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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
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Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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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
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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
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8 Ways to Improve Accuracy of Machine Learning Models
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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

What does not get discussed about analysis: Chaos to order

Priyanka
Priyanka Last Updated : 11 Oct, 2020
4 min read

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

Introduction

How can you ensure that the many lines of code you write meet the objective? The critical phases of analyzing data are after you have the dataset and before you set up the environment, to write codes. There is a science to what data we have access to and will it suffice to answer questions the business is seeking. That is a discussion for another time. Here we will focus on what happens once data is available.

An effort that is critical to the success of any analysis exercise is to organize thought.

 

Organizing data and thought

This is the phase before we begin to set up the environment to run the codes. The analysis begins much before we start writing the code. The process begins with understanding the question. Map it against the libraries of Python, Mathematics, statistics, color, language. That is not a comprehensive but a good starting point.

Let’s understand this with an example.

         “The learning curve is an upward sloping line with a positive delta”

That line will be understood differently by each reader here. Take a pause and note your thoughts. Think about what will be an appropriate visual description, other than words. You are welcome to share your thoughts with Analytics Vidya and the author even before you complete reading. you can write at [email protected].

There are many layers rolled up in that statement. Towards the end of the article, we will enlist them. Here we visit how we organize thought and the impact of color on visualizations.

 

Understanding Data sets

A data set (or dataset) is a collection of data. In a more technical sense, data are a set of values of qualitative or quantitative variables about one or more persons or objects, while a datum (singular of data) is a single value of a single variable. [1]

Tabulating is the first step towards organizing thoughts. It is a representation of something along rows and columns. Tabulation works great for numbers. Markdown cells are a good way to tabulate verbal descriptions.

Let’s look at the underlying data. Tabulating and quantifying aid understanding.

essential things data science

 

The temptation as a data scientist would be to say: value 1 performance at 25. If your first reaction was; why, who said so, can we make any conclusion basis this? Well, congratulations you are on your way to be a data scientist!

 

Visualization: Learning Curve: Line Graph

Visualization is a visual description of thought. Nonverbal descriptions are graphs.

Be aware that visualization of a learning curve need not always be a line graph. Nor does it have to be upward sloping. Data Science encourages us to build. Share your thoughts on other visualization possibilities of the learning curve.

 

Understanding color as a dimension

No matter how detailed the visualization is, if presented on a paper or screen it will be a 2-dimensional. Colour will add the third dimension.

Some of the simplest visualizations around our bar graphs. They are simple to decipher. They are simple also because they breed familiarity. Tally marks and bar graphs go back at least 10 years for most of us. Let us use Bar Graphs to understand color as a dimension.

The dominant features above can be presented as follows:

essential things data science

No matter how detailed the visualization is, if presented on a paper or screen it will be a 2-dimensional. All conversations will be between the x-axis and the y-axis. Colour will add the third dimension.

 

Layers to analysis

Preconceived notions and cultural influences add depth to data analysis. Data and Datum can be numbers, words, and expanding to be pictures and even voice. So everything that affects anything is data.

If you can organize thought, then you will be able to identify the variables. Each variable will be the heading of a column in the table. All readings together (if numeric) will be distributed. Data science will then become an exercise of understanding the interplay between variables.

Let’s enlist the various layers rolled up in the statement.

“The learning curve is an upward sloping line with a positive delta”.

  •  It is verbal.
  • In Python, I will need functions that work with string.
  • For a machine learning model, convert words to numeric form.
  • Graphs will help us visualize.
  •  Mathematics will help quantify.
  • Statistics will help make sense. What is the average performance? Can I add performance and learning effort? If the range of performance values were from 0- 100 but learning effort values ranged from 0 -1000. Then can I still use a line graph?
  • Cultural influences: make a radial graph of the same dataset. Think about if it will make sense to the reader. Will the reader have the motivation to invest the effort needed to read a complex graph?
  • Upward sloping line and positive delta: technical description.

In conclusion: organizing thought forms the bedrock of data analysis. Tabulation helps organize thought. Visualization will generate insights about variables. In the journey of data, scientists enlist as many layers. Explore the interplay between variables. Enjoy the journey! Share your thoughts with the author at

https://www.linkedin.com/in/priyanka-krishna-sharma/

and

[email protected]

 

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Priyanka
Priyanka

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Responses From Readers

Clear

Dr Vandana Sharma
Dr Vandana Sharma

Very interesting and thought provoking blog. Probably adding a locational tag may add another dimension to the data set and take the learning curve much higher.

123

Irina Stelmakh
Irina Stelmakh

Very interesting

123

Shubham Kangutkar
Shubham Kangutkar

Good one priyanka!

123

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