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Want to get started with Data Science? After reading this guide you’ll know what it is, why it’s important, where it’s used, the steps involved, the role of machine learning, and how Data Science is transforming businesses.Get Started Now
Data Science turns raw data into accurate predictions, so you can make high-impact decisions that lead to competitive advantage. And with our lives increasingly driven by data, using it has become a focal point for organizations of all types.
Traditional business intelligence is nothing new, but it only tells you what a problem is, how it happens, how often and where. Data Science reveals the source of a problem and what the future holds. And that’s kind of like holding the winning lottery ticket.
The raw material of Data Science is Big Data. Defined by the seven V’s, this includes volume, variety, velocity, value, variability, veracity, and visualization.
Visualization, which is what Data Science is most known for, involves the use of sophisticated graphs such as pie charts, bar graphs, time charts, line graphs, histograms, scatter plots and others. These can be integrated to analyze various data sets, giving you fresh and valuable insights, even in real-time. For example, you could know the implications of real-time customer purchases.
The type of analysis that can reveal the future, is known as Predictive Analytics. And with Google and Amazon routinely using it, everyone is not far behind. It’s no secret then, that the future of Data Science is now.
Ninety percent of the world’s data was created in only the last two years, and the digital revolution is happening as you read this. The amount of data being generated daily is a staggering amount. 2.5 quintillion bytes! That’s enough pennies to cover the surface of the earth twice a day.
The US alone creates 2,657,700 gigabytes of Internet data every minute. For every minute you spend reading this article, YouTubers will have watched 4.14 million videos.
Further, Netflix members viewed 69,444 hours of shows, 154,200 Skype calls were made, 45,787 Uber trips were taken, Twitter sent 456,000 tweets, Instagram added 46,740 photos, there were 3.6 million Google searches, 600 new Wikipedia page edits were made, and 103,447,520 spam emails were sent.
While data like this can reveal business insights, most companies are completely unprepared for the challenge. If they were, they’d have tools for solving their most complex business problems. For example:
In some industries, Data Science is hot. In others, it’s very hot. For example, some of these include Healthcare, Finance, Retail/e-commerce, Non-profit, Logistics and People Management. And here are some of the data-driven challenges industries face and how Data Science can help solve them.
Most would agree that the healthcare industry is overpriced and inefficient. For example, many of the treatments people receive are based on inaccurate diagnosis, and of little help. Data Science can help physicians make better treatment decisions and recommend more effective preventative care. In the process, this will significantly reduce healthcare costs.
Explosive amounts of data available to the financial sector. And this enables companies to offer credit online with less risk, such as loans for start-up entrepreneurs. Further, companies can create a baseline for spending patterns, and identify when something abnormal happens to prevent fraud.
For both retail stories and Ecommerce, Data Science helps to understand customer better. This makes it easier to gauge creditworthiness. Further, it facilitates more accurate pricing which gives customers access to lower prices, and companies benefit from increased sales.
Nonprofit organizations need resources to achieve their missions. But to attract donors, they must prove their worth by showing their work is getting results. Data science can help non-profits acquire funds and do more good. In addition, it will help them make better informed decisions.
Logistics is an ongoing challenge with many variables to contend with. These include shifting demand, human error, traffic, fuel costs, and changes in the weather. Predictive logistic analytics can be applied to all these, enabling companies to experience less mechanical downtime, more efficient routes, happier customers and higher stock prices.
Finding the right people is the key to growing your business. And your success depends as much on your people as it does on your product. While it’s an ongoing challenge to recruit, hire, train, and manage them, you need the right people poised to take the right action at the right time. And Data Science can help.
Before you can get started Data Science, you must ask some key questions. What problems should we investigate? How will the data be collected? Which analytic tools will we use for data analysis? Here are six steps involved in a Data Science project:
The greater part of solving a problem is defining it. Then you must translate your questions about the data into something actionable.
Here you must determine what data you have, what you need and how you’re going to get it.
Your data may be structured, but it can still be messy. And the quality of your output depends the quality of your input.
This step where you search for the best ideas to test. And then decide what you think will turn into insights.
Now you’re ready to apply your knowledge of statistics, math, and technology to crunch the data and reveal some insights.
Start communicating your data:
Machine learning has an elevated status in Data Science. And this is because of the key role it plays in enabling machines to learn from the past and predict the future. By running the data through algorithms and applying statistical analysis, Data Scientists can predict an output value within an acceptable range. These techniques fall into three areas:
One of the greatest opportunities for leveraging Data Science is sales & marketing. For example, Google trends uses data science to reveal worldwide rates of searches showing you what’s trending for an industry or product.
And it helps your sales & marketing team describe customers in greater detail for better product availability, pricing, supply, and logistics. Further, advertisements can be positioned for better visibility, lead scoring helps close more sales, and customer lifetime value calculations can help direct marketing strategy, especially for early stage businesses. And Salesforce.com makes reaching these goals easier than ever.
Salesforce.com is the leading Customer Relationship Management software solution delivered through the web. Originally created to help companies manage phone calls, emails, meetings and social media, it now offers a wide range of business solutions. For example, it has artificial intelligence built-in.
A salesforce artificial intelligence strategy includes sales, service, marketing, and commerce. AI tools for service can help cross-sell and upsell at the right time, and their marketing tools deliver the best content, the commerce tools offer personalized recommendations. And it’s all driven by Salesforce Einstein.
Einstein is the Data Science component of Salesforce.com. It lets you discover insights, predict outcomes, and help customers get what they want. Predictive Lead Scoring reveals what leads will convert, Forecasting helps predict future value, like reaching quota, and Recommendations help online shoppers get more bang for their buck.
RTS Labs helps clients through every step of the Data Science development process. First, we help you define your most pressing problem or greatest opportunity. Next, we help you collect, clean and examine your raw data. Finally, we help you perform in-depth analysis and visualize your data for better decision-making.
If you’re already using Salesforce.com, you must have realized that buying the system is the easy part. Now it needs to be customized to your needs, with end-user training for a high adoption rate. Not only do we provide Data Science Consulting and Machine Learning development, we offer Custom Software Development, Analytics Development, and Salesforce development, all In-house.Free Data Science Consultation