Data are facts, numbers, or texts that can be processed by a computer. Data is collected everywhere - through smartphones, computers, and in ways that you may not even be aware of. For example, when you write Gmail emails or search for things using Google’s search engine, Google uses your email content and search terms to mine the internet for information that may interest you.
Big Data is a large volume of structured and unstructured data that is essentially too large for traditional computer systems and business tools to handle. Big data is important to companies because it provides them with loads of historical and real-time information to analyze to understand corporate attributes and performance. The learnings derived from these analyses can ultimately drive corporate decision-making. Hopefully, the enhanced decision-making leads to new competitive advantages and sustains old ones.
Data science combines tools from statistics, machine learning, software engineering, data engineering, domain expertise, and “story telling” to extract useful knowledge from data. In a business context, data science is used as part of a process we refer to as data-driven decision making; the art of turning data into actions and shifting between deductive (hypothesis-based) and inductive (pattern-based) reasoning which is a fundamental change from traditional analytics approaches.
As we move into the data economy, data science is the competitive advantage for organizations interested in winning. Data-driven companies have realized a:
Anyone who either uses data to make decisions or should be using data to make decisions. Most analytical and strategic roles within organizations require data-driven decision making thus data science training can greatly benefit those individuals within those roles.
The first reason why business professionals need to learn learn data science is to improve their corporate decision making. Data-driven decision making has been shown to greatly improve corporate performance.
Data is one of the most underutilized corporate assets. According to the McKinsey Global Institute, the amount of data in our world has been exploding, and analyzing large data sets will become a key basis of competition and growth for individual firms. From the standpoint of competitiveness and the potential capture of value, there is a need for all companies to take big data seriously.
According to Harvard Business Review article An Introduction to Data-Driven Decisions for Managers who Don’t Like Math, companies in the top third of their industry that utilize data-driven decision making are, on average, 6% more profitable than their competitors. This finding is statistically significant and has led to measurable increases in stock market valuations. In addition, companies that employ data-driven decision making exhibit higher corporate productivity. When organizations increase data usability by 10%, there is a 17-49% increase in productivity in the organization.
Obtaining practical data science skills will help business managers become better decision makers by:
Data science in business is applied problem solving in the context of business problems. It allows managers to see problems from different angles given the ocean of available data therefore enhancing creative-problem solving strategies.
To effectively manage teams and lead with confidence, business managers need to be comfortable with handling data. They need to understand what data is actually useful for the company, what good data analytics looks like, where data analytics can specifically add value to the business, and which business questions data science can and cannot answer. Managing teams with this analytical perspective does not require that business managers have PhDs in mathematics and computer science. Instead, they need to have an appreciation and desire to become more data-analytically minded; they need to adopt a data mindset. The first step in becoming a data-analytically minded business manager is to get one’s hands dirty with data.
In addition, data science helps transform managers into organizational leaders. Such leaders have a unique opportunity to build new business models, grow businesses, and spark disruptive innovation and make data analytics an important part of the organization’s DNA.
Business managers need to learn how to “speak tech” to be able to better communicate with technical teams and work collaboratively with them on achieving company goals. Having practical data science and programming experiences will allow business managers to have more productive and meaningful conversations with their company’s technical teams. These communications will also help business managers put together more reasonable project timelines and more effectively get to the bottom of why certain business problems are happening. Managers who have good business intuition but also speak the language of technical personnel intelligently are rockstars!
Also, data science helps business managers to better “story tell” performance to key corporate stakeholders since data science helps business managers develop a three-hundred and sixty degree perspective on business performance.
Instead of simply relying on technical reports and results being correct, business managers can add value to the process by analyzing these results and providing meaningful business perspectives on them to technical teams.
According to Professor Susan Athey from Stanford’s Graduate School of Business, business managers often find that if they cannot directly engage in understanding and evaluating [technical] output from analysts they are left out or left behind.
By learning data science and programming languages such as Python, business managers can write scripts that execute time intensive, repetitive tasks more quickly and efficiently than manually doing them.
In conclusion, learning data science skills is a high ROI investment for individuals given the plethora of benefits received versus the low monetary and time costs of obtaining such skills.
The second reason why business professionals need to learn data science is because they have to! The amount of data produced these days is unprecedented, and there is a shortage of professionals who are capable of drawing meaningful value from such data.
Every day, we create 2.5 quintillion bytes of data - so much that 90% of the data in the world has been created in the last two years alone. Given the vast amount of available data, more professionals, both business and technical, need to actively engage with the data since they can provide tremendous value across different corporate functions. Fundamental data science skills are becoming more of a necessity for business managers instead of a “nice to have”.
According to the McKinsey Global Institute, there will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.
Clearly, it is in the interest of business managers to constantly learn new technical skills that the world demands today and in the future.
