Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.

Why Data Science is Important?

More and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.

DATA SCIENCE SKILL SET

Like most careers, the more advanced your position, the greater suite of skills you’ll need to be successful. However, when looking at becoming a Data Scientist, there are certain skills you’ll need to be proficient in regardless of your role.

1. Math and Statistics - Any good Data Scientist is going to have a strong foundation built on both math and statistics. Any business, especially those that are data-driven, will expect a Data Scientist to understand the different approaches to statistics — including maximum likelihood estimators, distributors, and statistical tests — in order to help make recommendations and decisions. Calculus and linear algebra are both key as they’re both tied to machine learning algorithms.
2. Analytics and Modelling - Data is only as good as the people performing the analytics and modelling on it, so a skilled Data Scientist is expected to have high proficiency in this area. Based on a foundation of both critical thinking and communication, a Data Scientist should be able to analyse data, run tests, and create models to gather new insights and predict possible outcomes.
3. Machine Learning Methods - While having expert level knowledge in this area isn’t always necessary, a level of familiarity will be expected. Decision trees, logistic regression, and more are key elements that machine learning enables and potential employers will be looking for these skills.
4. Programming - To move from the theoretical into creating practical applications, a Data Scientist needs strong programming skills. Most businesses will expect you to know both Python and R, as well as other programming languages. Object-oriented programming, basic syntax, and functions, flow control statements as well as libraries and documentation all fall under this umbrella.
5. Data Visualization - Data visualization is a key component of being a Data Scientist as you need to be able to effectively communicate key messaging and get buy in for proposed solutions. Understanding how to break down complex data into smaller, digestible pieces as well as using a variety of visual aids (charts, graphs, and more) is one skill any Data Scientist will need to be proficient in order to advance career-wise. Check out our Creating Data Visualizations with Tableau post to learn more about Tableau and why data visualization is so important.
6. Intellectual Curiosity - At the heart of the data science role is a deep curiosity to solve problems and find solutions — especially ones that require some out of the box thinking. Data on its own doesn’t mean a whole lot, so a great Data Scientist is fuelled by a desire to understand more about what the data is telling them, and how that information can be used on a broader scale.
7. Communication - Data doesn’t communicate without someone manipulating it to be able to do so, which means an effective Data Scientist needs to have strong communication skills. Whether it’s disseminating to your team what steps you want to follow to get from A to B with the data, or giving a presentation to business leadership, communication can make all the difference in the outcome of a project.
8. Business Acumen - For a Data Scientist to effectively use data in a way that’s meaningful to their employer, there’s a level of business acumen that’s required. You need to fully understand the key objectives and goals of the business and how it impacts the work you’re doing. Also, you also have to be able to create solutions that meet those goals in a way that’s cost-effective, easy-to-implement, and ensures broad adoption.

Life Cycle of a Typical Data Science Project Explained:

1. Business Understanding - Understanding the business or activity that your data project is part of is key to ensuring its success and the first phase of any sound data analytics project. To motivate the different actors necessary to getting your project from design to production, your project must be the answer to a clear organizational need. Before you even think about the data, go out and talk to the people in your organization whose processes or whose business you aim to improve with data.
2. Get Your Data - Once you’ve gotten your goal figured out, it’s time to start looking for your data, the second phase of a data analytics project. Mixing and merging data from as many data sources as possible is what makes a data project great, so look as far as possible.
3. Explore and Clean Your Data - The next data science step is the dreaded data preparation process that typically takes up to 80% of the time dedicated to a data project. Once you’ve gotten your data, it’s time to get to work on it in the third data analytics project phase. Start digging to see what you’ve got and how you can link everything together to achieve your original goal. Start taking notes on your first analyses and ask questions to business people, the IT team, or other groups to understand what all your variables mean.
4. Enrich Your Dataset - Now that you have clean data, it’s time to manipulate it in order to get the most value out of it. You should start the data enrichment phase of the project by joining all your different sources and group logs to narrow your data down to the essential features.
5. Build Helpful Visualizations – You now have a nice dataset (or maybe several), so this is a good time to start exploring it by building graphs. When you’re dealing with large volumes of data, visualization is the best way to explore and communicate your findings and is the next phase of your data analytics project.
6. Get Predictive - The next data science step, phase six of the data project, is when the real fun starts. Machine learning algorithms can help you go a step further into getting insights and predicting future trends. By working with clustering algorithms (aka unsupervised), you can build models to uncover trends in the data that were not distinguishable in graphs and stats. These create groups of similar events (or clusters) and more or less explicitly express what feature is decisive in these results.
7. Iterate, Iterate, Iterate – The main goal in any business project is to prove its effectiveness as fast as possible to justify, well, your job. The same goes for data projects. By gaining time on data cleaning and enriching, you can go to the end of the project fast and get your initial results. This is the final phase of completing your data analytics project and one that is critical to the entire data life cycle.

Why do we need data science?

Data science is changing the course of the action plan of business procedures and plans. It is solving intricate problems with the help of technology. Data science provides the inside knowledge, which is derived from the big data after processes of extraction and the information. This information is sometimes collected from the ongoing sources within the system, and most of the time, it is mined from external sources.

Data is the key component for every business, as businesses need it to analyse their current scenario based on past facts and performance and make decisions for future challenges. They need data to survive in today’s competitive market and mature their decision-making power, which would enhance their productivity and profitability. Today, data science is the requirement of every business to make business forecasts and predictions based on facts and figures, which are collected in the form of data and processed through data science.

The influence of data science on business will be based on the understanding of the critical information given as input to the system because the decisions are based on those results. Data science provides resolutions for challenges that are crucial for business decisions as the future of the business is based on them. Data science is important for marketing promotions and campaigns as it has offered the essence of needs and wants in the form of trends and consumer behaviour’s in a competitive market at the right time for the right consumer.

