A Data Science Central Community

Started Sep 22, 2014 0 Replies 0 Likes

We studied university curricula (from computer science, stats and business schools). The top contenders are linear programming, regression, clustering, Neural networks and SVM.Then we looked at the…Continue

Tags: mining, k-means, CART, kaggle, algorithms

Started Sep 11, 2014 0 Replies 0 Likes

Obviously, there are some wonderful problems solved by data scientists. However, I love the below problems which are worked on and believe they are the best · Detecting sarcasm in speech · Identify…Continue

Tags: education, guidance, case, studies, dataanalytics

Started Sep 9, 2014 0 Replies 0 Likes

But, I thought, the more helpful approach might be a plan.My goal is to create a plan where you get to the level of average industry practitionerSkills you need: Ability to take Excel/CSV data sets,…Continue

Started this discussion. Last reply by reymondmajengo Sep 4, 2014. 1 Reply 0 Likes

To correctly answer this question, you need to ask yourself what is the job that you want to aim? A data scientist can aim for three different jobs. For the lack of better words (or my lack of…Continue

Tags: career, education, bigdata, analytics, datascientist

Sai Reddy's discussion was featured### Some Machine Learning algorithms that you should always have a strong understanding of, and why?

We studied university curricula (from computer science, stats and business schools). The top contenders are linear programming, regression, clustering, Neural networks and SVM.Then we looked at the peer groups. Obviously, the top 10 algos are published once in 4 years I guess. The current list is C5.0, KNN, SVM, EM, K-means, Pagerank, CART, Naive Bayes and a few more. We also looked at competition sites like Kaggle and found the winning algos. Singular value decomposition, Restricted boltzman…See More

Sep 24, 2014

Sai Reddy posted a discussion### Some Machine Learning algorithms that you should always have a strong understanding of, and why?

We studied university curricula (from computer science, stats and business schools). The top contenders are linear programming, regression, clustering, Neural networks and SVM.Then we looked at the peer groups. Obviously, the top 10 algos are published once in 4 years I guess. The current list is C5.0, KNN, SVM, EM, K-means, Pagerank, CART, Naive Bayes and a few more. We also looked at competition sites like Kaggle and found the winning algos. Singular value decomposition, Restricted boltzman…See More

Sep 22, 2014

Sai Reddy's discussion was featured### Coolest things that have been done by statisticians, data scientists, or machine learning experts.

Obviously, there are some wonderful problems solved by data scientists. However, I love the below problems which are worked on and believe they are the best · Detecting sarcasm in speech · Identify every fraudulent medicine administration amongst hundreds of thousands of cases · Help physicians prescribe the most suitable medicine for a patient based on his insurance policy · Detect the patterns of customers in sales data that the marketing folks of the company did not know until then · Help a…See More

Sep 13, 2014

Sai Reddy posted a discussion### Coolest things that have been done by statisticians, data scientists, or machine learning experts.

Obviously, there are some wonderful problems solved by data scientists. However, I love the below problems which are worked on and believe they are the best · Detecting sarcasm in speech · Identify every fraudulent medicine administration amongst hundreds of thousands of cases · Help physicians prescribe the most suitable medicine for a patient based on his insurance policy · Detect the patterns of customers in sales data that the marketing folks of the company did not know until then · Help a…See More

Sep 11, 2014

Sai Reddy's discussion was featured### Skills you need to become a data scientist.

But, I thought, the more helpful approach might be a plan.My goal is to create a plan where you get to the level of average industry practitionerSkills you need: Ability to take Excel/CSV data sets, pre-process and visualize; Build a model and Visualize the results.Recommended steps:1. Download one data set from Kaggle/UCI or anywhere from the Internet. I am deliberately not giving a link as I want you to search through multiple sets. Create a deck of slides describing the business problem,…See More

Sep 10, 2014

Sai Reddy posted a discussion### Skills you need to become a data scientist.

But, I thought, the more helpful approach might be a plan.My goal is to create a plan where you get to the level of average industry practitionerSkills you need: Ability to take Excel/CSV data sets, pre-process and visualize; Build a model and Visualize the results.Recommended steps:1. Download one data set from Kaggle/UCI or anywhere from the Internet. I am deliberately not giving a link as I want you to search through multiple sets. Create a deck of slides describing the business problem,…See More

Sep 9, 2014

reymondmajengo replied to Sai Reddy's discussion Some software and skills that every Data Scientist should know?

"Great article, In short I can say Data Science= Statistics + Computer Science"

Sep 4, 2014

Sai Reddy posted a discussion### Some software and skills that every Data Scientist should know?

To correctly answer this question, you need to ask yourself what is the job that you want to aim? A data scientist can aim for three different jobs. For the lack of better words (or my lack of knowedge of those words!), let me classify them as1. Analysts, 2. Consultants, 3. EngineersAnalysts: These are the guys who do the same job repeatedly (statistical analysis in clinical trials, target marketing in banks etc.). In India, I see quite a few companies that get outsourced analytics also fall in…See More

Sep 1, 2014

Sai Reddy posted a discussion### New approaches for data modeling.

