Data Science Specialist: What Should You Know About This Speciality?
We live in an era of big data, and experts from various industries are constantly looking for new solutions to store, structure, and analyze data. Although there are many frameworks that address the storage problem, processing still remains a common issue, and here is where data science comes in handy. Data science is also a basis for artificial intelligence. If you want to make data science your profession, it’s a great choice, so let’s figure out what data science is, and what you should know to become a specialist.
Data science includes many disciplines, algorithms, tools, and principles. It’s mostly focused on raw data and its patterns, however, it has a little to do with statistics. For example, data analysts process the history of data, conducting an exploratory analysis. Data scientists not only do exploratory analysis in order to get insights but also use machine learning in order to predict the same patterns in the future. Data science is used in decision-making and predictive analytics, as well as prescriptive analytics, which combines predictive analytics and decision-making.
- Predictive Casual Analytics
This discipline of data science predicts the probability of a certain event occurring in the future. For example, banks and companies that provide money for credits need predictive analytics to understand the probability of their customers making their credit payments on time. In this case, data scientists can create a model that analyzes the payment history of a particular customer and predicts their future payments.
- Prescriptive Analytics
Prescriptive analytics is used when there’s a need for a more advanced model that not only analyzes data but also makes its own decisions. Such models can be modified with various dynamic parameters. This area of data science is relatively new. A good example of a prescriptive model is self-driving cars. Such cars gather data from sensors and cameras, analyze it, and take decisions on what route to take, what speed is appropriate, and where to turn.
- Machine Learning for Predictions
Machine learning allows for analyzing the historical data and building models that determine future trends. Machine learning for predictions is also called supervised learning because in this case, you already have the data that serves as a basis for training. For example, financial companies may use their transactional data in order to predict market trends.
- Machine Learning for Pattern Discovery
If there are no parameters that can serve as a basis for predictions, you have to analyze the dataset, detecting hidden patterns. Such models are unsupervised because there are no predefined labels that can be used for grouping the data. One of the most common algorithms for pattern discovery is clustering. For example, if a telephone company wants to create a network in a certain region by installing towers, clustering can help find the best spots for towers that will ensure a strong signal.
Most often, data science projects have the following lifecycle:
- Discovery: First, you have to determine priorities and requirements, selecting the necessary sources of data and technology. At this stage, you should also formulate the problem and initial hypothesis.
- Data preparation: Now you have to create an analytics sandbox where you can explore and preprocess data to prepare it for modeling. The next step is performing ETLT (extracting, transforming, loading, and transforming). This step is followed by data conditioning.
- Model planning: To plan a model, data scientists choose the techniques and methods that will allow them to determine relationships between variables. Depending on these relationships, they can choose the necessary algorithms. There are three most common tools for model planning: R, SAS/ACCESS, and SQL Analytics Services. Currently, R is the most popular tool because it’s suitable for creating interpretive models.
- Model building: The next step is developing datasets for testing and training. At this stage, you can determine whether or not your tools and environment can cope with the necessary tasks. To build models, data scientists use WEKA, SAS Enterprise Miner, Matlab, SPSS Modeler, Statistica, and Alpine Miner.
- Operationalizing: This stage includes delivering final reports, technical documents, briefings, and code. Sometimes, a pilot project may be also tested in a real environment.
- Communicating results: This is the final stage. A team should evaluate the solution in order to understand whether or not it solves the problem and works as intended.
Why Data Science Is Important
During the last few years, data science experienced countless improvements and developments, and the reason is that it’s useful for various industries. The first reason why data science is so popular is that it helps businesses better understand their customers. Using data science, marketers can get useful insights into the preferences and behavioral patterns of their target audience. In turn, it allows businesses to create personalized customer experience and to improve their products or services.
Data scientists also often work on mitigating risks and fraudulent actions. For example, they can create a model that will analyze the historical data on fraudulent purchases and detect fraudulent actions at the right time.
