Connect with us

Education

Jumpstart your career in Data Science

Source: Education Times

Be curious and ask questions to fully understand the Data Science value chain, writes Lakshmana Gnanapragasam

Data Science has been ranking consistently high among the top career choices for new graduates and experienced professionals alike. Data Science, as a practice, has benefited immensely by being in the thick of exponential trends that companies across industries are going through right now, including increased ubiquity of data, advancements in machine learning algorithms, and improvements in technology.

Thus, it is not surprising that several young and experienced professionals are interested in a career in Data Science. Here are few steps that can help you jumpstart a career in the field.

Step 1: Develop a data ninja growth mindset
Data Science offers no single, best answer to a problem statement. The solution is not in the data, but often it’s in the mind of the data scientist. So, first develop a data ninja growth mindset. What does this mean?
a) Being inquisitive and having a learner mindset: Data Science is an evolving space; ask a lot of questions about the problem and data
b) Being humble: You don’t know all the answers upfront and you may be wrong
c) Being able to work hard: Spend more time cleaning and preparing the data first than developing advanced algorithms
d) Being resilient and iterating solutions: Your first model is often wrong. Be willing to scratch-off your initial answers and iterate quickly to better solutions

Step 2: Self-learning
There are several self-learning opportunities available online, both paid and free. Check out Data Science introduction courses available across platforms. Start with some basic courses on Statistics and Probabilities, before proceeding to basic/advanced introductions to Data Science. Pick up a few languages and learn them thoroughly.

Step 3: Get credentials if you have the time and resources
While self-learning is a great first step to start your learning journey, in some cases it may be worth investing in accredited Data Science courses. Most reputed Engineering and Management schools offer such courses that will help you in two ways, including getting a Data Science degree/certification from a reputed institute, and learning from the best professors and industry specialists.

Step 4: Do exercises and get yourself noticed
While as part of self-learning, you would have done some exercises with sample datasets, now would be a good time to go for online Data Science competitions. Organisations continue to conduct Hackathon programmes to identify talent. The problems posed here are usually not exactly like real-world problems in terms of size and scope, but this would give you an opportunity to get hands-on experience in solving a problem. The intention is not to merely to win, but to learn how to solve a unique problem within a time constraint, and benchmark yourself against other practitioners.

Step 5: Ask for internships, internal work assignments
If you are a graduate seeking a career in Data Science, look for companies that offer an internship to final year students. As an intern, you may be asked to do some foundational work in Data Science that may include gathering, cleaning, and preparing the data than building advanced machine learning models. Invest time in this crucial stage and build strong relationships with stakeholders in the organisation.

If you are already working in an organisation, ask for internal work assignments or stretch projects with the Data Science team to advance your career path.

Step 6: Acing the interview for your first Data Science job
If you follow these steps, no special prep will be required for your job interview other than learning about the company and what they do. In the interview, showcase the effort you have put in, refer to your credentials and highlight the problems you have worked on. Companies like recruiting talent that have invested upfront in the areas they are hiring for.

Once you join the organisation, look for a role that would allow you to work on real business problems for internal or external stakeholders. Be curious and ask a lot of questions to fully understand this Data Science value chain. Start with the big picture. Prioritise understanding the organisation’s industry and domain, how value is generated and how Data Science can enable and transform the organisation.

Following these steps will help you move onwards and upwards in building a fulfilling career in Data Science.

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Business

Dealing with Parental Leave: How Your Business Can Support Employees with Families

Looking after your staff is a fundamental part of running a successful business, ensuring staff turnover remains low and workers remain happily motivated. Workers now have more agency than ever when it comes to choosing their employer, in part thanks to the rise in remote working which means workers are no longer limited to looking for roles within their local area.

39% of UK workers now work at home within a given week and workers are beginning to demand more in terms of employee benefits, especially when it comes to welfare.

One of the areas where employees may focus is “family-friendly” working and benefits. But what does the law say about these contractual offerings? And how can your business benefit from having a comprehensive “family-friendly” benefits package? We spoke to the employment law specialists at Beecham Peacock to discover how your business can become more caring.

What does the law say about parental leave?

In the UK, women are able to take up to 52 weeks of maternity leave. The first 26 weeks of leave, which includes two weeks of compulsory leave (four weeks for factory workers), are known as ordinary maternity leave, while the final 26 weeks are known as additional maternity leave.

