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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

Course Review – Big data and Hadoop Developer Certification Course by Simplilearn

Kunal Jain
Kunal Jain Last Updated : 25 Mar, 2016
5 min read

Introduction

There is no question that the Big Data revolution sweeping through the world of business has made its impact on companies big and small. Multinational giants and startups alike are going out of their way to incorporate Big Data capabilities, including the likes of Google, Amazon, Airbnb, Uber, Facebook, Tesla, and more.

The Big Data boom has resulted in a surge in the demand for skilled Big Data professionals not just in India – but around the world. In particular, the insurance, FMCG, online marketing, and e-commerce sectors -which deal with huge volumes of data on a regular basis – are facing a shortage of data specialists.

And according to a report by McKinsey Global, the demand-supply gap is acute, with an estimated 1.5 million Big Data managers needed by 2018!

Simplilearn’s Big Data and Hadoop Developer Certification Training offers you an opportunity to start your dream career in big data from scratch.

Not too long ago, I had a chance to chat with Indranil Banerjee, Associate Director, Simplilearn. I learned about their motives for launching this course and how they are trying to transform big data industry in India. I met their course instructors and learned about their methodology of teaching this course.

Recently I came to know that many students are contemplating taking this course and looking for further information on the same. To help them make a more informed choice, I’ve reviewed the course and offered my input.

Read on to find out more about this popular course and why it’s been the starting point for thousands of successful Big Data professionals the world over.

simplilearn analytics vidhya

 

 

What is the objective of this course ?

The main objective of this course is to develop skilled professionals in various big data technologies like Hadoop, Apache Spark, Mapreduce, Cassandra and many more. The course aims to provide comprehensive knowledge of Big Data tools to enable professionals at any level tackle data assignments with ease. The mandatory real-life industry projects that are included with the course ensure participants get adequate hands-on experience.

Needless to say, all this will contribute to one’s chances of employability in big data industry.

 

What is the Course Structure ?

This course is available in two formats: Self-paced, and Instructor-led \ Online Classroom. The online classroom instructor led program is offered via Flexipass. This feature allows access to multiple batches of training, across timezones for a period of 90 days. Both formats provide high quality e-learning content for 180 days to enable self paced training.

In both cases, the self-contained pedagogy used in this course primarily benefits students with no prior knowledge of big data. For instance, big data requires knowledge of Java. But even if you have no formal training in Java, the course offers a module in Java to get you started.

This course follows a basic to advanced level teaching structure. In addition to other big data tools, this course offers a dedicated module on Apache Spark (widely considered to be the future of Big Data). This course is thus up-to-date and is in-line with the latest industry trends and developments. The course offers training in Hadoop 2.7, MapReduce, Pig, Hive, HBase, Zookeeper, Sqoop, Flume and predictive modeling in R (additional course). Check out the complete course listing here.

For an enhanced learning experience, it offers a unique CloudLab feature. This feature gives the user the experience of working on a state of the art hadoop cluster with no maintenance issues. Also, the inclusion of industry projects across retail, insurance, social media, offers the much-needed practical experience of working on Big Data. A candidate will receive a 3 months experience certificate after the successful completion of this course.

However, amidst such exemplary offerings, moving with the currents trends, I understand that they are adding Python/R in the course as well. Use of these analytic tools with Big Data will further strengthen their course, which is already among the best available today.

 

What’s the Faculty like ?

The training faculty that host the sessions are fairly experienced and aware of the latest industry developments. I found no instances where the course instructor struggled to answer questions adequately

I noticed that the instructors laid more emphasis on explaining technical topics with illustrations from real-world scenarios. This helps participants relate theory with practice and absorb dense technical details easily.

Needles to say, if you opt for self paced classes, you’d miss out on this experience. However, the choice of course is completely left to your discretion.

 

How good is the content ?

The pre-recorded video tutorials have nice and clear video & voice clarity. The instructor’s accent is easy to understand and interpret. Difficult topics are explained using interesting real life examples.

