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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

10 Resources to Successfully navigate a career in Data Science!

Ram Dewani
Ram Dewani Last Updated : 08 Dec, 2020
5 min read

Overview

  • Here are 10 Data Science resources that answer the majority of career-related questions beginners have
  • By no means is this list exhaustive. Feel free to share other resources in the comments below

Introduction

The scope within the Data Science industry is diversifying and expanding with each second. The sheer volume of data produced is colossal, thus increasing the demand and work of the data scientists.

Furthermore, with each passing day, there are new algorithms to solve a problem, new tools, new libraries, etc which makes it only difficult to penetrate the data science industry. Hence, it only makes sense to begin your data science journey with the right resources.

data science resources

In this article, we point you towards 10 resources that answer the most fundamental questions any beginner will have while beginning his data science journey!

Table of Contents

  1. Should I become a data scientist (or a business analyst)?
  2. How To Have a Career in Data Science (Business Analytics)?
  3. 14 Must-Have Skills to Become a Data Scientist (with Resources!)
  4. 12 Essential Tips for People starting a Career in Data Science
  5. Do you need a Certification to become a Data Scientist? 5 Things you Should Consider
  6. 5 Popular Data Science Languages – Which One Should you Choose for your Career?
  7. 10 Questions Every Data Science Beginner Asks (with Answers and Resources)
  8. How can you Master Data Science without a Degree in 2020?
  9. Want to Build a Career in Data Science? Learn from these 5 Data Science Videos!
  10. 8 Thoughts on How to Transition into Data Science from Different Backgrounds

 

Should I become a data scientist (or a business analyst)?

  • framework by Kunal
  • Here are some of the questions to remove doubt

The data science buzz is really exciting and who doesn’t want to get the “Sexiest Job of the 21st century” but do you want to enter a field just for the sake of it? Let’s start off with the most basic question – Should you even become a data scientist?

Kunal Jain, the founder of Analytics Vidhya provides a structured framework on which you can rate yourself and see whether a career in data science and business analytics is for you or not.

questions

 

How To Have a Career in Data Science (Business Analytics)?

Once you are sure that Data Science or business analytics is your career choice, it is time to work on your skillset but there must be questions like – Should I go for a degree course? How to land an internship in a data science job role?

In this article, Kunal draws upon his rich industry experience and provides a step-by-step guide on becoming a data scientist. He also provides very insightful tips on entering the data science industry.

 

14 Must-Have Skills to Become a Data Scientist (with Resources!)

Due to the colossal growth in the data science industry, there has been a jump in the number of jobs. However, there’s a caveat! There is a massive shortage of skilled professionals in the data science industry. So how can you break this trend and become an industry-ready professional?

This article consists of  14 hard and soft skills like fundamentals of data science, statistics, big data, model deployment, etc, and links to amazing resources to learn from!

12 Essential Tips for People starting a Career in Data Science

Learning data science can be intimidating. Especially so, when you are just starting your journey. Which tool to learn – R or Python? What techniques to focus on? How many statistics to learn? Do I need to learn to code? These are some of the many questions you need to answer as part of your journey.

This guide is a rich resource filled with tips from an industry insider on how to navigate a career in data science from finding the right job role to resume building. You will find it all here.

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

I have come across many data science enthusiasts who are constantly confused about the pros and cons of getting a data science degree. If you have the same query then you are at the right place.

Data science certifications are ubiquitous – should you get one? If yes, which certification should you choose? What metrics do you need to look at before selecting a certification? In this article, we list down the different aspects of a data science certification you should consider before making your career choice.

5 Popular Data Science Languages – Which One Should you Choose for your Career?

I’m fairly certain all of you will have come across this eternal dilemma about choosing the “perfect” programming language to start your data science career. But there’s no one size fits all approach here. Each language has it’s own unique features and capabilities that make it work for certain data science professionals.

Some languages may be suitable for fast prototyping while others may be good at the enterprise level. So in this article, let us clear the confusion once and for all and see which is the best language that suits your data science career goals.

10 Questions Every Data Science Beginner Asks (with Answers and Resources)

Data Science is relatively a new field and along with its benefits comes a lot of obstacles! You’ll notice how a few key questions constantly keep popping up – Where to start? What to learn and how to learn? How to find the right resources for data science?

In this article, we have discussed the 10 most asked questions by data science enthusiasts and beginners. These will help you figure out different aspects of your data science career, including your resume, interview process, and other best practices!

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

As I mentioned above, Data Science is relatively a new 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. Some of the discussion points being –

  • Can you get a job without a data science degree?
  • Pros and cons of having a degree
  • Alternatives to having a data science degree.

Want to Build a Career in Data Science? Learn from these 5 Data Science Videos!

Navigating through the data science career is a tricky proposition and it can become a tough job without proper guidance but not everyone is privileged to get access to industry insiders.

Meetups are a great way to learn and connect but since Covid-19 that is not possible, Webinars are one of the best alternatives to learn and interact directly with industry experts. Some of the benefits of webinars are –

  • Get to learn from industry insiders
  • A great opportunity for networking
  • Get career advice and data science knowledge from experts

 

8 Thoughts on How to Transition into Data Science from Different Backgrounds

One of the major queries asked by people is how they should transition into data science. People from all sorts of backgrounds – IT, Sales, Finance, HR, Healthcare, etc. – all want a piece of the data science pie. So let us clear the confusion once and for all.

The 8 backgrounds we cover in this career transition article include – software engineering, finance, UX, application development, and a non-technical fresher. The author has provided links to plenty of resources and learning paths to help you start your data science journey.

End Notes

In this article, we discussed 10 articles that will help you get started on your data science journey. I hope it was fruitful and answered many of your questions.

Let us know in the comments if you have any queries.

 

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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