Amazon Web Services is a subsidiary of Amazon Company offering on-demand services of cloud computing platforms (IaaS, PaaS, SaaS) and APIs to many individuals, companies, and governments, based on a meter pay-as-you-go. These cloud computing web services provide a variety of basic building blocks and tools for distributed computing along with abstract technical infrastructure. Data scientists bestride on both businesses as well as the technical world with Data Analysis to achieve desired outcomes. In the field of Machine Learning (ML), Data Scientists design, develop, and build models from data by processing it, create and work on various algorithms, and train the models to predict and achieve their business goals.

Today in 2021, AWS comprises over 200 products and services including Cloud computing, Cloud Storage, Networking, Database Management, Data Analytics, Application Deployment, Machine Learning, Mobile development, Developer Tools, the Internet of things, and various other tools and services.

Data Science Tools AWS

Image source: analyticsvidhya.com

4. Kaggle

Kaggle is a subsidiary created by Google LLC. It is an online platform for Data Scientists and Machine Learning enthusiasts. Kaggle is an open community that allows users to find and publish various datasets for data science and machine learning, explore and build models in a web-based data-science environment, work with various other data scientists and machine learning engineers in the community, and can also participate in competitions to solve data science challenges. Kaggle was introduced in 2010 by providing Machine Learning competitions and now also offers a public platform for data, a broad desk for data scientists over the cloud, and also Artificial Intelligence education. Kaggle has run hundreds of machine learning competitions and these competitions have evolved many successful projects including HIV research, chess ratings, and traffic forecasting.

Kaggle

Image source: analyticsvidhya.com

5. Stack Overflow

Stack Overflow is a collaboration & knowledge-sharing SaaS platform for companies and as well as for programmers. Stack Overflow features questions and answers on a good range of topics in programming for IT enthusiasts and professionals. It is developed in 2008 by Jeff Atwood and Joel Spolsky and flagship site of Stack Exchange Network. It is an open-source community for developers to work together and help each other.

Till March 2021, Stack Overflow has recorded 14 million registered users and received over 21 million questions and 31 million answers. Most of the questions discussed are based on Java, Python, R, Android, and many more.

Stack Overflow

Image source: medium.com

Conclusion:

In this blog, we have discussed the most basic and essential data science tools that every data science aspirant should know. These tools help to grow the skills as well as get updates about the trending data science technologies.

Thanks for reading. Do let me know if there is any comment or feedback.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

N
Neelu

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