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Four reasons why you may not be ready for a job in data science

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This week on towardsdatascience.com Jeremie Harris has been sharing insights on the most common reasons why applicants get rejected for data science roles. In his vast experience coaching hundreds of data scientists, Jeremie believes that lack of competency in the below areas accounts for around 70% of interview rejections. So if you can work on improving on these points, it might just be enough to land you that job that you've been searching for. And of course, in this post I'll also let you know how the Qlik Academic Program can help you to develop some of these skills. 

1. Python for data science skills - To prove to prospective employers that you are job-ready, make sure that you have experience on a few projects using: data exploration, feature selection, hyperparameter search for model optimization and pipelines. 

2. Probability and statistics knowledge - As the cornerstones of data science work, it's vital that you are familiar with Bayes' theorem, basic probability and model evaluation. 

The Qlik Academic Program offers a product agnostic data analytics curriculum which includes teaching on Bayes' theorem and statistical concepts. 

NOTE: To access the above link, you must be a member of the academic program. 

3. Software engineering know-how - Software engineering work now often comes as part and parcel of Data Science roles. You must be able to manage your code and keep clean notebooks and scripts including version control, web development and web scraping.

4. Business Instinct - Unfortunately being the most technically able candidate won't necessarily land you the job. You also have to show that you are business savvy and that you can help propel the company forward to future success. This includes working on projects that people in the business actually need and want. A key way to ensure that you do this is by having the ability to ask the right questions from the start. And being able to explain your results to a non-technical audience is also vital, so that the outcomes for/ impacts on the business are clear.

Qlik's  Data Literacy Program can help you to improve these key business skills by teaching you to ask the right questions, interpret findings and to take informed action. 

You can read Jamie's full article here, and to develop your statistical knowledge and  business instincts, sign up for the Academic Program today! The program is open to all university students and academics globally. We hope that these tips will set you on the road to landing your dream job in data science!