Ultimate Guide To Learning Data Science
With increasing data available to different users and stakeholders, the need for storing large sums of it rose as time passed by. Hence, this need was identified and addressed by platforms and frameworks like Hadoop.
Solutions for data storage, however, resulted in focus shifts to data processing. Now that the relevant ‘people’ have collected and stored the raw data they think they need, the next step requires them to use it in a way that maximizes its value and contribution to their purpose and objectives. This is where the concept and principles of Data Science are applied to data reserves in creative ways in order to generate value for a business.
What Exactly Is Data Science?
This is a term that is commonly used in this modern era where rapid technological advancements are changing the way we deal with things in every aspect of our personal and work lives. Amidst all of this is Data Science, which can be considered to be the future of Artificial Intelligence. It is a well-integrated blend of several algorithms, tools and machine-learning principles with the aim of identifying and extracting relevant, unfolded patterns from pure (raw) data.
Confusions may have popped up in many minds regarding the role of Data Science and the one played by normal data analysts. The difference between the two approaches and techniques lies in the way they address and assess the data at hand-explaining/predicting.
Data Analysts are typically concerned with processing, ‘reading’ and giving out the history of data made available to them. Their skills and expertise confine their approach to just that. A Data Scientist, on the other hand, covers the area of explanatory analysis, just like a Data Analyst, but additionally uses several technically advanced machine-learning algorithms to carry out predictive analyses. A Data Scientist’s intellectual competency and skill set will enable them to view the provided data from every relevant angle and yield maximum value from the raw data.
We can claim that Data Science is key in using data effectively. Companies and businesses that are looking to enhance and refine their firm’s performance can certainly make use of this ‘technology’ to transition to being more ‘data-driven’.
How Is Data Science Used?
Data Science is used to make predictions and decisions by implementing techniques of ‘predictive casual analytics’, ‘machine learning’ and ‘prescriptive analytics’.
Prescriptive analytics has the potential of providing you with a model which possesses the intelligence and ability to make its ‘own’ decisions and modifying them to keep them in-line with the changing situations. This is a somewhat new field which is all about advising and ‘prescribing’. Simply put, in addition to predicting outcomes, prescriptive analytics suggests users a wide range of suitable actions (and reactions) according to the situation.
A very simple example of the use of such a model is Google’s self-driving vehicle. Data gathered and the algorithms driven from it can be useful in training self-driving cars by ‘coding’ them to be ‘intelligent’.
Predictive Casual Analytics
As the name suggests, predictive casual analytics has the potential of providing you with a highly functioning model that can predict the possibility and occurrence of particular events in the future. An example of this model’s application is the use of such a model in predicting the probability of a customer fulfilling his credit card payments in the future by analyzing that specific customer’s credit payment history.
In order to determine future trends as accurately as possible by studying data that is already available, a model based on machine-learning algorithms might just be your go-to tool. This can easily be categorized under the label of supervised learning as the data that is used to train machines and control their actions is the supervising element.
Machine-Learning (Pattern Discovery)
In case you do not have adequate and suitable data to make useful predictions, you still have the option of going about it with an alternative approach using an alternative algorithmic model. A machine designed for pattern discovery can assist you effectively in discovering and unfolding patterns which can eventually lead to meaningful and useful predictions.
This differs from the model discussed right above it in the sense that it is an ‘unsupervised’ version of machine-learning algorithms as no dataset is available beforehand.
The Essential Skillset for Data Science
When we defined Data Science earlier, we used the word ‘blend’. This blend comprises of two major skills that are a MUST when it comes to Data Science. These are discussed below.
Hacking and Technology
Note: We are NOT referring to ‘hacking’ as a skill used to break into other people’s computers.
Hacking’s tech-programmer ‘subculture’ meaning defines hackers as being highly creative in utilizing their technical expertise to find clever and quick solutions to problems as well to ‘build’ things. This skill is extremely important because Data Scientists NEED to be skillful enough in order to utilize technology smartly to manage studying complex data formats and working with or creating even more complex algorithms.
This skill also serves a Data Scientist’s thinking critically and plays a significant role in their development as intelligent algorithmic thinkers. All in all, their ability to strongly comprehend and deal with complex algorithmic data makes them efficient and competent in deriving solutions which ultimately leads to their models being more precise and effective.
