Skip to main content

Myth about Data Science - A must know for all Data Science enthusiast


1. Only Coder /Programmer can only become a Data Science

No, its not correct. People who is having Basic Programming skills like Python/R or atleast who can learn basic programming can come in to this field.Here i wanted to suggest people who is having Engineering background /Software they can choose Python as a programming and The person who wanted to transit their career in to data science field but coming from non Engineering background like Arts,Commerce,Science they can prefer R as a Programming language . Here am not saying for non technical background can not learn python , its bit difficult to understand the basic and algorithm but if they are ready to learn no issues, they can take any of these either Python or R., I have Mentioned while choosing any of these which one is good according to me in another article i.e python, you can refer my article to get better understanding.

2. Data Scientist are master of all technology .

No, fact is that you should have knowledge on basic data science skills , you dont required to be expertise. A famous line " Jack of all trades " we should have knowledge or basic idea about terminology , algorithm how to apply on the data , basic statistical skill , no one would master in this Era, specially when technology is keep on growing. So i can say you must familiar about the terminilogy and logic and their uses and main thing how to apply these in our data.

3. Data Science is all about tools and technology.

No , its not . Data science is not just about tools and technology, because just applying some lines of code and executing the algorithm and getting the good accuracy is not data science, you should know how to interpret the algoithm and understanding the algorithm /Choosing the algoirthm which is best for the particular problems which is correct or not.

4. Data Analyst and Data Science both works same:


“A data scientist is someone who can predict the future based on past patterns whereas a data analyst is someone who merely curates meaningful insights from data.”

“A data scientist job roles involves estimating the unknown whilst a data analyst job roles involves looking at the known from new perspectives.”

“A data scientist is expected to generate their own questions while a data analyst finds answers to a given set of questions from data.”

“A data analyst addresses business problems but a data scientist not just addresses business problems but picks up those problems that will have the most business value once solved.”

“Data analysts are the one who do the day-to-day analysis stuff but data scientists have the what ifs.”

This is what Abraham Cabangbang, Senior Data Scientist at LinkedIn commented on the difference between data analyst and data scientist -

“It’s definitely a gray area. At my previous company I did both analyst and Data scientist jobs and as an analyst we were more customer facing; the tasks we did were directly related to the tangible business needs—what the customers wanted/requested. It was very directed. The scientist role is a little more free form. The first thing I did as a data scientist is work on building out internal dashboards, basically surfacing information that we were tracking on the back end, but weren’t being used by the data analysts for any reasons; for example, we might have lacked the infrastructure to display it, or the data was just not very well processed. It really wasn’t anything tailored out from a customer need, but came from what I noticed the analyst team needed in order to do their job.”.
Labels:

Comments

Post a Comment

Popular posts from this blog

Differentiate between univariate, bivariate and multivariate analysis.

Univariate analysis are descriptive statistical analysis techniques which can be differentiated based on one variable involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis. The bivariate analysis attempts to understand the difference between two variables at a time as in a scatterplot. For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis. Multivariate analysis deals with the study of more than two variables to understand the effect of variables on the responses.
Post a Comment
Read more

How to deal with missing values in data cleaning

The data you inherit for analysis will come from multiple sources and would have been pulled adhoc. So this data will not be immediately ready for you to run any kind of model on. One of the most common issues you will have to deal with is missing values in the dataset. There are many reasons why values might be missing - intentional, user did not fill up, online forms broken, accidentally deleted, legacy issues etc.  Either way you will need to fix this problem. There are 3 ways to do this - either you will ignore the missing values, delete the missing value rows or fill the missing values with an approximation. Its easiest to just drop the missing observations but you need to very careful before you do that, because the absence of a value might actually be conveying some information about the data pattern. If you decide to drop missing values : df_no_missing = df.dropna() will drop any rows with any value missing. Even if some values are available in a row it will still get dropp...
1 comment
Read more

Random Forest Algorithm

Random Forest is an ensemble machine learning algorithm that follows the bagging technique. The base estimators in the random forest are decision trees. Random forest randomly selects a set of features that are used to decide the best split at each node of the decision tree. Looking at it step-by-step, this is what a random forest model does: 1. Random subsets are created from the original dataset (bootstrapping). 2. At each node in the decision tree, only a random set of features are considered to decide the best split. 3. A decision tree model is fitted on each of the subsets. 4. The final prediction is calculated by averaging the predictions from all decision trees. To sum up, the Random forest randomly selects data points and features and builds multiple trees (Forest). Random Forest is used for feature importance selection. The attribute (.feature_importances_) is used to find feature importance. Some Important Parameters:- 1. n_estimators: - It defines the number of decision tree...
Post a Comment
Read more