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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

Do you need a Certification to become a Data Scientist? 5 Things you Should Consider

Ram Dewani
Ram Dewani Last Updated : 18 Nov, 2020
7 min read

Overview

  • Data science certifications are ubiquitous – should you get one? If yes, which certification should you choose?
  • Here, we list down the different aspects of a data science certification you should consider before making your career choice

 

Introduction

Should I get a certification in data science? It’s the most mind-boggling question every aspiring data scientist inevitably faces.

And this is followed by more questions – Will it add more weightage to my resume or will it help get me employed? This, as I’m sure a lot of you reading will be aware, ultimately leads to more stress and confusion.

Data Science Certification

There are definitely some advantages that come along with a data science certification. It reflects your interest and passion in the field of data science but there’s a caveat – due to the boom of data science, there has been a massive uptake of these courses which makes them common or general.

So, what can you do in this situation?

To stand apart from the crowd, you will need to take up a course that provides you with industry exposure and high-quality projects. A certification is taken as a standard to measure great talent.

BlackBelt + is one such course that will provide with you each and everything you will need to become a highly valuable professional in the data science industry. It isn’t just about certification, it is about the quality and guidance that comes with it.

In this article, we will dissect the question into multiple components and help you get the answer to this all-consuming question!

 

Do I want to become a Data Scientist?

Certainly, Data Science has become a glamorous role over the years, and especially when HBR termed the role of the data scientist as the sexiest job of the 21st century. Today, the market size of data science stands at $38 billion and is expected to reach $140 billion by 2025! It is undeniably a high growth role.

Data Science Certification

The majority of enthusiasts aim to become a data scientist because of the glamour and high salary band but this is hardly the reality anymore. Many people apply for the same job role but don’t have the right skill set for the job resulting in a crunch of high expectations.

In the initial years of my career, I wanted to solve data-driven problems and so I aimed to become a data scientist. Little did I know that there are many other data-based roles available in the industry like a Business analyst, data engineer, and many more!

For example, if you come from the software engineering industry then data engineering may be the right role for you or if you want a job that has a high number of openings then you may want to go with Business analytics as a career choice.

The big question we must ask ourselves – Why do I want to become a data scientist? If you want to know about other data-based roles, you can check out this article:

What are the different paths to become a data scientist?

There is no limitation when it comes to learning especially in the digital age where you have an ample amount of options open! You can browse through millions of freely available resources or even pay a hefty amount for a shining course. It all depends on how you want to do it. Let us discuss some different resources and their pros and cons.

Data Science Certification - Path

  • Reading Blogs – Blogs are widely and freely available resources on the internet. The greatest benefit of blogs is that there is a lot of variety in it and they are of course easy to grasp. You don’t need to go chapter-wise to reach a specific topic. The disadvantage is when you are a beginner, it becomes hard to connect the dots with the overall topic leading to gaps in knowledge. Analytics Vidhya has provides a range of articles on various topics. Make sure you go through the blog section. 🙂
  • Learning through video tutorials – Can’t learn through reading? Videos tutorials are a great option for you. It is always better to see someone implement the concept, code, or project in front of you and then repeat after them. Video tutorials have the same disadvantage as mentioned in the above point.
  • Free Courses – Yes, Free Courses are available for data science! These are usually short introductory courses which you can explore as a beginner. Many of them also provide certification as well. The advantage of such courses is that you get the comprehensive learning path for the intended concept. The disadvantage is that these are not specialized programs and only consist of generic knowledge. You can get access to a variety of free courses at Analytics Vidhya. (P.S Some of them even have free certification)
  • Certification courses – Duh! Certification courses offer a great way to learn data science. You get a complete curriculum and reach the goal in a structured approach. These are usually taught by industry experts with high-quality content. There is no specific disadvantage of the program the only one being – you need to choose the certification course wisely. You can get access to Analytics Vidhya’s all the courses in the BlackBelt + program

Starting a data science career without proper guidance and planning can be confusing. We have compiled a clear-cut free roadmap guide to building a career in Data Science that is curated by expert curators at Analytics Vidhya – 

A Gist about Certification Programs in Data Science

Data Science certifications come in a variety of forms. Some require an internet connection and some require physical presence. Let us check out the different formats.