During maternity leave, a woman’s rights to pay rises, accrued holiday, and returning to work are protected by the law.

Eligible mothers-to-be are entitled to be paid statutory maternity pay for 39 weeks. This will depend on whether or not they satisfy service and earnings criteria. Otherwise, they may not be able to claim maternity allowance.

Statutory maternity pay equates to six weeks paid at a rate of 90% of average weekly earnings (before tax). For the remaining 33 weeks, the current rate of payment is £172.48 or 90% of their average weekly earnings – whichever is lower. This rate is reviewed annually.  

There has been much discussion about the mandatory amount of maternity pay and whether it does enough to support women in the workplace – a recent study found statutory maternity pay is just 47% of the national living wage. To attract and retain women, businesses may wish to consider offering enhanced maternity pay and benefits packages.

For partners, leave entitlements are different. Statutory paternity and adoption leave entitles fathers/partners to take one or two weeks of paid paternity leave, paid at a rate of £172.48 or 90% of their average weekly earnings – whichever is lower. This rate is also reviewed annually.

When this leave is taken differs depending on whether paternity or adoption leave is being taken. Again, your business may wish to consider enhanced leave and pay packages.

For eligible parents, another option that is increasingly taken up is shared parental leave. Whilst the mother will always have to take two weeks of compulsory leave (four weeks for factory workers), the remaining 50 weeks (or 48 weeks for factory workers) can be taken by either parent.

This gives both parents flexibility and the opportunity to spend time with their child.  Statutory parental leave pay is paid at the same rate as the latter part of statutory maternity or paternity pay, and can be paid for up to 37 weeks to eligible employees. Again, businesses may wish to consider offering enhanced parental leave pay to attract and retain employees.

What are the positives of greater employee benefits for parental leave?

Of course, there are extra costs associated with paying more than the statutory pay requirement. However, offering parental leave options and policies that go above and beyond the minimum requirements can benefit a business just as much as it benefits your employees. Such packages will enable business to attract and retain employees.

How to draft a comprehensive parental leave policy

Lisa Branker, Head of Employment Law at Beecham Peacock, advocates for a comprehensive leave policy that supports all of your employees. She comments:

“Entitlements and eligibility for parental leave, pay and benefits should be clearly contained in your business’ relevant policy. If your goal is to attract and retain your workforce through flexible and/or enhanced benefits packages then this information needs to be clearly set out and accessible. A clear policy makes employees aware of how much leave and pay they are entitled to, helps managers to respond to any queries, and allows your business to plan for and support working parents.”

“Pay and leave aren’t the only considerations – for example, your business may be able to offer a salary sacrifice scheme to make childcare arrangements. Other, non-financial support can also be a huge help for new parents or parents-to-be. Increasing the flexibility of working hours or offering a hybrid working scheme can give your colleagues the support they need to manage the transition into parenthood. These measures will enable you to motivate and retain your workforce, without creating an onerous financial burden.”

Every company is different – and there’s unlikely to be a one-size-fits-all solution. Think about which solution (or combination of solutions) is best-suited to your company before creating or amending a parental leave policy. If you’re considering creating or updating your policy, Beecham Peacock’s free policy reviews are a great starting point to check your offerings meet your business and legal needs.

https://www.beechampeacock.co.uk/employment-law/

Continue Reading

Business

Bridging the Gap: Evaluating Technology Companies’ Efforts in AI Consumer Education

Agata Karkosz, Leader at FPAcademy at Future Processing

AI is becoming increasingly influential across various sectors and industries. And its impact is only expected to grow in the future. In fact, AI adoption has more than doubled since 2017, with businesses making larger investments to scale and fast-track development.

With such rapid advancements taking place, it is important to educate consumers about navigating the complexities of the technology.

The Current Landscape of AI Education

There are several companies actively providing education and training in the field of AI. Google AI has been offering AI and machine learning courses to consumers for a few years now, while lesser-known companies, like Coursera, edX and Fast.ai, have also entered the space.

Nevertheless, these offerings are often catered to people interested in learning and developing skills in the technology, rather than the everyday consumer. In fact, research released last year by leading software development company, Future Processing, found that more than two-thirds of UK consumers aged 50 and above felt technology companies needed to do more to help them understand AI.

