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Conducting market research isn’t in itself so important for a company. What is important is the ability to predict the future with data science and analysis of its strategies. It helps giving more insight into what customers want, where people are going, how people are spending their time online, and on social media. Data science isn’t really about programming anymore; it’s actually informing technology trends by using machine learning models on huge sets of data. Using this analytical tool can help companies survive in today’s highly competitive market while remaining relevant to their customers.

What is Data Science & Analytics?

Data science comprises computer languages used for analyzing huge sets of data in different fields. Although some programming is necessary for this type of job, not all of the work involves coding. It may require research on how to improve your strategies depending on the result of an analysis.

Who is a Data scientist?

Data scientists are people that use statistics and machine learning concepts on data sets. They analyze the information gathered from different sources, including websites, social media platforms, etc., in order to make business-critical decisions.

Types of Data Science and Machine Learning

There are different types of machine learning and data science. Some of those based on the programming paradigm include: linear regression, logistic regression, neural networks, clustering, dimensionality reduction methods such as PCA and OLSM, SVMs and genetic algorithms. Other machine learning techniques such as KNNs and Naive Bayes classification are based on the statistical paradigm. Depending on how much work it needs, each can be used by companies for their strategies.

The most common use of data science is in data mining which involves processing information that’s already available to find key insights that haven’t been found before. Some of these techniques are linear programming, neural network programming, statistical methods, clustering analysis and factor analysis.

Data science applications are in demand in different industries. For example, medical health companies can use data science to predict disease outbreaks in order to stop them before they become large epidemics. Banks can use social media data in order to predict market changes in trading activities depending on people’s moods and topics they discuss online. Data science can also be used during elections to draw out the real opinions of voters rather than their projected intentions. The obvious examples are Facebook and Twitter where people post their opinions about what’s happening around them every day. These posts can help companies and political campaigns understand how people feel and what their concerns are.

Data Science Strategies

Data science can provide insights on business strategy, even if it’s considered more as a field of analysis. Companies gather data from different sources such as social media today, so they can’t ignore it as a source of market trends. The best thing about data science is that companies don’t have to start from scratch every time since it provides the big picture of all the available information gathered from different sources. Data science not only analyzes information but also provides predictions about future trends based on that data.

Even if it’s not used in the same way as machine learning, data scientists can still increase their companies’ efficiency by finding trends in their strategies. It’s possible to use big data to develop models that can suggest new products for your company, analyze your competitor’s strategies in the market, and even help you control expenses or make predictions about revenues through past trends. Big data gives you additional insights, like identifying your customers’ needs and knowing which products they will buy based on previous purchases.

Benefits of Data Science for Businesses

Data science is another tool that businesses can use for making decisions regarding their activities. It helps businesses increase their profits by avoiding losses, reduce operational costs and save time by making more accurate predictions.

It provides a new level of transparency with their customers by providing suggestions on which products to offer them depending on their needs. Another strategy is to use the data collected from the comments of your customers in social media platforms or blogs in order to understand their real expectations about your products and services. It can help you improve your existing strategies based on customer insights.

There are so many ways to apply big data for improving business strategies. If you have a new product launch ahead of your competitors, data science can help you anticipate its popularity so that you can put more effort into those strategies that will be successful for your business.

Data science is essential for every company’s growth. It can help improve business strategies in order to keep pace with the competition in the market. A good example would be Walmart who predicted that people would look for organic products when they purchase groceries. They used machine learning to be able to develop their own organic products that could also be sold in their shops. This allowed them to have instant access to new customers who are interested in buying fresh produce without having to spend a lot of money on it.

Hire related Fintalents

Hire the best Data Science and analysis specialists in 2,900+ industries

Fintalent is the invite-only community for top-tier M&A consultants and Strategy talent. Hire global Data Science and analysis consultants with extensive experience in over 2,900 industries. Our platform allows you to build your team of independent Data Science and analysis specialists in 48 hours. Welcome to the future of Mergers & Acquisitions!

Why should you hire Data Science and analysis experts with Fintalent?

Trusted Network

Every Fintalent is exclusively invited and vetted.

Ready in 48h​​​

Hire efficiently. Your M&A team is ready in 2 days or less.​​​​

Specialized Skills​

Fintalents are best-in-class - and specialized in 2,900+ industries.​

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We guarantee highest integrity and ethical principles.​​​

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Frequently asked questions

Most frequent questions and answers

What clients usually engage your Data Science and analysis Consultants?

We work with clients from all over the world. Our clients range from enterprise and corporate clients to companies that are backed by Private Equity or Venture Capital funds. Furthermore, we work directly with Family Offices, Private Equity firms, and Asset Managers. Most of our enterprise clients have dedicated Corporate Development, M&A, and Strategy divisions which are utilizing our pool of Data Science and analysis talent to add on-demand and flexible resources, expertise, or staff to their in-house team.

How is Fintalent different?

Fintalent is not a staffing agency. We are a community of best-in-class Data Science and analysis professionals, highly specialized within their domains. We have streamlined the process of engaging the best Data Science and analysis talent and are able to provide clients with Data Science and analysis professionals within 48 hours of first engaging them. We believe that our platform provides more value for Corporates, Ventures, Private Equity and Venture Capital firms, and Family Offices.

Our Hiring Process – What do ‘Community-Approach’ and ‘Invite-to-Apply’ mean?

‘Invite-to-Apply’ is the process by which we shortlist candidates for the majority of projects on our platform. Often, due to the confidential nature of our clients’ projects, we do not release projects to our whole platform but using the matching technology and expertise of our internal team we select candidates who are the best fit for our clients’ needs. This approach also ensures engagement with our community of professionals on the Fintalent platform, and is a benefit both to our clients and independent professionals, as our freelancers have direct access to the roles best suited to their skills and are more likely to take an interest in a project if they have been sought out directly. In addition, if a member of our community is unavailable for a project but knows someone whose skill set perfectly fits the brief, they are able to invite them to apply for the role, utilizing the personal networks of each talent on our platform.

Which skills and expertise do your Fintalents have?

The Fintalents are hand-picked and vetted Data Science and analysis professionals, speak over 55 languages, and have professional experience in all geographical markets. Our Data Science and analysis consultants’ experience ranges from 3+ years as analysts at top investment banks and Strategy consultancies, to later career C-level executives. The average working experience is 6.9 years and 80% of all Fintalents range from 3-12 years into their careers.

Our Data Science and analysis consultants have experience in leading firms as well as interfacing with clients and wider corporate structures and management. What makes our Data Science and analysis talent pool stand out is the fact that they have technical backgrounds in over 2,900 industries.

How does the screening and onboarding of your Data Science and analysis talent work?

Fintalent.io is an invite-only platform and we believe in the power of referrals and a closed-loop community. Members of our community are able to invite a small number of professionals onto the platform. In addition, our team actively scouts for the best talent who have experience in investment banking or have worked at a global top management consultancy. All of our community-referred talent and scouted talent are subject to a rigorous screening process. As such, over the last 18 months totaling more than 750 hours of onboarding calls, of which only 40% have received an invite-link after the call.

What happens if I am not satisfied with my Data Science and analysis consultant’s work?

During your initial engagement with a member of our Fintalent talent pool with no risk. If you are not satisfied with the quality of your hire for any reason then we are able to find a replacement at short notice. There is no minimum commitment per project, but generally projects last at least 5 days and can last 12+ months.

Interested in our invite-only community of tier-1 Data Science and analysis experts?

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