Data science helps a wide range of business and nonbusiness professionals ranging from marketing professionals to medical doctors.Business:
Data Science is often used to help answer pivotal strategy, operations, and human resources questions. From a strategic standpoint, data science can help questions about customer attributes, new products/markets, predicted statistics such as revenues and/or costs, customer groups, classifications, and many other questions. In terms of operations, data science is often used to answer optimization and efficiency questions. Such questions include: how can processing times be reduced, how can delivery times be improved, etc. From a human resources standpoint, data science is used for hiring processes - specifically selecting candidates to interview and for employee development/support purposes. By analyzing data and using the findings to drive decision-making, HR managers can allocate resources efficiently to help reduce employee turnover and improve absenteeism and presenteeism issues.Finance:
Data Science is used in many areas of Finance ranging from trading to risk management. Some of the more interesting uses include sentiment analysis, asset pricing, economic forecasting, and loan decisions (including loan amounts).Medicine:
There are two main areas where data science is used: prescriptions and operations. Data science is often used by doctors to determine the best prescriptions for patients. If a doctor has past patient prescription data points labeled across specific demographics, he/she can analyze these data points to help make better prescription decisions for current patients. In terms of operations, data science is often used in the same way as it is used in business operations. Questions include: how can a doctor optimize appointment times, how can Emergency Rooms minimize patient processing time, etc.Politics/Public Policy:
Data Science is often used as a method for allocating precious campaign resources. For example, President Obama’s past presidential elections have been known for their uses of data science to effectively target neutral voters instead of targeting everyone which is the strategy of traditional campaign approaches. Many believe that these effective uses of data science ultimately helped President Obama secure victories in his elections. On a policy side, data analytics is often the driver of why certain policies are created and prioritized in government in the first place.Public Health:
Have you ever wondered how vaccines for viruses such as influenza are created? The basis for the vaccine is data science! What this means, simply, is that public health professionals map diseases using sophisticated data science-based models to determine how viruses such as influenza will change year after year. Knowing this with some degree of confidence, drug manufacturers can offer vaccines that target the most likely strands for that year. Such vaccines have saved lives and prevented outbreaks.Education:
Data science is transforming the education space in so many ways, but one of the most important ways is adaptive learning. Instead of students being taught the same concepts in the same ways at the same pace, teachers can now customize content and delivery according to how students learn best and at the right pace - thus providing a whole new level of differentiated instruction. What does this ultimately mean? Students learning more and at deeper levels! And, this is so exciting given that education is a key ingredient in innovation.Law:
It is no secret that lawyers are "Gatekeepers of Data". The standard image we all have in our minds is lawyers in large rooms with boxes of papers stacked to the top as they solve cases. Given the advancement of technology, most of these files are now digitized. So, how are data science and law even in the same sentence? Because lawyers are starting to use data science to quickly find the right information in support of cases that they are preparing. In fact, this is a very new development in the field and many legal tech companies are trying to create technologies to make these fact finding processes seamless for lawyers.Sports:
Teams that do not have deep purses are still able to effectively compete with deep purse teams. How? The right mix of coaching, strategy, and data analytics. Winning is not just about putting the "best" or "most athletic" players on the field - especially when it comes to team sports. Coaches need to think about all sorts of data points ranging from weather to player health. Deep analytics can help uncover who should be playing when, what formation makes sense, and can even drive specific play-calling.Academia / Research:
Professors and researchers have to deeply analyze data in all shapes and forms. Using a combination of programming and smart data science strategies, professors and researchers can go through collected data more quickly to determine if the data is useful and draw meaningful conclusions for academic paper purposes.Your Personal Life:
Do you ever wonder how Netflix knows what movie to recommend you next? Or, how Amazon recommends you products? Or, how Target knows what coupons to send you in the mail? The short answer is data science. These companies analyze your search and preference patterns and match them against other similar users/customers to make determinations about what you may like.
As you can see, data science is the foundation for so many things in our lives and many more not even covered in this answer. If you are in a decision-making capacity and work with data or are just curious about how certain machine-learning & artificial intelligence technologies work, learning data science will benefit you.
To leverage the full potential of data science, you need both. Commercial software tools such as Tableau and Excel are well suited for certain tasks; however, their interfaces and functionalities might be limiting when working on other, especially more complex business-related, tasks.
Understanding the basic data science methods and how to implement them using programming languages such as Python or R gives you greater analytical flexibility and can expedite data analytics tasks. Both programming languages are free and have proven to be powerful tools for data analysis and provide access to a myriad of effective data science libraries.
In the end, to perform data analyses effectively, you will find yourself using several complementary tools and methods instead of working with a single off-the-shelf solution.
