4 Important Skills You Need For Success in the Data Science Field

Nearly 80 percent of college students will change their major during their pursuit of a degree. Studies show that students who wait longer to declare their major have a graduation rate of around 84 percent.

If you have a passion for technology, then pursuing a data science degree may be a good idea. While getting this degree will be challenging, it will be worth the effort you invest.

In order to have success in this industry, you will need a wide range of skills at your disposal. The following are just some of the most important skills needed to have a long and fruitful career in the data science industry.

Data Science Field

  1. Programming Skills are a Must

Knowing how to use the tools of the trade is important when applying for a job in the technology world. Most employers will expect data science graduates to be familiar with statistical programming languages like R or even Python. You will also need to brush up on your SQL skills due to how widely used it is in the building of databases.

Generally, most colleges will provide extensive training on coding to their data science students. Being present during these training sessions and taking plenty of notes can help you greatly in the future.

  1. A Good Understanding of Statistics

Having a firm grasp on how to perform statistical tests, maximum likelihood estimates and distributions can also be advantageous as you pursue a job in the data science industry. Extensive knowledge of statistics can help you greatly when trying to figure out a valid approach for a tech problem you have been presented.

Many data-driven companies rely heavily on statistics to help them make important decisions. These decisions generally affect a company’s bottom line, which is why having an experienced data scientist at the helm is essential.

  1. Machine Learning Methods

Large companies that use a lot of data will want to hire a data scientist with a great deal of machine learning knowledge.  Machine learning methods like random forests and k-nearest neighbors help companies like Google Maps and Uber serve their client-base effectively.

Many of the aforementioned methods can be implemented in R or Python code libraries. This means that you do not have to be that familiar with how a particular algorithm works to do your job. Simply understanding the broad strokes of machine learning can help you use a variety of methods to meet the demands of your employer.

Master of Data Wrangling

  1. Become a Master of Data Wrangling

In a perfect world, the data you are hired to work with will be perfectly organized and easily accessible. In reality, your main job will be to wrangle data and find a way to completely overhaul and organize it. There will be some instances when the data you are compiling will have an endless array of errors. Knowing how to find these errors and fix them can help you impress your new employer.

The key to having success in the data science industry is getting the best training and education possible. Soaking up all of the lessons you are taught in college will serve you well when it is time to find a job in your chosen field.

Deepak

After working as digital marketing consultant for 4 years Deepak decided to leave and start his own Business. To know more about Deepak, find him on Facebook, Google+, LinkedIn now.

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