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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

A Primer on Getting Started with Data Science for Beginners

Abhishek
Abhishek Last Updated : 08 Dec, 2020
7 min read

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

Introduction

meme

“Data Scientist: the sexiest job of the 21st Century”.

“Data Science will become USD 178 billion markets by 2025.” 

“Companies are investing hugely in data science.”

After completing my engineering and starting my job I was continuously been bombarded with these statements on the internet. I was puzzled and like Lord Buddha wanted to know life’s truth, I also wanted to clarify my doubts. For seeking answers, I searched the internet, approached many people in/out of this domain.

In this article, I have compiled how I decided on starting my journey into Data Science.  We will discuss how to build your digital profile and how to learn and retain Data Science Concepts. Are you confused about whether to start with Data Science or not? Don’t worry I will share with you my tips and tricks which will clarify all your doubts.

 

Is Data Science real or just old wine in a new bottle?

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value.

 

2nd meme

Data Science was always present between us. Excel, SQL, Statistics are the early age tools of Data Science. This doesn’t make this field obsolete. Data Science will always amaze us with new and updated magic tricks. Earlier we used to feed data into an excel sheet and then plot graphs nowadays data is automatically stored and graphs are automatically plotted with advanced visualization tools. With the advancement, Data Science has given us many Buzzwords like Machine Learning, Deep Learning, AIOps, etc… and it will continue to do so.

There’s nothing like one size fits all

Data Science beginner - Size

To be honest there is no definite path and there should not be one also. The real essence of data science is people from different backgrounds and technicalities working together. From whatever journey I have covered, I can just give you course names and tell you to complete such courses and do such projects but this will feel like burdening you. You will be in a rat race of completing the courses and projects and eventually get exhausted. I am an electronics engineer whose final year project was Facial recognition smart door using Raspberry Pi, whose interest made him learn Machine Learning and Deep Learning. I also didn’t follow a specific path, I always went with what I liked and kept me awake at night.

 

Learning Data Science: Yes or NO?

Data Science beginner- yes or no

Many of you might be thinking that whether a non-CS/IT person can learn Data Science or not. The answer is yes. A non-CS/IT can learn Data Science and it is not a compulsion for CS/IT to learn Data Science.

One does not need to start DS, ML, or AI due to societal pressure. You may feel left out if you are not extensively preparing yourself for the AI wrath. If you are good at what you are doing and love what you are currently doing then it is totally awesome to just keep doing what you are doing and keep yourself updated by reading news and blogs.

Like biodiversity is needed to balance the ecosystem, similarly, tech diversity is needed for a prosperous community. We will always need Mechanical Engineers, Electricals, Artists, Web Developers, App Developers, Content Creators, Doctors, Film Makers, CA’s, and Athletes, etc.

 

How deep should you dive into Data Science?

Data Science beginner -deep dive

In Data Science there is something for everyone. So don’t have many fears and start training your brain model with it. The input features of this are your current domain, programming interest, curiosity, enthusiasm, passion, and hard work.

So let’s look into some domains which are associated with Data Science which one can pursue:

  • Data Visualization: If you are creative and love statistics you can learn data Visualization tools and become a Data Viz Engineer.
  • Data Engineering and Data Warehousing: It deals with storing and querying the data for future analysis. Maintaining data is as important as making predictions. A good prediction model requires good quality data.
  • Cloud and Distributed Computing: If you are an expert in IT networking then familiarize yourself with the data science project life cycle and you can design and deploy Data Science models for easy access.
  • Business Intelligence and Strategy: If you are a domain expert then you are the backbone of the whole data science project. A BI Strategist is responsible for managing dashboards, reporting to stakeholders, testing and validating models, and documenting.
  • IoT Developers: If you are into hardware and fond of building circuits and controllers then you can play the part of gathering the data using sensors and making it ready for real-time analysis or storing.
  • Computer Vision: If you love image processing then you can apply deep learning concepts and work on automating processes and building object detection models.
  • ML Engineer: Machine learning engineers feed data into models defined by data scientists. They’re also responsible for taking theoretical data science models and helping scale them out to production-level models that can handle terabytes of real-time data.
  • NLP Engineers: NLP Engineer responsibilities include transforming natural language data into useful features using NLP techniques to feed classification algorithms.

