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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

Applying Software Engineering Process for more effective Data Science Projects

S
Shivam Last Updated : 15 Jun, 2021
6 min read

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

Hello World!

An interesting title isn’t it? I thought the same when the idea to write a blog about this came to me.

If you are from a computer science background, you may be already knowing about the Software Engineering process. But if you are not then here are some basics for you to know.

What is Software Engineering?

Confused What Is It GIF by Kim's Convenience what is Software Engineering Data Science

 

IEEE, in its standard 610.12-1990, defines software engineering as —

Software Engineering is the application of a systematic, disciplined, which is a computable approach for the development, operation, and maintenance of software.

In simple words,

Software Engineering is the process of analyzing user requirements and then designing, building, and testing software application which will satisfy those requirements.

 

And What is the Software Engineering process?

karley sciortino GIF by SLUTEVER Software Engineering Data Science

 

In terms of software engineering, there not some rigid process but an approach to develop software. This process is divided mainly into five tasks– Communication, Planning, Modelling, Construction, and Deployment.

Communication: It is mainly about communicating with your customers to understand their requirements.

Planning-: It is about planning the whole process of the development of software.

Modelling: It is about creating models to better understand software requirements and the design that will achieve those requirements.

Construction: In this, the actual code is generated and testing is done.

Deployment: The completed software is delivered to the customer who evaluates the delivered product and provides feedback based on the evaluation.

These are the five basics tasks of the Software Engineering process. But it may be possible that some of these tasks may overlap.

Now you may be thinking

 ‘How does this help us in making more effective data science projects?’

I think this process may be getting applied by professionals either knowingly or instinctively.

So I am writing this mostly for my fellow students, but since learning never stops, we all are students for our entire lives, aren’t we?

But then how to use this process for data science projects done by students?

Thinking Think GIF by The official GIPHY Page for Davis Schulz Software Engineering Data Science

 

Well, let’s just split the process into five tasks again—but for the data science project now.

 

Task 1. Communication:

As I stated above this is mainly about getting customer requirements. Now requirements over here may be of customers, supervisor, etc. But say you have a dataset on which you want to do a data science project on so where are the requirements.

I think requirements are ones that you start the project with like—What do you gain from doing this project? How applicable this project is in the real world? etc.

Then, what kind of challenges could you face during this task?

The most important and the most difficult one(in my honest opinion) is — Understanding the business problem. It relates to just a simple question. Have You really understood the business problem?

Solution for the challenge:

The most effective and the easiest solution for the above problem would be to just Speak!

  • Speak with your supervisor, mentor, etc.
  • Try and understand the business problems.
  • Note down all the requirements.
  • Ask questions and doubts regarding the problem.

But doing just this isn’t enough! 

Try and explain everything you have understood to your mentor or supervisor. They may correct few misunderstandings if there are any and this may help you to make a better data science project.

If you are doing the project solo and don’t have a mentor, speak with your friends. Ask for their inputs. You could also speak with your family and explain the problem to them. Take their help to gain a third-party perspective.

“Sometimes asking for help also means you are helping yourself.” – Renuka Pitre

So, now you have done Task 1. Let’s go to Task 2.

Task 2. Planning:

This step is mostly about planning your data science project. Like how much time are you going to require to do it? or what dataset do you require(if you don’t have it) or whether you are going to use supervised learning or unsupervised or reinforcement learning? etc. comes into this step.

So, what kind of challenges could you face during this task?

It is mostly related to questions that I stated above– how much time are you going to require to do it? or what dataset do you require(if you don’t have it)?

Solution for the challenges:

For the first question(how much time are you going to require to do it?), just plot a timeline chart. Decide how much time you are going to spend on data preprocessing, model evaluation, etc. Making a rough timeline chart can help because you would have a rough deadline for the completion of your project.

This is most effective for solo projects that students do(most just take it easy including me too). This helps us to learn time management and how to use our time effectively. For job/internship seekers, it also shows the ones hiring that you can use your time effectively to do projects.

As for the second question– this is mostly for students who are doing solo projects. Data is freely available nowadays. You can probably find it on Kaggle or Google, but remember to select the right dataset since there are many. If you cannot find it, then I would suggest learn web scraping or ask a friend who knows web scraping for help. The second option also shows that you are willing to work in a team.

Now onto the next task.

 

Task 3. Modelling:

In this step, you would be doing data preparation and gaining insights from the data which you are using. In short, Data Preprocessing and  EDA or Exploratory Data Analysis come in this step.

Challenge and Solution?

There could be only two challenges — Proper Data Processing and EDA. For Data Preprocessing, do thorough preprocessing. Because the more clean your data is, the better your model is going to be.

There is not much challenge for EDA I guess. But as far as I have seen, doing a more in-depth EDA leads to a better Data Science project. Just a suggestion but you could do basic and quick EDA in Excel, Tableau, or Power Bi to understand some trends in data, and more in-depth in python and R.

Now let us go to the main part of the project.

Task 4. Construction:

Comedy Central Thank You GIF by The Jim Jefferies Show

Now I am not going to say much about it since you may already have understood what comes in this.

Your actual data science project happens in this step.

Like what model you are using? How much accuracy of the model should be? etc.

What could be challenged in this step?

There are many challenges and errors in this step but the most important one would be– Choosing the Right Algorithm.

Solution?

Now, most of the small challenges and errors could be resolved by googling or stack-overflowing them(which we do when in doubt). The most important one– well that depends. It mostly depends on what type of relationship your data has between the feature and the target variables. It mostly helps if you try various models to find out which works the best.

Now, let’s go to the most important step in my opinion.

Task 5. Deployment:

You have now finished your project and you wish to send it to the client. This step mainly involves showing your client what you have done to improve their business.

And based on their feedback should you revise your model or not.

Then, what kind of challenges could you face during this task?

The only challenge I can think of would be– Communication of Results.

Solution?

Well, most clients and stakeholders don’t know a lot of technical jargon, so explaining it technically to them wouldn’t help much. Explaining it in simple and layman language would help. Most effective would be using PPTs and the presentation of your results graphically.

For students doing solo projects, just make a web app using Flask or streamlit to explain your findings then deploy it on the web using Heroku.

So,

We have now finished applying the software engineering approach to a data science project.

But, don’t forget the most important thing about the software engineering approach. It is not the process but the documentation.

Having documentation about your thinking and how you applied it in form of the above process, would help both you and your client.

 

Key Takeaways:

We learned about software engineering, the process of software engineering, and how to apply this process for a more effective data science project.

That’s it from me.

 

About the Author

Shivam Parab

Currently, I am pursuing my Bachelor of Engineering (B.E) in Computer Engineering from Smt. Indira Gandhi College of Engineering, Mumbai. I am very enthusiastic about Data Science, Data Analytics, and Machine Learning.

You can connect with me on LinkedIn. Feel free to check me out and connect with me.

Your suggestions and doubts are welcomed here in the comment section. Thank you for reading my article!

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

 

S
Shivam

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