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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

Learning path & resources to start your data science (analytics) career today

Kunal Jain
Kunal Jain Last Updated : 31 Jan, 2017
3 min read

Marie said it correctly – the most difficult step in any process is the first step!Start your analytics career today

 

Recently, we launched a list of various analytics trainings being offered across the globe and are still adding more trainings to it to make it more comprehensive. While we get the entire page up and ready for you, I thought let me start putting down ways in which this information would be helpful to people.

What better place to start, than to help out the people who need it the most? Yes you are right…I am talking about people who want to start their journey in analytics or are in very initial stages of doing so. For the purpose of this article, I’ll also assume that you are motivated to take this journey in self learning mode. If you are not or need higher degree of assistance, you can easily go back to the listing page and substitute these resources for paid ones based on their ratings and subject matter.

These resources should make you knowledge ready for your first job in analytics industry

 

Choice of Language:.

I strongly believe that the choice of your first language should be a mainstream one! It helps you get a lot of resources and large community / tech support to fall back upon. For this reason, you should either choose R or SAS. You can also think of Python, WEKA or Matlab, but I would suggest R or SAS over them. Between R & SAS, you can not go much wrong – SAS has the biggest market share and R is catching up fast. Both offer free versions for learning, which you can download for learning the software. If you are still confused about the choice, you can read a detailed comparison here.

 

Books to read:

To understand power of analytics:

These books provide a good overview of how analytics can impact our business decisions and thought process, challenges faced in implementing data based solutions and also its limitations (the last one).

Freakonomics by Steven D. Levitt

Moneyball by Michael Lewis

Scoring points by Clive Humby and Terry Hunt

When Genius Failed by Roger Lowenstein

 

Gearing up on the subject:

The Signal and the Noise by Nate Silver

Big Data – A revolution that will transform How we live, work and think

Web Analytics 2.0 by Avinash Kaushik

 

Video based trainings:

Learning the basics:

Linear Algebra and Statistics from Khan Academy – All the basics you would need explained in awesome way! You realize how learning can be fun when you see them for the first time

Intro to Descriptive Statistics on Udacity & Inferential Statistics on Udacity – for the activity filled classes and exercises they provide.

 

For learning tools

Base SAS and Statistics course from SAS Institute – If you choice of tool is SAS

SAS Analytics U tutorials from SAS Institute (again if SAS is your choice)

Data Science Specialization from John Hopkins University on Coursera – If you want to take learning in relaxed manner (3 – 4 hours every week over a period on 9 months)

edX Analytics Edge (R) – For those who can sustain more intensive schedule (20 – 25 hours every week for 3 months)

Google Analytics certification by Google – if you want to build a career in Web Analytics

Chandoo.org for learning and refreshing Excel – it contains some nice tips and tricks.

Qlikview / Tableau Tutorial – I think you should learn one of these visualization tools, so that you can draw powerful visualizations quickly

 

Other Reference material:

SAS Analytics U – download center

SAS documentation & SUGI papers

CRAN project website for downloading R and packages

Videos from Google on R (available on YouTube)

R documentation

 

For staying up to date with the industry

Subscribe to following blogs;

 

Do you think I have missed out on any resources, which can be immensely valuable to a newbie in this industry? If so, please feel free to add them here.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Tejasvi
Tejasvi

Hey! Kunal, Thank you for compiling all the pertinent data. It is almost exhaustible for someone, like me, who is starting a career in Analytics. Looking forward to more insights from and for the industry.

123

Mayank Patel
Mayank Patel

Hi Kunal, Great articles (all over the website) Kunal. I had some queries. I am a software tester in an MNC for about 20 months and have done testing on data and reports processed and developed using MSBI (SSIS, SSRS, SharePoint services). I want to learn data analytics (as i want to do MS in MIS in future) and am researching for the same. Initially i wanted to start with R as its a programming language and i would learn from scratch but as i read your article comparing R vs Python vs SAS, i thought SAS was a good option for me to start. I browsed through the net and found that i can start with SAS Base Programmer 9.0 certification, for this SAS offers SAS Prep guide (worth 9K :-( ) but also the reviews on the net say “Its not the best but its the only source”. Questions: 1. Am i correct in starting with SAS over R considering my future ambition ? 2. Do you know any other good sources for learning for SAS ? Thanks in advance bro :-)

123

Kunal Jain
Kunal Jain

Mayank, As I said, there is no right or wrong answer between R and SAS. It is just the fit in various scenarios. SAS has far bigger market share in India, but is more expensive - so most of the new startups rely on R and not SAS. If you want base SAS certification - the free course on SAS website should besufficient. Regards, Kunal

123
Siddharth Sharma
Siddharth Sharma

Hi Kunal. Excellent Article to help the Beginners. Thanks, Siddharth Sharma

123

Gautam Anand
Gautam Anand

Hi Kunal You have started a very good effort for the beginners. Its very nice to read the articles by you and your team. I am learning a lot by visiting here. Its also good to see Sid (My senior Mr. Sidhartha Sharma) visiting here :)

123

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