The ability to view all forms of data available for analytics or algorithms through a ‘quantitative lens’ is key in diving (mining) into data and building a data product. Analyses of data sets involve correlations, textures & dimensions which can be expressed and viewed from a mathematical point of view. Using data to the ‘fullest’ and finding a variety of solutions can indulge a Data Scientist in extensive quantitative thinking.
Solving many common and even unique problems faced by businesses can most certainly require Data Scientists to construct analytic models that are based on hard mathematics. Obviously, to build a well-functioning, suitable and successful model, they will NEED to have a clear understanding and in-depth knowledge of the underlying mechanics of what they’re about to build.
About This Data Science Guide
This guide offers the most insightful articles, educational videos, expert insights, specialist tips and best free tutorials about data science from around the internet. The learning guide is split into four levels: introduction, basics, advanced and expert. You can learn at your own pace. Each item shows an estimated reading or watching time, allowing you to easily plan when you want to read or watch each item. Below you’ll find a table of contents that enables you to easily find a specific topic you might be interested in.
What is Data Science?
Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.
At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value.
Theories Behind Data Science
If you’d like to perform data science there are several theories and principles that you need to understand. And once you understand these theories and principles, it will allow you to learn a certain set of practices, and step by step skills that data scientists do. If you don’t understand these theories and principles, then you won’t be able to understand the practices and skills. So first let me teach you a few theories and principles that are involved, and once you understand the theoretical elements, then I can teach you a simple step-by-step method for doing data science.
The History of Data Science
The idea of data science spans many different fields, and has been slowly making its way into the mainstream for over fifty years. In fact, many considered last year the fiftieth anniversary of its official introduction. While many proponents have taken up the stick, made new assertions and challenges, there are a few names and dates you need know.
The Evolution of Data Products
Data products are increasingly part of our lives. It’s easy to look at the time spent in Facebook or Twitter, but the real changes in our lives will be driven by data that doesn’t look like data: when it looks like a sign saying the next bus will arrive in 10 minutes, or that the price of a hotel reservation for next week is $97. That’s certainly the tack that Apple is taking. If we’re moving to a post-PC world, we’re moving to a world where we interact with appliances that deliver the results of data, rather than the data itself.
What Do Data Scientists Do?
Modern data science emerged in tech, from optimizing Google search rankings and LinkedIn recommendations to influencing the headlines Buzzfeed editors run. But it’s poised to transform all sectors, from retail, telecommunications, and agriculture to health, trucking, and the penal system. Yet the terms “data science” and “data scientist” aren’t always easily understood, and are used to describe a wide range of data-related work.
Big Data Analytics — What It Is And Why It Matters
Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today’s technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions.
Things You Need to Know about Big Data
Simply put, Big Data refers to large data sets that are computationally analyzed to reveal patterns and trends relating to a certain aspect of the data. There’s no minimum amount of data needed for it to be categorized as Big Data, as long as there’s enough to draw solid conclusions. M-Brain explains the different facets of Big Data through the 8 V’s.
Why Big Data Is a Big Deal
“There is a big data revolution,” says Weatherhead University Professor Gary King. But it is not the quantity of data that is revolutionary. “The big data revolution is that now we can do something with the data.”
The revolution lies in improved statistical and computational methods, not in the exponential growth of storage or even computational capacity, King explains. The doubling of computing power every 18 months (Moore’s Law) “is nothing compared to a big algorithm”—a set of rules that can be used to solve a problem a thousand times faster than conventional computational methods could.
Real Challenges Data Scientists Face
Data is a lucrative field to pursue, and there’s plenty of demand for people with related skills. However, no career is without its challenges, and data science is not an exception. In this article, I want to explore the real challenges of data science, based on perspectives from those in the field and those who manage them. Future data professionals, here’s what you should be prepared to handle.
Data Science vs. Data Analytics — What’s the Difference?
However, it can be confusing to differentiate between data analytics and data science. Despite the two being interconnected, they provide different results and pursue different approaches. If you need to study data your business is producing, it’s vital to grasp what they bring to the table, and how each is unique. To help you optimize your big data analytics, we break down both categories, examine their differences, and reveal the value they deliver.
What is Machine Learning?
