Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

How can you Master Data Science without a Degree in 2020?

Ram Dewani
Ram Dewani Last Updated : 30 Nov, 2020
6 min read

Introduction

Becoming a data scientist has become like the “American Dream” – everybody wants to have it!

However, for all the beginners out there – a big question remains unanswered – Do I need to have a degree to become a successful Data Scientist?

Data Science is a relatively newer field, even the top-notch universities have started offering specialized courses only recently, which has created a sudden buzz and confusion in the industry. In this article, we are going to break down the question and look at all its aspect.

If you are a beginner, starting out on your data science journey, we have compiled a clear-cut free roadmap guide to building a career in Data Science that is created by expert curators at Analytics Vidhya – 

 

Can I get a job without a Data Science Degree?

The straight answer is YES! Don’t get me wrong here, a degree has its own Pros and Cons which we will discuss in detail in the article, but you can definitely get a job on the basis of your skills and projects.

Have you ever gone to an interview and the interviewer instantly gave you the job because you had the “degree”? NO! He’ll test the depth of knowledge and only then offer you the job. Therefore a degree might land you the “opportunity” of the job but “getting” the job takes skills and effort!

 

Pros and Cons of a Data Science Degree

Pros

  • Access to Expert Faculty – The universities and colleges have professors and experts on each of the subjects. There is no doubt that these programs provide you access to some of the great minds especially if you are looking forward to getting a Ph.D.
  • One on one sessions – Learning is always better when you get to interact with the experts and mentor one-on-one. This helps in clearing doubts, getting advice and the overall experience is much better.
  • Clearly defined curriculum – The curriculum of a degree course is well defined with clear industry standards. These are comprehensive and give a holistic view of data science.
  • More accessible to recruiters – Universities and Colleges have tie-ups with organizations and hence there are ample opportunities to land a coveted job directly after the degree.

Cons

  • The high cost of course – Data Science is in great demand but this comes at a huge cost. You may have to loosen your pockets by Lakhs of rupees. Moreover, a good degree from a European or an American University may be much higher and may cost you in 7 figure rupees.
  • Time is taken to complete the course – If you are already a working professional, it will be hard for you to give up a paying job and commit yourself for 1-2 years without an incoming salary.
  • The selection process usually prefers people from technical and statistics field – If you are coming from a non-technical or a non-stats background, you’ll probably not get the priority. The selection process and the interview process of the majority of universities are designed in a way that technical people have a higher advantage.

Alternatives to getting a degree

Now getting a degree is not the only way to land the title of  “The Sexiest Job of the 21st century”. There are a couple of other alternatives. It will definitely take some time and effort but it will be worth it. So let’s see –

Take up a comprehensive Certification Course

Certification courses are a great way to learn Data Science from scratch and mastering the field. These courses typically range from 6-12 months and offered at a much less price. Although you will need to keep a few things in mind before buying the course –

  1. Make sure the course is comprehensive
  2. The course instructors must be experts
  3. The course must be up-to-date
  4. It should offer plenty of real-life projects.
  5. A one-on-one mentorship would be great.
  6. Job assistance along with Resume building and interview preparation.

Sadly, there are tons of courses out there which charge a premium but do not offer all the features mentioned above. But there’s one course that provides all and helps you become an industry-ready data science professional.

The AI and ML Blackbelt+ course offers –

  1. Mastery in 15+ Tools
  2. Expertise in Data Science, Machine Learning & Deep Learning Subjects
  3. Ability to solve real-world industry problems
  4. 1:1 Mentorships with Industry Practitioners
  5. Comprehensive & Personalised Learning Path
  6. Dedicated Interview Preparation & Support

Build knowledge via free course

Free Courses are a great way to build upon your knowledge in the initial phase of your journey. These courses offer a great introduction to data science concepts. But beware, these courses are for beginners and if you have mastery over a few subjects I would recommend you to move on to specialized courses. Let’s look at the list of a few important free courses –

  1. Introduction to AI and ML – The perfect course to understand and navigate the Artificial intelligence and Machine Learning industry. It mentions all the skills, tools, career path to become an AI and ML professional.
  2. Python for Data Science – Python is one of the most powerful and most widely used languages to build machine learning models. This course is great for Python beginners and also provides free certification!
  3. Introduction to Natural Language Processing – If you are an NLP enthusiast, this is the perfect course for you. You will get to learn the basics of Natural Language Processing, Regular Expressions & text sentiment analysis using machine learning in this course.
  4. Getting Started with Neural Networks – Deep Learning has picked over the last decade and many enthusiasts are interested in learning neural networks. The course answers questions like – What is a neural network? How does it work? What does a neural network do?

 

A few Must have things

Participate in competitions – Data Science competitions are a sure shot way to build and prove your skills and get noticed by potential recruiters..  You can go on the DataHack platform and pick a problem statement of your choice and get started. Recruiters love the candidates who have built their knowledge through practical applications.

Master Data science Without Degree - Hackathon

Start writing articles – If you have a knack for data science and a passion for writing then what is a better way to express yourself than writing articles? Article writing helps you learn all the hard technical concepts and turn them into easy-to-grasp topics. Article writing is another great way to help you catch the eyes of potential recruiters.

Master Data Science without Degree Blogathon

Start from internships – The problem with getting a full-time job in data science is “required experience”. The best way to get that experience is through internships. Although you may have to start from the beginner of the ladder even though you have experience in some other field but it’s definitely worth it.

 

End Notes

In order to get into data science, you must not make a degree an excuse for building a successful career. In this article, we discussed the Pros and Cons of having a data science degree and alternatives to a full-time degree.

One of the best alternatives to undergoing a comprehensive program that provides you with beginner-to-advanced courses along with tons of real-life projects. The AI and ML Blackbelt+ program provides you with 15+ courses and 39+ real-life projects. What’s the best part? You get 1:1 mentorship sessions so that you get a customized learning path that is customized just for you and you never get off track!

Ram Dewani
Ram Dewani

Product Growth Analyst at Analytics Vidhya. I'm always curious to deep dive into data, process it, polish it so as to create value. My interest lies in the field of marketing analytics.

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Debanuj
Debanuj

Good

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details