The upcoming version of the course will provide upgraded content based on the new Cloudera certification. They will also feature motion graphics and an on-screen instructor. I think that will be an exciting change as I always feel that on-screen instructor provides higher engagement.

This will help increase concentration and make learning more visualized compared to voice explaining slide mode.

 

Who should take this course ?

This course is broadly suited for candidates of all domains who are keen to pursue big data as a career. The inclusion of Java module has made it even more appealing for non-technical candidates as well.

However, as per my understanding, prior knowledge of java in this course should be a major advantage. The additional java module can be used as a refresher (if required). Candidates from IT background would find this course easier to relate and understand. On the other hand, a newbie with some diligence and enough practice should be able to discern the terminologies used in different stages of this course.

Hence, I would thus suggest that this course is best suited for experienced (<2 years) IT professionals. But that doesn’t mean a newbie cannot benefit from the training. You might need to work harder than others, but if you truly aspire to become a big data scientist, I don’t see it being impossible.

 

Past Students Experience

Professionals who have taken the Big Data certification training in the past seem quite satisfied. Many have landed their first big gig in Big Data as a direct result of the course. Here are a few reviews I have picked from their website.

Deepak Priyadarshi (Founder and CEO at Filament Factory) – a student of this course : It was an amazing experience to train on BIG DATA with Simplilearn. This course is very feasible to the beginners and the course contents are downloadable on the go.

Devendra Sahu (Project Manager at Wipro) – This program is very well structured and planned to help anyone who want to understand Big Data & Hadoop concepts. I recommend it to everyone who wants to get into the Hadoop ecosystem.

 

Overall Recommendation

My final verdict is: I recommend this course to all aspiring big data candidates. This is a good starting point for your journey. Apart from the quality of the offerings, which is exceptional, the price point of the course also makes it value-for-money material.

The self paced module is available for just Rs.10,999. The instructor led / online classroom module can be availed at Rs. 21,999. After looking around a bit, I’ve come to realize that it’s difficult to find a course in this price range, considering the immense array of offerings being provided.

Also, the CloudLab feature offered saves a lot of time spent in installation and configuration. This virtual hadoop environment provides an accelerated learning experience. The inclusion of industry projects across sectors make for an much needed practical experience on your resume.

 

End Notes

I hope this review would help you in deciding on your first or next big data course. I understand there are many courses available on big data and each has its own “value proposition”, Simplilearn’s course is a great place to begin.

If you have any questions about the program, I’ll be happy to share my views & perspective. If I don’t have the answer myself, I’ll reach out to people who do.

You can test your skills and knowledge. Check out Live Competitions and compete with best Data Scientists from all over the world.

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

mahesh
mahesh

Sir I have registered for simplilearn hadoop developer course. But as of now I have freezed my course. When is the course going to be updated . What is the best learning path for an data scientist job.

123

Dhananjay Yadav
Dhananjay Yadav

To become well-versed with The Sexiest Job ( Data Scientist) as per Harvard Business Review, you can start with Data Scientist with SAS and Excel followed by Data Scientist with R- language. In current scenario, SAS and R courses are a necessity for landing an Analytics Job at any well renowned Organisation. Simplilearn's Data Scientist Programs offers you all of it.

123
ankush
ankush

This is very helpful, was planning to do Big data training.

123

Sandeep
Sandeep

Hi Kunal, I am a Analytics professional with exposure to SAS/R. Now I want to learn Big Data as well, can you please let me know, which course among the below two is better 1) SimpliLearn 2) Course offered by Jigsaw Academy. Request you to please provide your recommendation Regards Sandeep

123

Dhananjay Yadav
Dhananjay Yadav

My personal suggestion would be to go with Simplilearn's Big Data & Hadoop Developer course. Since, not only is it a cheaper option, you also get added benefit Cloud Labs to perform your practical understanding on the platform.

123

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