In addition to an informed, working definition of machine learning (ML), we aim to provide a succinct overview of the fundamentals of machine learning, the challenges and limitations of getting machine to ‘think’, some of the issues being tackled today in deep learning (the ‘frontier’ of machine learning), and key takeaways for developing machine learning applications.
What is Data Visualization?
Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.
What Makes a Data Visualization Memorable?
“A visualization will be instantly and overwhelmingly more memorable if it incorporates an image of a human-recognizable object—if it includes a photograph, people, cartoons, logos—any component that is not just an abstract data visualization,” says Pfister. “We learned that any time you have a graphic with one of those components, that’s the most dominant thing that affects the memorability.”
Visualizations that were visually dense proved memorable, as did those that used many colors.
Excellent Data Visualization Examples
Data visualization can be static or interactive. For centuries, people have been using static data visualization like charts and maps. Interactive data visualization is a little bit newer: It lets people drill down into the dirty details of these charts and graphs using their computers and mobile devices, and then interactively change which data they see and how it’s processed.
Ready to feel inspired? Let’s take a look at some great examples of interactive and static data visualization.
Industries Becoming Defined by Big Data and Analytics
The ever-improving capabilities of big data platforms increasingly create new opportunities for industries with representatives who want to examine analytics to benefit their companies.
Here are five sectors with business operations shaped by big data and analytics — and what they have to offer.
How Industries Are Using Data Science
All industries and government organizations alike are awash with data in this pro-tech age. Be it the University of Tasmania developing a Learning and Management System using data based on students’ study habit, or the Wimbledon Championship utilizing data to analyze the sentiments of the viewers in real-time; data has found its usage across varied industries.
Data Science Use Cases in Manufacturing
The amount of data to be stored and processed is growing every day. Therefore, today’s manufacturing companies need to find new solutions and use cases for this data. Of course, data brings its benefits to manufacturing companies as it allows to automate large-scale processes and speed up execution time.
Data science is said to change the manufacturing industry dramatically. Let’s take under consideration several data science use cases in manufacturing that have already become common and brought benefits to the manufacturers.
Data Scientists to Wipe out Business Analysts
While it’s a flourishing time to be a data scientist, I predict business analysts will likely take the first hit and be forced to either adapt their skills or be left behind. A shift is occurring in which companies are no longer using business analysts to determine what the future of a business looks like – instead, they are turning to data scientists to use machine learning and data mining techniques to discover new product trends and patterns of customer behavior that create a more accurate picture of where various aspects of the business is going.
How to Build a Data Science Team
Organizations seeking a competitive edge are increasingly looking to hire data scientists to parse through all of the information they collect and draw actionable insights from it. But building a data science team requires a strategic approach and realistic expectations about what these professionals can actually do, experts said.
Make a Success Story of Your Data Science Team
Regardless of whether you manage an existing data science team or are about to start a new greenfield project in big data or AI, it’s important to acknowledge the inevitable: the Hype Cycle.
The increasing visibility of data science and AI comes hand in hand with a peak of inflated expectations. In combination with the current success rate of such projects and teams, we are headed straight for the cliff edge towards the trough of disillusionment.
Strategic Plan For Building Your Data Science Team
To achieve the goals for the Data Science – which is to become more effective at leveraging data and analytics to optimize key business and operational processes, mitigate compliance and security risks, uncover new revenue opportunities and create a more compelling, differentiated user experience – we need to consider three key roles, and the interaction between those three key roles, that round out the data science community. We need to understand the responsibilities, capabilities, expectations and competencies of the Data Engineer, Data Scientist and Business Stakeholder.
Best Data Science Programming Languages
While most languages cater to the development of software, programming for Data Science differs in the sense that it helps the user to pre-process, analyze and generate predictions from the data. These data-centric programming languages are able to carry out algorithms suited for the specifics of Data Science. Therefore, in order to become a proficient Data Scientist, you must master one of the following data science programming languages.
Data Science Programming Languages Compared: Python vs. R
As I frequently travel in data science circles, I’m hearing more and more about a new kind of tech war: Python vs. R. I’ve lived through many tech wars in the past, e.g. Windows vs. Linux, iPhone vs. Android, etc., but this tech war seems to have a different flavor to it. What feels different in this case is that the application area is the same, namely performing work in data science where the solution often depends on the use of libraries that implement various machine learning algorithms. This being the case, the question is what language should you adopt as a data scientist?