Benefits of DATA SCIENCE for your business

Data Science is not a seven-headed bug. In reality, one of the main functions of a data scientist is to study and structure your business data so that you can extract more accurate insights from your company. There are many benefits of Data Science to a business. That is why we decided to bring, in today’s blog post, the top 6. Keep reading and learn about them.

Increases business predictability

When a company invests in structuring its data, it can work with what we call predictive analysis. With the help of the data scientist, it is possible to use technologies such as Machine Learning and Artificial Intelligence to work with the data that the company has and, in this way, carry out more precise analyses of what is to come.

Thus, you increase the predictability of the business and can make decisions today that will positively impact the future of your business.

Ensures real-time intelligence

The data scientist can work with RPA professionals to identify the different data sources of their business and create automated dashboards, which search all this data in real-time in an integrated manner. This intelligence is essential for the managers of your company to make more accurate and faster decisions.

Favours the marketing and sales area

Data-driven Marketing is a universal term nowadays. The reason is simple: only with data, we can offer solutions, communications, and products that are genuinely in line with customer expectations. As we have seen, data scientists can integrate data from different sources, bringing even more accurate insights to their team. Can you imagine obtaining the entire customer journey map considering all the touchpoints your customer had with your brand? This is possible with Data Science.

Improves data security

One of the benefits of Data Science is the work done in the area of ​​data security. In that sense, there is a world of possibilities. The data scientists work on fraud prevention systems, for example, to keep your company’s customers safer. On the other hand, he can also study recurring patterns of behavior in a company’s systems to identify possible architectural flaws.

Helps interpret complex data

Data Science is a great solution when we want to cross different data to understand the business and the market better. Depending on the tools we use to collect data, we can mix data from “physical” and virtual sources for better visualization.

Facilitates the decision-making process

Of course, from what we have exposed so far, you should already imagine that one of the benefits of Data Science is improving the decision-making process. This is because we can create tools to view data in real-time, allowing more agility for business managers. This is done both by dashboards and by the projections that are possible with the data scientist’s treatment of data. As you can see, Data Science is the solution for your company to become more efficient in this digital age. The most incredible thing is that the benefits of Data Science that we mentioned so far are just an example.

In-Demand Data Science Careers

Data science experts are needed in virtually every job sector—not just in technology. In fact, the five biggest tech companies—Google, Amazon, Apple, Microsoft, and Facebook—only employ one half of one percent of U.S. employees. However—in order to break into these high-paying, in-demand roles—an advanced education is generally required.

“Data scientists are highly educated–88 percent have at least a master’s degree and 46 percent have PhDs–and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist,” reports KDnuggets, a leading site on Big Data.

Here are some of the leading data science careers you can break into with an advanced degree.

Data Scientist

Average Salary: $139,840

Typical Job Requirements: Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are much more technical.

Machine Learning Engineer

Average Salary: $114,826

Typical Job Requirements: Machine learning engineers create data funnels and deliver software solutions. They typically need strong statistics and programming skills, as well as a knowledge of software engineering. In addition to designing and building machine learning systems, they are also responsible for running tests and experiments to monitor the performance and functionality of such systems.

Machine Learning Scientist

Average Salary: $114,121

Typical Job Requirements: Research new data approaches and algorithms to be used in adaptive systems including supervised, unsupervised, and deep learning techniques. Machine learning scientists often go by titles like Research Scientist or Research Engineer.

Applications Architect

Average Salary: $113,757

Typical Job Requirements: Track the behavior of applications used within a business and how they interact with each other and with users. Applications architects are focused on designing the architecture of applications as well, including building components like user interface and infrastructure.

Enterprise Architect

Average Salary: $110,663

Typical Job Requirements: An enterprise architect is responsible for aligning an organization’s strategy with the technology needed to execute its objectives. To do so, they must have a complete understanding of the business and its technology needs in order to design the systems architecture required to meet those needs.

Data Architect

Average Salary: $108,278

Typical Job Requirements: Ensure data solutions are built for performance and design analytics applications for multiple platforms. In addition to creating new database systems, data architects often find ways to improve the performance and functionality of existing systems, as well as working to provide access to database administrators and analysts.

Infrastructure Architect

Average Salary: $107,309

Typical Job Requirements: Oversee that all business systems are working optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy.

Data Engineer

Average Salary: $102,864

Typical Job Requirements: Perform batch processing or real-time processing on gathered and stored data. Data engineers are also responsible for building and maintaining data pipelines which create a robust and interconnected data ecosystem within an organization, making information accessible for data scientists.

Business Intelligence (BI) Developer

Average Salary: $81,514

Typical Job Requirements: BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems.

Statistician

Average Salary: $76,884

Typical Job Requirements: Statisticians work to collect, analyze, and interpret data in order to identify trends and relationships which can be used to inform organizational decision-making. Additionally, the daily responsibilities of statisticians often include design data collection processes, communicating findings to stakeholders, and advising organizational strategy.

Data Analyst

Average Salary: $62, 453

Typical Job Requirements: Transform and manipulate large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing. Data analysts also aid in the decision-making process by preparing reports for organizational leaders which effectively communicate trends and insights gleaned from their analysis.

Summary

In the end, it won’t be wrong to say that the future belongs to the Data Scientists. It is predicted that by the end of the year 2018, there will be a need of around one million Data Scientists. More and more data will provide opportunities to drive key business decisions. It is soon going to change the way we look at the world deluged with data around us. Therefore, a Data Scientist should be highly skilled and motivated to solve the most complex problems.

Check out our Data Science certification training here, that comes with instructor-led live training and real-life project experience.