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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

8 rules for new age analytics learning!

Kunal Jain
Kunal Jain Last Updated : 17 Apr, 2015
4 min read

Data science has become one of the most dynamic field. Every alternate month I hear about a start up coming up with next gen tools and products. Cloudera, Neo4j, MongoDB, Ayasdi are some of the companies, which are showing us this exciting future.

With this pace of development, your learning strategy needs to change accordingly. You can no longer learn a tool and be good for 2 – 3 years. You need to learn continuously.

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I lay out 8 rules of new age analytics learning in this post. If you are serious about your future in analytics, you should keep them in your heart:

 

Rule 1: Open Source tools are in

Open source tools are growing in their presence by the day, for the right reasons. They are cost effective, have big communities to support and have faster pace of development. They are also the tools available to a scientist (or a freelancer) working on his own.

You can read a more detailed comparison of these tools against SAS here. Except for people just entering analytics industry (with primary concern as jobs), learning R (or Python) is almost mandatory to future-proof yourself.

So, if you want a long term career in analytics, learn one of these now!

 

Rule 2: Democratization and free trials of tools will become the norm

Free trials of tools in individual capacity will become the norm (if it is not already). What I mean is, that more and more companies will offer a basic version of their tools for free. For example, Qlikview offers personal edition as a free download, but you need to buy licenses if you want to share you dashboards. Big query (from Google) offers querying till a particular data size for free.

How does this matter?

Well, you get access to any tool you want as a starter. You can test out tools before purchasing / deploying them. Also, you can accelerate your learning through downloading and experimenting through these tools.

 

Rule 3: Deep learning in at least one subject will propel your career 

Especially so, in early part of your career. In order to distinguish yourself, you will need specialization in at least one area. If you are a Business Intelligence professional, you need to understand the entire spectrum of tools available, their pros and cons. Same with Big data experts and data scientists. The pace of change doesn’t allow you to specialize in all these subjects.

You are free to pick what you prefer, as each of the specialization offer good career aspects.

 

Rule 4: However, for leadership positions, you will need broader perspective

Leaders will be expected to know what is happening across spectrum and how can that benefit the organizations. So, at some point, when you have deep knowledge in one of the subject areas, you need to pay equal attention to other areas (at a higher level though).

Or as Srikanth Velamakkani, CEO, Fractal Analytics puts it:

srikanth_quote2

 

Rule 5: Outstanding visualizations and storytelling will differentiate best analysts from the rest

With data size increasing every second, you can no longer rely on bar-charts and pie-charts to tell your stories. New creative visualizations help deliver stories effectively and efficiently. Be it infographics, graph representations of networks or geo-spatial heat maps – all of these are far better to make an impact compared to a bar graph / table narrating the same.

 

Rule 6: Man machine co-ordination will gain in importance

Machine learning is gaining importance due to strong fundamental reasons. Be it Google driverless cars, or your smartphones trying to understand your needs or a sensor on your wrist to monitor your health regularly, all of these require man machine co-ordination. I think the career opportunities here can almost be divided in 2 categories:

  1. Data collection from various sensors and machines
  2. Analyzing stream of data to come up with insights and personalized experiences

 

Rule 7: Data Science competitions are opportunities to learn and showcase your talent

I love these competitions and hope that you do too. Sadly, I don’t get as much time for these as I would want. But these are ideal platforms to learn along side your peer. Look at the kind of discussions happening on various competitions on Kaggle and you will understand what I am talking about, You learn immensely through these competitions.

As a side benefit, they can become your hiring platforms.

 

Rule 8: Last but not the least, learn continuously

This goes without saying! The more you learn, the better it is. What is important is to learn regularly and continuously. Be it courses on Coursera, Youtube tutorials, blogs or iPython notebooks on github, just spend time to learn on a regular basis.

Each of these rules is something I believe in. Internalizing them will help you become clearer on your learning journey. What are your thoughts on these? Do you have any other rules / perspective to add? Kindly do so, through the comments below.

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.

photo credit: giulia.forsythe via photopin cc

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

AKveni
AKveni

I have done my big analytics course, but do not have any experience in software field and my background is BA. I have done my higher diploma in software engineering and learnt SAS, but no experience. Now I want to join as a R programmer as a trainee, where could I get a chance?

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Kunal Jain
Kunal Jain

AKveni, People will be more open to take you as a trainee if they see some efforts you have put in. Start going through courses on Coursera and apply for jobs in parallel. Thanks, Kunal

123
Nitesh
Nitesh

Hi Kunal , Thanks for writing this wonderful Blog .I regularly follow all the blogs @ anayticsvidhya .It really helped me a lot when i was in dilemma that i should enter this industry or not .Now i have decided and learned SAS , going through R and at the same time also learning at cousera (DATA SCIENCE ) .As you mentioned kaggle i am also following all the events kaggle this is a great platform of learning and also every one can interact with global community . I am from engineering background and two plus yrs of experience but not in analytics industry .So,how much all these efforts will help me to lend in analytics industry . what else i should do to enter in this area. Thanks, Nitesh

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Kunal Jain
Kunal Jain

Nitesh, Good job on the initiatives you have taken to enter the industry. You are on right track and I would urge you to stick to these. Quality of learning would matter more than quantity of learning. So, if you are participating in a competition on Kaggle, look at what other people are doing and then compare it against what you are doing. Wish you all the best for a bright career! Thanks, Kunal

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vaishalee
vaishalee

nice blog. started reading it today n finding it very usefull. I would like to know the difference between statistical programmers required skillset and business analyst skillset. thanks

123

Kunal Jain
Kunal Jain

Vaishalee, Can you be a bit more specific about these roles? In case you have a job description, please feel free to point at them. These terms are used loosely in the industry and hence I can not provide a standard answer without knowing the context. Thanks, Kunal

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