Yes data science is a rising career if you look at a career in terms of job trends in relation to growth. Stats show that the US could potentially have as many as 250,000 open data science jobs by 2024 and that the data science skills gap will mean that companies are scrambling to train or hire talent in the coming years. The reasons why data science is such a fast growing job are due to the following reasons:

The amount of data has skyrocketed. Due to the fact that the amount of data has grown exponentially, so has the need for people that can read and analyze it. Data driven decisions are more profitable. Ultimately for many companies and organisations, data is not useful unless it is beneficial which it certainly is in different ways. Data not only helps companies make better decisions but those decisions also usually come with some financial gain. A study by the Harvard Business Review found that companies in the top third of their industry in the use of data-driven decision making were on average 5 percent more productive and 6 percent more profitable than their competitors. A company does not have to rely on the decision makers intuition or gut instinct, data allows for decision making based on the numbers alone. If data can make companies more profitable and competitive in the market, that is definitely a reason why more companies hire employees that can interpret the data and put it in layman’s terms, so that the team can understand how to move forward. As bootcamps and online programs try to fill this gap, businesses may find themselves competing for talent in the coming years.

Yes data science is a rising career if you look at a career in terms of job trends in relation to growth. Stats show that the US could potentially have as many as 250,000 open data science jobs by 2024 and that the data science skills gap will mean that companies are scrambling to train or hire talent in the coming years. 

Is data science really a rising career?

In order to draw the immediate attention of first-time visitors, the home page should be well-designed and should provide a good user experience. Since your business communicates with the visitors visually, there are certain factors, such as fonts, symmetry, colour, text count, structure, spacing, etc., which one needs to keep in mind as this helps create an impact. The home page design is about designing and placing the header, footer, content, logo and call-to-action in the right position to guide visitors to find the required information quickly and easily.

Things to consider when designing Your Homepage: The homepage is the first page that visitors land on when they visit a company’s website. So it is essential to create an attractive and informative homepage to make people understand your business and your services. A well-designed website will entice potential customers to buy your product or service. Your homepage design should be easy to navigate for users, which creates a better user experience.

Use the Same Name for Domain and Company: The domain name helps the consumers to check out your website, so it is crucial to choose one that helps them remember without any difficulty. There are certain things that one needs to follow while creating a domain name, such as

? Using keywords for your domain name ? Keep the domain name short ? Use simple words which are easy to spell and remember ? Choose words that show uniqueness for branding purposes and avoid hyphens ? Research thoroughly before choosing a domain name ? Consult or seek help from domain name generators.

So your domain name plays an important role in making a memorable impression on visitors who visit your website for the first time. Choose or come up with the domain or company name that is relevant to your industry rather than unrelated words in the name of unique. Using the same name for both your domain and company name will help increase the credibility of your company which leads to more conversion.

Power of Logo and Taglines: A logo acts as the bridge between your business and customer, which delivers your company’s mission and message to the people. Ensure to incorporate a logo with minimalist and elegant design that creates an impact leaving a memorable place in their minds. A logo helps people identify your brand instantly within a few seconds, even if they tend to forget your company name. While designing your logo for your business, ensure that the design is something that people often encounter in their daily lives to make it memorable. Insert catchy business taglines along with your logo, which helps describe your company’s message to the consumers.

Navigation Bar is the Key: The navigation design for your website is vital because it makes your website user-friendly, which helps people navigate seamlessly to get the required information. If the navigation design of your website gives a good user experience, then users may choose to buy your product or service; the labels, patterns and structures of navigation design also play a crucial role. There are different types of navigation design, such as the standard horizontal menu, hamburger menu,scroll-triggered, mega menu, fixed or sticky menu and hover activated drop-down menu. Avoid patterns like hamburger menus because, by default, it hides the navigation and appears only when it is triggered.

The homepage design involves more factors besides attractive visual designs. It helps communicate to your existing customers and first-time visitors about your business and why they should choose your company among other competitors. The design should deliver your company’s purpose and message to the people to increase their interest and gain their trust, leading to an increased conversion rate.

In order to draw the immediate attention of first-time visitors, the home page should be well-designed and should provide a good user experience. Since your business communicates with the visitors visually, there are certain factors, such as fonts, symmetry, colour, text count, structure, spacing, etc.

