Weekly News Digest: August 1st – 5th | Harnham Recruitment post

This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics.


Big tech interviews don’t have to be intimidating if you know how to prepare properly.  With so many big tech companies focusing more on cloud technologies, many data scientists are targeting the likes of Meta, Amazon, Netflix, and Google for their first jobs in the industry. While candidates will understandably have some apprehension around their interview with such companies, they are not that different from other interviews.  Most big tech companies have similar interviewing practices. They are simply more selective than others which results in high rejection rates. Consequently, you don't need to worry about ‘impossible’ questions when preparing for these interviews. Instead, you should concentrate on the typical technical interview questions, paying close attention to how you will differentiate yourself from the hundreds of other highly competent applicants for the same profession. Get ahead of your interviews with a personal strategy that helps you play to your strengths and avoid your weaknesses by having a thorough understanding of how a company organises, plans, and evaluates interviews. Towards Data Science shares the following tips to remember during the interview process with big tech companies: Learn the realities and competitive landscapeData science skills are nothing without real-world problem-solving skillsYour competitors will probably have several years of industry experience and the educational qualifications to matchIf you’re making a career change, the hardest part of the process will be getting a foot in the doorChoose your learning resourcesDetermine how you will stand out during the interview process To read more about this, click here.


Biostatisticians’ responsibilities go beyond simply analysing data at the end of a clinical study – they’re involved in the management of the clinical trial from day one in order to maximise the possibility of new treatments being authorised for the market.  Overseeing these clinical trials means they have a long to-do list. They make recommendations for trial design, choose the right sample size, and ensure that the patients who are enrolled are randomised fairly. They provide definitions for data analysis, help define endpoints, and create tables and graphics for the clinical study report. “People often think that biostatistics comes in at the end of a clinical trial, but this can lead to a lot of issues, for example, when you find out too late about missing data or incorrect randomization,” said Malin Schollin, Director of Biostatistics at LINK Medical, a Swedish contract research organisation. “There is great value in having a statistician on board during the entire project because then we can take part in the decision making, and help assess how it will affect analyses, evaluation, or results.” To read more about this, click here.


Data Science uses Python to deal with massive amounts of data every single day. Many students are interested in data science and, in the same vein, work on a variety of mini projects based on data science using Python. Data science is a discipline that assists us in extracting knowledge and information from many sorts of structured or unstructured data. Here are five python Data science mini projects to explore. Real-time audio analysis – This will pique the curiosity of music enthusiasts by allowing you to perform real-time audio analysis with the Fast Fourier Transform tool, which is a crucial skill set for a data scientist. Color Detector – Determine all colour hues from a given image or video, whether it is black and white or colour. This can be quite useful for investigating officials and in the industry! Banking fraud detections – Detect credit card fraud utilising data science principles such as decision trees, neural networks, and logistic regression. Real-time image animation – Deal with visual expression dependent on camera position. This involves the use of data science in conjunction with computer vision. Business Advisor software using data science – One of the most intriguing projects because it employs exploratory data analysis, in which the programme automatically analyses the data, raises questions, and then displays facts and solutions in the form of visual graphs and other charts. To read more about this, click here.


START WRITING ARTICLES Data is all around us; we all generate massive amounts of data every day. Yet non-technical individuals have no idea what data is or why it is so valuable.  That is why, in every presentation, a data scientist must educate their audience. You may be required to describe the data, explain how you intend to utilise it to construct a model, and present your findings. You can’t do this without critical skills in writing and communication.  Businesses have a tonne of data and they need data scientists who can share their insights with others, while also understanding and using this data. Sometimes people with technical expertise aren't the best communicators, which is where strong writing skills can be especially useful.  Writing can help you transmit ideas effectively. Once you become good at writing, explaining your ideas in a presentation or meeting will become more natural and fluent.  When writing an article, you become more aware of your work. You can spot weak points and discover sections that need more research or could be a new topic for another article. So, believe it or not, writing can help you improve your communication skills and learn more about the field of data science.  To read more about this, click here.  We've loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

Posted in