  • MOOCs – or Massive Open Online Courses are probably the most known certification course in Data Science including Andrew Ng’s Machine Learning. These MOOCs may be self-paced and can be started anytime. Anyone can access these courses without any formal admission process. The only problem with these courses is that they can get very expensive if you want the certificate. The courses are usually conducted by universities such as Harvard, Stanford, and others.
  • Classroom courses – These courses are in the same format as a school or university. You’ll join a batch of aspirants and taught via the pre-designed curriculum. You’ll be given assignments as homework and you can always raise your hand when in doubt. But you may find yourself in a difficult spot if you miss a few classes because these won’t be repeated just for you. Due to Covid-19, these courses are going online as live classes.
  • Online courses – As the name suggests, these courses are online. These are pre-recorded videos set according to the curriculum. You can access the videos at any point in time in whichever order you want. But you won’t be able to clear your doubts in real-time here.
  • Hybrid courses – Want the best of both worlds? Hybrid courses offer you part classroom course and part online course. You’ll get all the self-paced content at the start of the course. There will also be a limited number of classroom classes in which you can get your doubt cleared.

 

Honestly, Does it Matter?

Well, Yes and No!

In simple words, certifications definitely matter but not because of the certificate itself but because of the skills you have gained as part of the certification.

Over the decade, many data science certification courses have popped up leading to the generalization of these courses. Now anyone can just take up a certification and claim to be a data scientist but it doesn’t work like that when you apply for a job.

Recruiters receive tons of applications and they don’t have the time to skim through the certifications and then check its authenticity. Instead, they focus on the skillsets and projects completed.

In the end, it all boils down to the interview process. The interviewer will test everything that you have mentioned in your skillset. Therefore, if you choose to go ahead with a data science certification, make sure that you keep up with your classes and gain the right skills. Your certificate won’t get you the job, skills will.

 

How do I select a certification?

  • Time – The certification course duration varies from a month to over a year. The question at the end is how much time can you devote. If you are a working professional, then an online course or online course of 3 months can suffice for basic machine learning. If you want to specialize then you may want to commit a longer duration of time.
  • Skills taught – Python, R, or SQL, Excel? When selecting the course, the first decision is probably to select the language and other skills you want to have and then match it to the course curriculum.
  • Mentors – A Data Science course without a mentor is riding the bicycle in the dark. You really don’t know where you are heading so make sure you get a one-on-one session with a mentor.
  • Prerequisites – Make sure that you go through the prerequisites mentioned in the course. For any prerequisites, you can go through Analytics Vidhya’s free courses.
  • Cost – At the end of the day, we need to match our expectations with our budget. Analytics Vidhya offers plenty of courses at the lowest prices. You can check them out.

If you are a beginner in Data Science, we have just the right course for you – the Blackbelt+ program. It comes with 14+ courses, 25+ projects, and a 1:1 mentor. The course doesn’t boast about its certification but the ability to transform you into an industry-ready data science professional. Your mentor will build a customized learning path specific to your career goals. you can learn Data science holistically with Blackbelt+.

 

End Notes

To conclude, taking the decision to undergo a certification course is difficult. You risk your time and money and hope to build a new career. It is a strong bet if you choose the right course and build your skillset.

The certification in itself has no value unless the skills complement it. Always look for a course that adds value to your skills and not just the certificate.

Hope this article clears your doubts. Let me know in the comments about your thoughts on certification courses.

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.

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Responses From Readers

Clear

Vibhu
Vibhu

It is absolutely correct. I have a certification from a big institute and the fees was bomb. Its more than a year I have completed my certification and I have no job. I have also been rejected by many companies.

123

Akshat Gupta
Akshat Gupta

IS IT NECESSARY TO DO DATA SCIENCE CERTIFICATION FOR CAREER IN BUSINESS ANALYTICS?

123

Nikita Bhavsar
Nikita Bhavsar

This article was very informative and helpful. It has busted so many doubts that were in my mind regarding this field of data science.

123

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