The Puzzle of Home Page Design

Data Science has grown phenomenally over the last few years and with that, so have the variety of roles and responsibilities within the field. Sometimes, the creative names of job roles are hard to decipher, and it leaves us wondering exactly what our area of focus needs to be. This post explains some of the different areas of Data Science and the technical skills or knowledge you would typically need to be successful in the positions. Here are some of the common ones you would have come across.

You will find that most people working in data are referred to as a Data Scientist, mainly because it is such a broad remit. We’ve even seen terms such as “Citizen Data Scientist” become popularised which simply means people who aren’t trained or qualified in the field but have taught themselves many of the applications.

A true Data Scientist is someone who can master several skills right through from working with raw data, using statistical techniques via data programming tools like Python or R and is able to present all those insights in a simple way to a wider business. The main functions tend to be machine learning, Big Data technology, predictive statistics, recommender systems and distributed computing (computers on numerous networks or disparate data). Big companies like Google and Facebook are famed for hiring many Data Scientists for their mass of complex algorithms that feed their businesses.

A Data Analyst will take the work done by the Data Scientist and perform deeper analytics with it using platforms like R, Python, SQL, VBA (Excel). The focus of a Data Analyst is to work out why a number has happened rather than traditional roles which might have simply presented the results as management information. For example, if a retail business sells 10 products on a Monday and the previous Monday, they sold 20, a Data Analyst will work to find the root cause of the difference between the two days and feed that back in to the relevant teams.

Often, a Data Analyst will display their findings within reporting tools such as Qlik, Tableau or Sisense to name a few.

A Data Engineer will be responsible for preparing the infrastructure that the Data Analysts and Scientists will be using. The models built by these roles are only as good as the data that feeds them, and a Data Engineer will ensure such governance of that via Big Data technologies, ETL processes and complex queries leading towards a “single source of truth.”

Data Engineers will not usually know machine learning or statistical methods but focus solely on the design and the architecture of the datasets. A great example of this is where businesses have several disparate data sources. Most probably have a website, a back-office system, finance system, telephony system, Google Analytics, Facebook Ads, Marketing platform, HR system, payments systems, email exchange, live chat or chat bots and perhaps even an App. The Data Engineer will work to get all of these into a single data warehouse that everybody else can use and gather trust in the work the rest of the team are doing.

This role is hot property right now and arguably the most sought after. A Data Developer is a bit like a mix of a Data Scientist and a Data Engineer. Where a Data Scientist is mainly focused on building statistical models and algorithms, the Data Developer will look to build products that turn these into full business solutions.

The Data Developer will gradually take data from different models, get some insights from teams and analysts whilst incrementally creating solutions that are able to deploy the work they are doing. So, ultimately, whilst the developer knows about engineering, machine learning and architecture, they look to create business value solutions using data. The exact context of this differs between businesses but it is a position where algorithms and machine learning can actually by implemented into business as usual processes.

A Data Manager will usually be an experienced data professional with a more strategic view of how data can be leveraged as a business asset for commercial use. They will understand all the different roles we’ve already spoken about, without necessarily being a master of any, in order to direct the team and promote a data culture both inside and outside of the organisation.

A role like this is incredibly important as Data Science projects are only useful if they have a practical or commercial purpose that provides a real-world benefit. For example, what if somebody in a Marketing team asked the Data Scientist to spend time building an algorithm that predicts which country the next customer will come from. It might be a nice fun little tool but serves no practical purpose to the business. A Data Manager will ensure that time is used effectively, and any projects are aligned to business revenue or customer experience.

What are Big Data and Machine Learning anyway?

All these roles fall into the world of Big Data and Machine Learning. These are both very broad terms that are subsets of artificial intelligence but they involve using machines to take data and automate tasks without the need for manual human intervention. Therefore, they tend to be used as buzzwords within most data roles, being so far reaching and applicable to virtually every project.

There are various other role titles coming out of the woodwork as the field of data continues to develop including Data Modeller, Data Architect, Data Evangelist, Data Artist or even a few Data Ninjas! However, all of these relate back to the core functions talked about in this article and they are the areas it is important to have a working knowledge of to progress in the field.

The difference between Data Analytics, Data Analysis, Data Mining, Data Science and Machine Learning.
Data Science has grown phenomenally over the last few years and with that, so have the variety of roles and responsibilities within the field.

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Is data science really a rising career?

The Puzzle of Home Page Design

Data Science and Machine Learning jobs