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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

How to choose the right data science / analytics / big data training?

Kunal Jain
Kunal Jain Last Updated : 16 Sep, 2017
4 min read

Over the last 2 years, this is the most common query I receive from our readers:

Which data science / analytics training should I go for?

The query comes in varied shapes and size, but the inherent question is still the same.

 

I can empathize with people facing these questions – the number of tools, analytical techniques under application and trainings provider, all have increased many-fold in last few years. If the trends and projections are to be believed, this is probably just the start of a growth phase.

Let’s take an example, as a person switching from software industry, do you learn SAS or do you learn R? Or should you learn Big Data tools and techniques? How about machine learning? Data Visualization tools? Even if you zero in on one of these, the next question which arises is where and how to undergo these trainings?

best big data science trainings

I am sure most of the person in this situation feel like the person in the image above. This is where a framework can help you.

 

Framework to choose right analytics training:

I aim to provide a framework to you to decide:

  • Which tool to learn?
  • Which techniques to focus on?
  • How to learn?
  • Where to learn?

You can apply it at various stages of your analytics career to find out what should you be learning next.

 

Overview of the framework:

The answer to first 2 questions in this framework are in form of levels or steps. You start from level 0 and move one step at a time. So if you are a complete fresher start from Level 0 of tools and level 0 of techniques. But, if you are a fresher with statistics background, start with Level 1 of tools (assuming you know Excel) and Level 1 of techniques (move to level 2 if you know predictive modeling)

Once you have finalized the tools and techniques to learn, move on to step 3 and step 4 of the process.

 

Step 1: Which tool to learn?

Level 0: Excel.

If you don’t know excel, you should learn it first. You should be able to play with Pivot tables, do simple data manipulations and apply lookups in Excel.

Level 1: SAS / R / Python

This is going to be your work horse. You can choose any of these languages. For a more detailed comparison, have a look at this article.

Level 2: QlikView / Tableau / D3.js

You should add up your repository with one of the visualization tools.

Level 3: Big Data tools

This in itself can be multiple levels – start with Hadoop stack – HDFS, HBase, Pig, Hive, Spark

Level 4: NoSQL Databases

Again, you can read an overview of NoSQL databases here and start by learning the most popular one – MongoDB.

Exception 1: If you come from MIS / reporting background, you can start from learning visualization tools like QlikView and Tableau (Level 2) and then go to Level 1

Exception 2: If you come from software engineering / web development and know one of the 2 languages – Java or Python, you can start from Big Data tools as well (level 3)

 

Step 2: Which techniques should you be learning?

Now that you know, which tool would you want to learn, let us look at the techniques to learn. Again the structure is similar

Level 0: Basics of statistics – Descriptive and Inferential statistics

Level 1: Basic predictive modeling – ANOVA, Regression, Decision trees, Time Series

Level 2: All other remaining machine learning techniques except Neural nets

Level 3: Neural nets and deep learning

 

Step 3: How should you learn?

How should you learn is dependent on 2 factors:

  • Resources you can spend on learning; and
  • Your self learning motivation.

This image explains the selection:

how_to_learn2

On one extreme, you have option to join open courses – where you spend low (almost zero) resources, but need high self learning motivation. On the other hand, you have courses run by big universities like Stanford / MIT / North Western, where you will need to spend money and will get help and mentor-ship from experts over longer duration. You can choose the style of your learning depending on where you fit in.

Please note that irrespective of which method and blend you choose, you will need to aid these trainings by hands on projects and practice. No resources or trainings can cover that for you. Here are a few examples of these projects.

For people relying completely on self learning, our learning paths can be of great help. There is one for Python, SAS, Weka and Qlikview each and several more under development.

 

Step 4: Where to learn?

Now that you know, what to learn and how to learn, you can shortlist various options available. You should talk to people who have undergone that training / course and gather some reviews. You can also use our training listing page and apply filters to shortlist the trainings available for various tools and techniques. We have more than 300 trainings listed here and are in process of adding more trainings and courses.

 

End Notes:

So, there you go! You should have a way to find out your way through this data science course juggle. Hope you find this framework immensely useful. I have tried to put a framework to the most common query I get from our audience. The idea is to enable you to make the right decision to the extent possible. If you think, you are in a situation which doesn’t get addressed by the framework above, please feel free to ask those questions through comments / discussion portal.

P.S. These are my views. A lot of these recommendations are based on my experience and what I think is the right choice. As you can expect, some of these questions don’t have a right or wrong answer. They are subjective in nature. So, if you have a different opinion about something I have mentioned, please feel free to let me know.

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.

analytics careeranalytics coursesAnalytics trainingcareer advicecareer pathcareer shiftdata science coursesdata science training
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

Suravi Kalita
Suravi Kalita

Nicely written article.

123

Darshana
Darshana

Dear Kunal, Thanks a lot for sharing very interesting insights about choosing the right program for analytics and big data. :) Regards, Darshana

123

Ruthger
Ruthger

Hi Kunal, Very nice and clear article! What I actually missed were basic Unix Shell programming skills. It can be extremely useful to know how to use commands like grep, awk and sed etc to perform essential data cleaning and pre-processing of the data before bringing these data as for ex. a .csv file into Excel or R. Could you expand a bit on what you feel are the advantages of QlikView / Tableau / D3.js beyond for example making the graphics in R? All the best! Ruthger

123

Kunal Jain
Kunal Jain

There are 2 advantages where I think a data visualization tool can come very handy: 1. Understanding and exploration of Huge Data - For example, while working on Avazu CTR Kaggle problem, we were working on 7GB data with anonymized columns. It was becoming time consuming to load this data in R and perform exploratory analysis. With QlikView, we could load the entire data in less than 5 minutes and then perform exploratory analysis very quickly. What helps is quick slice and dice and drill throughs available. So you can quickly identify high and low value population and segregate them in your modeling in R 2. The second application is in finally delivering your insights to the customers. Once your analysis is complete, you can use story-telling feature of these visualization tools to present your findings. You can bookmark the graphs and access them quickly on the go. If people want to explore additional information - it is typically far more easier to do so rather than opening RStudio and then writing / running the codes. Hope this helps you answer the question. Regards, Kunal

123

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