Respondents were asked what AI applications and tools they are currently using, with AI voice assistants and customer service chatbots coming out on top. But, because the technology’s capabilities are wide-reaching, companies need to up their game to ensure the average consumer is in the know.

Educational Initiatives: Are They Enough?

Companies must work harder to help consumers of varying levels of technical expertise understand AI. Doing so will help demystify AI and build consumer confidence and trust.

Understanding this need, Google AI offers resources such as the “Machine Learning Crash Course”, a free online course designed to introduce individuals to machine learning concepts. Microsoft, too, has its AI School, an online platform supplying free courses and resources covering various AI topics, while other major firms, including Facebook, Intel, Amazon and NVIDIA, have sparked similar education initiatives.

Still, due to fresh technological advancements and research breakthroughs, as well as increased data availability, open source and collaboration efforts, rapid industry adoption, and greater funding and investment, the challenge for companies comes in maintaining up-to-date and readily available education materials.

The Challenge of Simplifying Complexity

The inherent complexity of AI arises from the interdisciplinary nature of the field, combining elements of computer science, mathematics, statistics, neuroscience, and engineering. AI involves complex algorithms, sophisticated mathematical models, and intricate programming structures that are hard to rationalise.

Balancing the need to simplify AI concepts for broader understanding while retaining the essential information poses a significant communication challenge for businesses. Oversimplifying may lead to misconceptions or a lack of appreciation for the complexities involved while providing too much detail can result in confusion.

Successful communication often involves using relatable metaphors, real-world examples, and interactive experiences to engage consumers and help them grasp the fundamental principles without getting lost in technical intricacies. Effective communication about AI requires collaboration between experts, educators, communicators, and the general public to ensure a more informed and inclusive understanding of this rapidly advancing field.

Transparency and Explainability

Enhancing transparency and explainability in AI systems has become a key focus for technology companies to address concerns related to bias, accountability, and trust. Several efforts have been made to make AI systems more understandable and interpretable. Some common strategies and initiatives include:

Interpretable Models

Many technology companies are working on developing AI models that are inherently more interpretable. This involves using algorithms and architectures that produce results that can be easily explained and understood. For example, decision trees, rule-based systems, and linear models are often more interpretable than complex deep neural networks.

Explainable AI (XAI) Techniques

Explainable AI is a research area focused on developing techniques and tools that help users understand the decisions made by AI models. Techniques such as feature importance analysis, saliency maps, and attention mechanisms aim to highlight the factors influencing the model’s predictions.

Ethical AI Guidelines

Many companies have established ethical AI guidelines and principles prioritising transparency and fairness. These guidelines may include commitments to avoiding biased data, providing clear explanations for decisions, and involving diverse perspectives in the development process.

User-Friendly Interfaces

Technology companies are investing in user-friendly interfaces that allow users to interact with AI systems more intuitively to enhance transparency. Dashboards, visualisations, and plain-language explanations help users understand the model’s behaviour and outputs.

Addressing Challenges and Gaps

AI education faces several challenges that stem from the diverse nature of the field, the varied backgrounds of learners, and the evolving landscape of information dissemination.

The rapid evolution of AI is a primary challenge, but a lack of standardisation, diverse audience backgrounds, and the spread of misinformation have also hampered the public’s ability to understand the technology.

The highest concerns surrounding AI are privacy, transparency and security. This is supported by Future Processing’s research, with respondents aged 50 and above ranking security and data privacy as their highest concerns when using AI, followed closely by misinformation and question misinterpretation.

But, through ongoing collaboration and dialogue, governments can establish standards which foster diversity and inclusivity, provide up-to-date resources, and emphasise practical application to deliver more effective and equitable AI education.

The Role of Ethical Guidelines

With AI frequently coming under the spotlight, technology companies are increasingly recognising the importance of ethical guidelines and policies in AI development and usage.

Many have published official documents outlining their ethical principles and values concerning AI. These documents typically cover commitments to fairness, transparency, accountability, privacy, and avoiding biases in AI systems. For example, Google and Microsoft’s respective AI Principles are publicly available documents that articulate the companies’ ethical commitments.

Learning to Embrace

Continuous efforts in AI consumer education are imperative to navigate the evolving landscape of AI, bridge educational gaps, address ethical considerations, and ensure that individuals are equipped with the knowledge and skills needed in an increasingly AI-driven world. The commitment to transparency, inclusivity, and ethical practices will contribute to building a more informed and responsible AI community, meaning we can embrace all it can bring to the table, rather than dwelling on its shortcomings.

