Skip to main content

How To Get Data Science Job/Internship

 If you all guys out there looking for tips and tricks about how to get a data science job or internship you want to apply for?

Before telling you how to get you should know how to be an expert in it so for that please read my article.

You have come to the right place! Nowadays reports and publications consistently name ‘data scientist’ as one of the preferable jobs. While there are many articles about the set of skills you need to get the data scientist position, we wanted to focus on the students who crave working in this prosperous field.

The data science will be a good job if you want to get chill in your life because according to many articles it will be the best job in 2020.

Just follow the below steps for getting jobs/ internship

  1. Learn about basic stuff you do not need to research topics. so read-only about regression or classification and from top neural network. That will be enough study for fresher to know, another thing you can learn on the job.
  2. After knowing the basics now you have to do some project to practice those skills go to Kaggle and do basic problems that will give you an idea of how to tackle the problem in real life.
  3. Now you are ready to give an interview for getting the job, now for this, you have to show to the recruiter that you know the basics and you also did the problems based on real-life on kaggle.
  4. Last thing I want to say don't forget to revise DATA STRUCTURES AND ALGORITHM because it will help in everything.
Image for post
Four Points just join them.

Now you know the four points that will help now you just have to solve the puzzle( for getting the job ) step by step as I told.

Labels:

Comments

Post a Comment

Popular posts from this blog

Random Forest and how it works

  Random Forest Random Forest is a Machine Learning Algorithm based on Decision Trees. Random forest works on the ensemble method which is very common these days. The ensemble method means that to make a decision collectively based on the decision trees. Actually, we make a prediction, not simply based on One Decision Tree, but by an unanimous Prediction, made by ‘ K’  Decision Trees. Why should we use There are four reasons why should we us e  the random forest algorithm. The one is that it can be used for both  classification and regression  businesses. Overfitting is one critical problem that may make the results worse, but for the Random Forest algorithm, if there are enough trees in the forest, the classifier  won’t overfit  the model. The third reason is the classifier of Random Forest can handle  missing values , and the last advantage is that the Random Forest classifier can be modeled for  categorical values. How does the Random...
1 comment
Read more

DBSCAN Clustering Algorithm-with maths

  DBSCAN is a short-form of   D ensity- B ased   S patial   C lustering of   A pplications with   N oise. It is an unsupervised algorithm that will take the set of points and make them into some sets which have the same properties. It is based on the density-based clustering and it will mark the outliers also which do not lie in any of the cluster or set. There are some terms that we need to know before we proceed further for algorithm: Density Reachability A point “p” is said to be   density reachable from a point “q” if point “p” is within ε distance from point “q” and “q” has a sufficient number of points in its neighbors which are within distance ε. Density Connectivity A point “p” and “q” are said to be density connected if there exists a point “r” which has a sufficient number of points in its neighbors and both the points “p” and “q” is within the ε distance. This is a chaining process. So, if “q” is neighbor of “r”, “r” is neighbor of “s”, “s” ...
2 comments
Read more

Neural Network theory and implementation for Regression

Introduction and background In this article, we are going to build the regression model from neural networks for predicting the price of a house based on the features. Here is the implementation and the theory behind it. The neural network is basically if you see is derived from the logistic regression, as we know that in the logistic regression: Formulae for Logistic Regression:  y = ax+b so for every node in each layer, we will apply it and after this output is from the activation function which will have the input from logistic regression and the output is output from the activation function. So now  w e will implement the neural  network  with 5 hidden layers. Implementation 1. Import the libraries which we will going to use 2. Import the dataset and check the types of the columns 3. Now build your training and test set from the dataset. 4. Now we have our data we will now make the model and I will describe to you how it will predict the price. Here we are making...
Post a Comment
Read more