Most businesses also have problems associated with massive amounts of unstructured data. Back in the days when technology was not as developed as it is today, most of the data was structured and could be easily analyzed. Today, more than 80% of enterprise data is unstructured, and this figure will continue to grow due to the popularity of the Internet of Things and other data-related technologies. Businesses obtain data from text files, sensors, financial logs, multimedia forms, and many other instruments. Simple business intelligence tools are no longer able to process such volumes of different data, so there is a need for more efficient algorithms and learning models. In addition, data science offers countless opportunities for decision-making and allows experts from various fields to predict any events based on historical data.
Main Trends in Data Market
- Open Source and Big Data
Such applications as Spark and Hadoop are extremely popular in the big data industry, and these are open source applications. According to research, about 60% of companies that work with big data rely on open source software, and the usage of Hadoop grows by almost 33% per year.
- In-Memory Approach
Traditional databases use hard drives and SSDs, storing data on disk. Such an approach can no longer satisfy modern companies that need to increase the speed of big data processing. This is a reason why the in-memory approach gains popularity. IBM, Pivotal, SAP, and other vendors offer solutions that store the data in RAM, increasing the speed of processing significantly.
- Machine Learning
As the capabilities of big data analytics grow, more and more companies invest in machine learning. According to statistics, machine learning is one of the top 10 technology trends, and its importance will continue to grow.
- Intelligent Apps
Machine learning and artificial intelligence allow for developing intelligent applications, which analyze the previous behavior of users and use this data to offer personalized experiences and services. The most common example of such apps is recommendation engines, which are already used by many entertainment and e-commerce services.
- Intelligent Security
Predictive analytics allows organizations to analyze their security logs, collecting the data on cyber attacks in order to predict and prevent such attempts in the future. This is done by integrating event management platforms and security information into big data platforms.
- Internet of Things
The Internet of Things (IoT) also has a tangible impact on the big data industry. Although IoT solutions are most often used in home security systems and transportation, they also gain popularity in healthcare, agriculture, and energy/utilities.
What You Should Know to Work in Data Science
First, you need to have the necessary education. According to statistics, 88% of data scientists have a Master’s degree, and 46% hold Ph.D. Most often, these people are specialized in Computer science, Mathematics, Statistics, Social sciences, and Physical sciences. However, if you want to become a data scientist, you also should possess a number of specific technical skills.
To work in data science, you must know Python. This programming language is one of the most common requirements for data science positions, along with C/C++, Java, and Perl. Python is used at almost every step of the lifecycle of a data science project. Although the knowledge of Apache Hadoop is not always necessary, it’s also a quite common requirement, and it was ranked by LinkedIn as the second crucial skill for data scientists.
Even though Hadoop and NoSQL gain popularity in data science, most employers will also expect you to work with complex queries in SQL (Structured Query Language). This programming language can help you perform many important tasks, such as adding, extracting, and deleting data from databases. Thus, we recommend that you have a perfect understanding of SQL, being able to use it on a professional level.
Apache Spark also becomes a very popular technology in the world of big data. Spark is similar to Hadoop but it works faster because it caches computations in memory instead of writing them to disk. Apache Spark was created specifically for data science purposes. It runs even the most complicated algorithms faster than other solutions. It also enables data scientists to eliminate the loss of data.
According to research, most data scientists lack knowledge in machine learning and artificial intelligence. However, given that the importance of these technologies grows faster than ever, we suggest that you also familiarize yourself with such techniques as logistic regression, supervised machine learning, reinforcement learning, decision trees, etc. Such skills will certainly be a great advantage when looking for a job in data science.
You should also be familiar with data visualization: you will have to present data in an understandable manner so that you can explain the results of your work to decision-makers. Such tools as Matplotlib, ggplot, Tableau, and D3.js will help you present overwhelming data in the form of charts and graphs.
Where to Learn About Data Science for Free: Online Courses