Very interesting question which can be answered in multiple perspectives. Techniques: If you are looking at techniques in data modelling, there are quite a few that are exploding. Deep learning, spectral methods, kernel methods, probabilistic graphical models, social networking analytics are all the latest and fastest growing areas. Business verticals: We are also seeing a lot of interest in data science applications in the entire circle of health care industries like pharmaceutical industries,…See More

Aug 28, 2014

Sai Reddy's discussion was featured### How are BSP (binary space partition) trees used in machine learning algorithms?

One of the standard approaches in machine learning is called “instant based learning (IBL)”. K-Nearest neighbors algorithm is perhaps the most known amongst these appraoches. Special cases of BSPs called K-D trees are very often used in real world engineering of K-NN.Let me get to details:IBLs require that you identify one or more nearest neighbors to a given point or a record to make a prediction. For example, if I want to figure out whether to classify a transaction as good or fraud, I search…See More

Aug 22, 2014

Sai Reddy's discussion was featured### Easiest way to learn machine learning.

There are some excellent resources here. But, I thought, the more helpful approach might be a plan and hence am adding one more answer to this list.My goal is to create a plan where you get to the level of average industry practitionerSkills you need: Ability to take Excel/CSV data sets, pre-process and visualize; Build a model and Visualize the resultsRecommended steps:1. Download one data set from Kaggle/UCI or anywhere from the Internet. I am deliberately not giving a link as I want you to…See More

Aug 20, 2014

Sai Reddy posted a discussion### Easiest way to learn machine learning.

There are some excellent resources here. But, I thought, the more helpful approach might be a plan and hence am adding one more answer to this list.My goal is to create a plan where you get to the level of average industry practitionerSkills you need: Ability to take Excel/CSV data sets, pre-process and visualize; Build a model and Visualize the resultsRecommended steps:1. Download one data set from Kaggle/UCI or anywhere from the Internet. I am deliberately not giving a link as I want you to…See More

Aug 18, 2014

Sai Reddy's discussion was featured### Currently the hot topics in Machine Learning research and in real applications.

In general the following are fairly hot in machine learning and data science communities that are interested in modeling:Deep learning: This seems to be breaking all benchmarks in accuracies in a variety of complex problems. NLP: Understanding sentiment, sarcasm, urgency and summarizing free flowing text are being studied extensively. Spectral methods and Kernel methods driven modeling methods are always hot problems.From an engineering perspective, there is a lot of emphasis in building newer…See More

Aug 9, 2014

Sai Reddy's discussion was featured### Solution for pharmaceutical industry

Image taken from eletsonline.comINSOFE scientists developed novel algorithms that could be used by pharmaceutical industry to check fraud in clinical trials. Typically, Pharma companies follow a tedious, semi-manual approach to solve this all important (billion dollar) problem. What makes it…See More

Aug 6, 2014

- My Website or LinkedIn Profile (URL):
- http://in.linkedin.com/pub/devarapalli-sai-sree/4a/861/649/

- Field of Expertise:
- Business Analytics, Predictive Modeling, Data Mining, Operations Research, Statistical Programming, Vizualization

- Professional Status:
- Executive Management

- Interests:
- Other

- What Other Analytical Website do you Recommend?
- http://insofe.edu.in/

- No comments yet!

© 2019 AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

**Technical**

- Free Books and Resources for DSC Members
- Learn Machine Learning Coding Basics in a weekend
- New Machine Learning Cheat Sheet | Old one
- Advanced Machine Learning with Basic Excel
- 12 Algorithms Every Data Scientist Should Know
- Hitchhiker's Guide to Data Science, Machine Learning, R, Python
- Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Pyth...
- How to Automatically Determine the Number of Clusters in your Data
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- Fast Combinatorial Feature Selection with New Definition of Predict...
- 10 types of regressions. Which one to use?
- 40 Techniques Used by Data Scientists
- 15 Deep Learning Tutorials
- R: a survival guide to data science with R

**Non Technical**

- Advanced Analytic Platforms - Incumbents Fall - Challengers Rise
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- How to Become a Data Scientist - On your own
- 16 analytic disciplines compared to data science
- Six categories of Data Scientists
- 21 data science systems used by Amazon to operate its business
- 24 Uses of Statistical Modeling
- 33 unusual problems that can be solved with data science
- 22 Differences Between Junior and Senior Data Scientists
- Why You Should be a Data Science Generalist - and How to Become One
- Becoming a Billionaire Data Scientist vs Struggling to Get a $100k Job
- Why do people with no experience want to become data scientists?

**Articles from top bloggers**

- Kirk Borne | Stephanie Glen | Vincent Granville
- Ajit Jaokar | Ronald van Loon | Bernard Marr
- Steve Miller | Bill Schmarzo | Bill Vorhies

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives**: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions