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Verwin Bhagya C
What are the skills I need to develop to get into data analytics?
 Posted on 3/5/2019
4 Answers
Verwin User Default Image Ashwin Ramanathan Answered on 11/5/2019
Data analytics is much more than just statistics, R code and modeling.  The ability to bring value to your organization through data analytics is very important. There is no use of a model that just throws out some obscure numbers and predictions, hence in that line.

1. Is the problem properly defined , towards which end are you going? These are some questions to ask to yourself before starting a project. Also always try to work towards a value increment than just a random number. even if your model is 90% accurate and it brings value to your organization by saving time and money, its more worth than a model that is 99% accurate but doesnt bring any value 

2. Learn how to define timelines. whenever someone asks you to do something, do not panic or get flustered. Evaluate the resources you have, the effort it will take and the challenges you would face before committing to a deadline.

3.Learn to make good PPT's and word documents that communicate to the reader very clearly without much confusion

4.Whatever you make, try to give it as a solution. For eg. you can build a stand alone linear regression model that runs on the python console. or you can make the same model as a small application hosted on a server so that anyone can use without having  knowledge of python

5.Keep learning new things that you feel will help you in the future. Given the job nature of data analytics, it is best to keep yourself updated as the field changes rapidly

6.Learn how to get the data from the source rather than having someone give it to you as a usable file. Much of the analytics work in a project is getting the data, cleaning it and building features. So modeling is not as important as it may seem. the model just explains the data. if you feed in garbage, the model is going to feed out garbage.

7. Whatever you do, try to do it on the cloud. Any task from storage to parallel computing, try to do it on the cloud or server,as thats where the world is at right now, rather than doing it in your own machine
Verwin Krishna  Chaitanya Krishna Chaitanya Answered on 6/5/2019
In addition to what others have responded, you also need to have Product Management expertise. This one is definitely debatable, but in my opinion, those who understand the product are the ones who will know what metrics are important. There are tons of numbers one can A/B test, so a product-oriented data scientist will pick the right metrics to experiment with. 
Verwin Rahul  Deshmukh Rahul Deshmukh Answered on 4/5/2019
It's important to know machine learning basics. You should be able to explain k-nearest neighbors, random forests, ensemble methods. These techniques are typically implemented in R or Python. These algorithms show to employers that you have exposure on how to apply data science in a practical manner. Hope this helps.
Verwin Prakash  Sundaram Prakash Sundaram Answered on 3/5/2019
Basic programming languages: You should know a statistical programming language like R or Python (along with NumPy & Pandas Libraries) and a database querying language like SQL.
Statistics: You should be able to explain phrases like null hypothesis, p-value, Maximum Likelihood estimators and confidence intervals. Stats is important to crunch data and to pick out the most important figures out of a huge dataset. This is critical in the decision-making process and to design experiments.
What are the skills I need to develop to get into data analytics?
Data analytics is much more than just statistics, R code and modeling.  The ability to bring value to your organization through data analytics is very important. There is no use of a model that just throws out some obscure numbers and predictions, hence in that line.

1. Is the problem properly defined , towards which end are you going? These are some questions to ask to yourself before starting a project. Also always try to work towards a value increment than just a random number. even if your model is 90% accurate and it brings value to your organization by saving time and money, its more worth than a model that is 99% accurate but doesnt bring any value 

2. Learn how to define timelines. whenever someone asks you to do something, do not panic or get flustered. Evaluate the resources you have, the effort it will take and the challenges you would face before committing to a deadline.

3.Learn to make good PPT's and word documents that communicate to the reader very clearly without much confusion

4.Whatever you make, try to give it as a solution. For eg. you can build a stand alone linear regression model that runs on the python console. or you can make the same model as a small application hosted on a server so that anyone can use without having  knowledge of python

5.Keep learning new things that you feel will help you in the future. Given the job nature of data analytics, it is best to keep yourself updated as the field changes rapidly

6.Learn how to get the data from the source rather than having someone give it to you as a usable file. Much of the analytics work in a project is getting the data, cleaning it and building features. So modeling is not as important as it may seem. the model just explains the data. if you feed in garbage, the model is going to feed out garbage.

7. Whatever you do, try to do it on the cloud. Any task from storage to parallel computing, try to do it on the cloud or server,as thats where the world is at right now, rather than doing it in your own machine
Verwin User Default Image
Answered on: 11/5/2019
In addition to what others have responded, you also need to have Product Management expertise. This one is definitely debatable, but in my opinion, those who understand the product are the ones who will know what metrics are important. There are tons of numbers one can A/B test, so a product-oriented data scientist will pick the right metrics to experiment with. 
Verwin Krishna  Chaitanya
Answered on: 6/5/2019
It's important to know machine learning basics. You should be able to explain k-nearest neighbors, random forests, ensemble methods. These techniques are typically implemented in R or Python. These algorithms show to employers that you have exposure on how to apply data science in a practical manner. Hope this helps.
Verwin Rahul  Deshmukh
Answered on: 4/5/2019
Basic programming languages: You should know a statistical programming language like R or Python (along with NumPy & Pandas Libraries) and a database querying language like SQL.
Statistics: You should be able to explain phrases like null hypothesis, p-value, Maximum Likelihood estimators and confidence intervals. Stats is important to crunch data and to pick out the most important figures out of a huge dataset. This is critical in the decision-making process and to design experiments.
Verwin Prakash  Sundaram
Answered on: 3/5/2019