While R has traditionally been the programming language of choice for data scientists, some believe it is ceding ground to Python. Here is a short list of some the arguments I’ve heard of late, along with my personal assessment of each
How Data Science Can Answer Cybersecurity Challenges
In cybersecurity, your goal is to identify threats, stop intrusions and attacks, properly identify malware and spam, and prevent fraud. Data science and machine learning can be used to help better identify these threats. For example, when it comes to identifying malware and spam, data from a wide range of samples can be used for deep learning and training purposes so that malware and spam are properly detected.
How to Become a Data Scientist
Data science is arguably the hottest career of the 21st century. In today’s high-tech world, everyone has pressing questions that must be answered by “big data”. From businesses to non-profit organizations to government institutions, there is a seemingly-infinite amount of information that can be sorted, interpreted, and applied for a wide range of purposes. Finding the right answers, however, can be a serious challenge.
Case Studies in Data Science
Data science is used by pretty much every industry out there. Insurance claims analysts can use data science to identify fraudulent behavior, e-commerce data scientists can build personalized experiences for their customers, music streaming companies can use it to create different genres of playlists—the possibilities are endless.
Allow us to share a few of our favorite data science case studies with you so you can see first hand how companies across a variety of industries leveraged big data to drive productivity, profits, and more.
Predictive Analytics — Examples of Industry Applications
Businesses today seem to have a multitude of product offerings to choose from predictive analytics vendors in every industry, which can help businesses leverage their historical data store by discovering complex correlations in the data, identifying unknown patterns, and forecasting. This is hardly surprising considering the fact that predictive analytics can help businesses answer questions such as “Are customers likely to buy my product?” Or even “Which marketing strategies might be most successful?”
Starting a Career in Data Science
With so many different data science careers to explore, you might find yourself wondering which is the right one for you and if you’ve got what it takes to fit the profile.
Is Data Science for Me?
Well, we’ve all asked ourselves that question when we were at square one of our data science learning path. And we haven’t forgotten that every expert was once a beginner.
So, this data science career guide has a three-fold purpose:
— Show you why data science opportunities are worth exploring;
— Inform you about the different careers in data science and boost your efficiency in discovering suitable data science roles;
— Give you the know-how you need to pursue your professional data science path.
Tricks to Crack Data Science
I know you are an analyst and all you care about is numbers. But, what differentiates an awesome business analyst from average data analyst? It’s their potential to understand business. You should try to understand business even before you take up your first project.
What Does The Future Data Scientist Look Like?
The separation of different types of data scientists may have occurred because current data professionals have found themselves having to cover too much ground to manage what’s being demanded of them.
“This can sometimes be a case of trying to master both the technical and the business understanding and communication aspects of their role,” says Iain Brown, head of data science at SAS UK and Ireland.
The Future of Data Science
The exponential growth in data we have witnessed since the beginning of our digital era is not expected to slow down anytime soon. In fact, we have probably just seen the tip of the iceberg. The coming years will bring about an ever increasing torrent of data. The new data will function as rocket fuel for our data science models, giving rise to better models as well as new and innovative use cases.
Learning Data Science for Beginners
Learn Data Science is this full tutorial course for absolute beginners. You’ll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You’ll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.
Further Reading: Best Data Science Books
Data Science from Scratch: First Principles with Python. To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.
Data Science – MIT Press Essential Knowledge series. A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.
Data Science: A Comprehensive Beginner’s Guide to Learn the Realms of Data Science. With an in-depth study of data science and its various components, this book is made specifically with beginners in mind. Get to learn the basics of data science and how to gain practical experience with words and terms, which are broken down for easy understanding.
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
Further Learning: Best Data Science Courses
IBM Data Science Professional. This program consists of 9 courses providing you with latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets.
Introduction to Data Science by IBM. In this course, learners will develop foundational Data Science skills to prepare them for a career or further learning that involves more advanced topics in Data Science. The course entails understanding what is Data Science and the various kinds of activities that a Data Scientist performs.
Data Science: Foundations using R. This course covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research.
Applied Data Science with Python. The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.