In the future, there will be many more new job profiles come into the picture the only thing which will keep you job-ready is continuous learning.

 

So what’s my guide on Machine Learning

I enrolled in several self-paced courses. I did not restrict myself with the videos and assignments given by the courses rather after some time I used to read the topic name and started learning from research papers, internet searches, books, and other sources. You can also select any course which feels right and fit in your pocket. See how the content is delivered and what are the other services offered. I will share with you my roadmap on how I prepared. I won’t bound you with the rigid timeline, you can also follow it at your own pace.

 

ml meme

Mathematics and Statistics

Learn the Basics of Statistics and brush up on your school and College Mathematics.

  1. Derivative and Function minimization
  2. Vector and Matrices
  3. Probability Distribution
  4. Random Variables
  5. Normal Distribution
  6. Z-Score
  7. Hypothesis Testing
  8. Z-Test and T-Test
  9. Chi-squared Test
  10. ANOVA Test
  11. F-Stats

Just the basic knowledge is enough of the above topics.

Basic Programming

You can select any language of your choice. I chose Python. Some fundamental concepts to know:

  1. Data Types
  2. Conditions
  3. Loops
  4. Functions
  5. Object-Oriented Programming
  6. Exception Handling

Don’t worry if you are not comfortable with these concepts in the beginning. After ample practice, you will be confident.

Machine Learning

Understand how different algorithms work and how to implement it:

  1. Linear Regression
  2. Logistic Regression
  3. Decision Tree
  4. Random Forest
  5. Ensemble, Bagging, Boosting
  6. XGBoost
  7. Naive Bayes
  8. KNN
  9. K-Means Clustering
  10. Hierarchical Clustering
  11. DBSCAN
  12. Principal Component Analysis
  13. Support Vector Machine
  14. Time Series and Anomaly Detection

For experienced professionals, one can start with advanced excel, other data visualization tools like PowerBI and Tableau. You can get many platforms where you can perform ML predictions without knowledge of coding.

If you are a fresher then start learning to program and complete a few courses on ML. Experiment a lot and keep working hard. Word of motivation for fresher: “If ML is your passion then be like Batman: Do your office work in the day and follow your passion at night.

If you are in college attend conferences, workshops, tech fests, complete courses participate in hackathons, meet as many people as you can, and most importantly enjoy the process.

Retaining what you learn

Now that you are into Learning let me share with you some tips on how to retain what you learn:

  • Hands-on implementation of the project. Start with a basic project and make it large. Integrate it with apps, deploy on cloud platforms, etc.
  • Feynmann Learning Technique:
    • Choose a concept you want to learn about
    • Pretend you are teaching it to a student in grade 6
    • Identify gaps in your explanation; Go back to the source material, to better understand it.
    • Review and simplify
  • You can record videos of you explaining concepts and upload it on YouTube. In the end, you will have your own video notes to refer.
social media notes

Not only YouTube you can choose any social media like I keep my digital notes on Instagram and Facebook

https://www.instagram.com/pandaspython/

https://www.facebook.com/pandaspython08

Building your digital profile will not only help you in retaining things but it will also help you a lot in building connections with others in the industry. You can showcase your work, collaborate with others, and work on projects. This practice will develop communication and overall personality skill which many people lack.

Add each and every bit of code you do on Github and write a nice readme.md about it. Start Blogging or create a website to showcase what you are doing ( Google sites is also enough). Just digitally record each and everything you do. Blogger, Medium Facebook Page you can write anywhere you want.

 

End Notes

So this was my article discussing what data science is and up to what extent one should learn. I have also shown how to learn in an effective way. If you have any doubts and want suggestions on which books or blogs to follow then ask me in the comment section. Lastly, I would like to say that enjoy the process rather than bounding yourselves to courses.

Abhishek
Abhishek

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