Let’s do a back-of-the-envelope cost-benefit analysis and find out if data science training is worth the cost! This brief analysis will not take into consideration opportunity cost and time because such costs are very hard to quantify. It is also important to note that this analysis assumes that data science enhances and improves decision making and “better” decisions improve corporate bottom lines. Also, this analysis focuses only on the Fortune 500, the five hundred most profitable US industrial corporations each year, given readily available data, so it may not be fully applicable to your specific company.
The first step in this analysis is to calculate the average profit of a Fortune 500 company. This is $1.89 billion ($945 billion / 500).
The second step is to calculate the average number of employees in a Fortune 500 company which is 53,600 (26.8 million / 500) and to calculate the number of employees involved in business data-driven decision making. This is ~10,720 (53,600 * 20%).
The third step is to calculate the average performance improvement dollar value that data driven decision making provides the average Fortune 500 company. This is $103.95 million ($1.89 billion * 5.5%).
This implies that each employee contributes ~$9,697 of performance improvements for the average Fortune 500 company ($103.95 million / 10,720 employees).
Let’s build in an error component of $2,697 per employee contribution to provide even more credibility to this back-of-the-envelope analysis. If training costs $2,000 for a two-day data science course and employees contribute ~$7,000 in performance improvements to the company, is data science training worth it?
We would say most definitely yes given the potential return on investment.
You can certainly purchase data science and spreadsheet tools such as Microsoft Excel and Tableau to assist with certain data science tasks, but the good news is that you do not have to purchase these tools to conduct robust, value-adding data analyses!
Instead, you can use free programming languages such as Python or R and their powerful data science libraries. Given some initial training, you can effectively and efficiently apply these powerful technologies to your business problems.
All! Every industry has data available to analyze therefore data science skills can benefit individuals who work in these industries. Deciding whether to learn data science is not so much a function of what industry one works in; instead, it is more of a function of whether one currently uses data to make decisions or if one could perform better in his/her job by utilizing data-driven decision making.
If you fall into one of the two buckets just discussed, you would benefit from learning data science.
Programming is the implementation of computer science theory into practice. Code is the tangible solution to a problem or need. The programming itself is not the main benefit of learning how to code; the main benefit is the enhanced problem solving abilities that result after learning computer programming. Learning how to program requires deeply thinking about the complex problem, breaking it down into relevant parts, and reflecting on how to efficiently solve each of the problem subparts using clever algorithms and functions. It is this algorithm design process which is extremely beneficial to professionals as they seek out new ways to enhance their problem solving skills.
Furthermore, programming helps professionals by:
Lastly, learning computer programming can help business & finance professionals better execute certain business tasks such as financial modeling because the thinking required for both computer programming and financial modeling is very similar.
When it comes to handling large and complex data sets, the Python programming language has proven to be a powerful and free tool for data science. With a myriad of advanced data science libraries, Python is often found to be more flexible than Microsoft Excel and other commercially available software, especially when it comes to analyzing larger amount of data. Its effective prototyping capabilities and global community of programmers make Python one of the most commonly used languages for solving data science tasks.
Furthermore, we recommend learning data science with Python initially because:
It is also important to note that programming and data science concepts learned through Python will also apply to other common data science programming languages such as R. There will be new syntax to learn when switching from Python to R, but for most people, the learning curve is far less steep than when learning these concepts the first time around.
Lastly, it is important to highlight that data science languages such as R are also fantastic - we do not want to imply in any way that R is inferior to Python. When you gain enough proficiency in data science, the real answer is to use tools such as Python, R, and Excel together for maximum analytical and data visualization horsepower.
We do not assume that participants have any prior computer programming or data science experiences when signing up for our data science courses.
However, it is important to note that even if you have computer programming and/or data science backgrounds you may still find Cognitir courses to be helpful as refreshers on practical data science applications.
Cognitir is a unique training company in that it provides specialized technical training courses for business, finance, and nonbusiness professionals as well as students. This is very different to other online training providers, which offer thousands of various and unrelated training courses with the aim of maximizing their audience reach. For these other online training providers, the goal is to maximize the number of online users. Cognitir believes in offering the best and most relevant training courses for its specific target audiences. As a result of this focus, Cognitir has quickly risen to being a leading, global provider of technical training courses for business, nonbusiness, & finance professionals and students.
It is a good question. The term “Big Data” is a fad. Certain companies and even governments have been using immense amounts of data years before the term “Big Data” was coined. However, the importance of data science in business and technology is far from being a trend. Machine-learning based technologies are the future of technology as we are already seeing many tasks which are automated by software and robots.
It is important to recognize that the amount of data collected today is unprecedented. As new technologies continue to develop even more data will be collected and real strategic advantages can be created if companies and individuals learn to smartly use the collected data. To be able to use this collected data in value-adding ways, individuals first need to have fundamental data science training, which covers everything from investing in the right data sources to applying the appropriate data science techniques on the specific data.