Continue Reading

Business

Future-proofing the workforce for AI innovations with continuous learning  

By Alexia Pedersen, VP EMEA at O’Reilly 

The UK government has made clear its intentions to make the UK a global AI superpower. Key to this is the National AI Strategy, which aims to boost enterprise use of AI, attract international investment and develop the next generation of tech talent to ensure the UK plays a leading role in discovering and developing the latest innovations.  

This sentiment is echoed by business leaders, with our latest ‘Generative AI in the Enterprise’ report revealing that a significant number of businesses are increasing adoption and investment in generative AI. 

Amid unprecedented adoption, business leaders must prepare their workforce for the advent of such technologies in the workplace. This requires greater emphasis on continuous learning and development (L&D), offering ongoing opportunities for employees to develop new skills that are required for the effective and safe use of such technologies. Looking ahead to 2024, what should business leaders do to get started?  

The current state of play  

Understandably, business leaders are pushing ahead with generative AI investments, given its potential to drive growth, optimise operations and deliver exceptional customer experiences. In fact, more than two in five (44%) IT professionals confirmed that their company plans to spend between £25,001–£50,000 on these solutions in the next 12 months alone. 

Yet, we cannot ignore that almost all (93%) IT pros are concerned with their C-suite’s ambitions for the use of generative AI tools, which is due to fears that workplace policy and training opportunities are failing to keep pace.  

According to IT teams, staff outside of their department have been provided limited (32%) or no training opportunities at all (36%) about how generative AI will impact the workplace. As a result, more than a quarter (27%) of IT professionals identified the lack of training for employees as one of their biggest concerns around AI adoption, which is on par with their fears of more advanced cybersecurity threats posed by such technologies.  

This year, increased L&D opportunities will be pivotal in bridging gaps in knowledge – ensuring companies can continue to invest in AI tools but with greater assurance that deployments will be ethical and safe.  

Future-proofing your workforce with a continuous learning culture  

Fortunately, in today’s digital landscape, staff are keen to invest time in their development and take on new opportunities that provide growth opportunities.  

Within IT departments, the majority (82%) of staff want more AI-related L&D opportunities to help advance their current roles. They feel so passionately about it that more than two in five (43%) IT employees have sought external training opportunities over the last twelve months, and a similar amount (61%) are considering moving companies over the next twelve months if their employer fails to provide upskilling opportunities around generative AI. 

These findings highlight that if employers want to recruit and retain the best talent in 2024, they need to play a vital role in creating a culture of continuous learning – empowering staff to take on new challenges, seek out opportunities for growth and share their knowledge with others.  

To help employees prioritise learning around day-to-day responsibilities, companies should consider ‘in the flow of work’ learning opportunities. This concept was coined by Josh Bersin to describe a paradigm in which employees learn something new, quickly apply it and return to their work in progress. While traditional learning approaches such as attending a seminar or conference are effective, many employees simply don’t have the time to devote to them or they prefer to learn at a time that suits them best.  

Instead, ‘in the flow of work’ learning provides employees with the tools needed to quickly find contextually relevant answers to their questions at a time that suits their schedule. Companies can offer more flexible learning opportunities via a trusted L&D partner, who will tailor materials to an individual employee’s unique learning style and objectives and also offer structured learning to upskill. For example, badges are becoming an increasingly popular method for verifying an individual’s knowledge and skills. Because skills training and upskilling opportunities have risen in popularity as benefits that candidates seek, employers can attract and retain key talent by offering ongoing learning opportunities, including the ability to acquire badges.

Looking ahead  

In 2024, successful AI deployments will require more than just investment in cutting-edge solutions. Business leaders should also invest in developing a culture of continuous learning, one that equips employees with the skills and mindset needed to leverage generative AI technologies effectively. 

Comprehensive, more flexible learning opportunities that are accompanied by thorough workplace policies will be essential for innovative enterprise use cases to flourish. At the same time, this will go a long way in enhancing recruitment and retention strategies in the face of a widening technical skills gap. Only with a highly skilled workforce will the UK truly live up to its aspirations of becoming a global leader in AI. 

Continue Reading

Copyright © 2021 Futures Parity.