Data science is one of the most valuable skills in the digital economy, but it’s also one of the most misunderstood. To help clear up any confusion, we’re sharing a guide to what data science really is, how it works?
- What is Data Science?
- Uses of Data Science
- 5 Steps to Becoming a Great Data Scientist
- Top 5 Data Science Jobs of 2021
What is Data Science?
The term “data science” is used to describe the field of tasks related to collecting, analyzing, and using data. Data scientists work on advanced methods for processing, cleaning, and visualizing data as well as extracting insights and information from it.
Data science is an interdisciplinary field that uses concepts, methods and tools from various fields within the broad areas of mathematics, statistics, information technology, and computer science. It seeks to identify patterns and trends in large data sets.
If you’re looking to learn data science, then you’ve come to the right place. The data science process is fairly simple, but it involves several steps and a variety of sub-fields. This guide will take you through all of these steps and help you become a Master of Data Science in no time.
Uses of Data Science
With data science, you can organize your data into neat silos and show the relevance of each individual piece of data to the business. But what are the uses of data science. To cover that gap I have listed some important uses of data science.
1. Data Science Use to Predict Outcomes Based on Statistics
Data science is the practice of extracting knowledge and insights from data in various forms, either structured or unstructured, similar to data mining. The term “data science” describes a body of knowledge that is about using empirical evidence to discover valid predictive relationships and/or causal relationships between various aspects of the world.
Businesses usually consider data science the process of using this knowledge in more efficient ways to achieve strategic business goals. The term “science” refers to findings and ideas achieved through the application of data science principles.
According to the latest data science trends, the world of business is continuing to integrate data science into many new and existing efficiency, decisions and innovation efforts.
In recent years, the use of data science has exploded in popularity, with the number of people using data science software and tools increasing by 256% between December 2015 and December 2020. And in 2021 it is keep growing.
Companies such as Facebook, Airbnb, Netflix, Palantir, PwC, Wesabe and many more have made use of data science in their work, recently.
Analysis of big data is one of the most powerful and popular uses of data science today. Companies collect, store and analyze huge troves of data which often contain tons of information about customers, employees and competitors.
But startups tend to focus more on defining the best ways to use collected data in novel ways, rather than applying an existing algorithm to it.
Data science helps create analytics by identifying patterns and connecting them through models. The models develop auxiliary datasets which can be analyzed and acted upon without handing over the collected data to a third party. This results in a high degree of efficiency and cost-effectiveness.
2. Data Science for Business
What is Data Science? Data science is the application of statistical techniques to help businesses make better decisions. The main goal of data science is to use all of the available data, gathered from many different sources, to derive models that best picture what’s going on in our world.
Another popular use of data science involves models that predict consumer purchasing behavior. Salesforce is an excellent example of a company using data science to predict which customers will buy from them.
Use of data science for forecasting could provide an additional edge for businesses and organizations.
Businesses can leverage data science to come up with new marketing campaigns. Here are some examples.
Many businesses use the use of data science in order to fulfill customer needs. Here are some more examples.
As you can tell, data science isn’t limited to tech companies. Organizations selling a variety of products and services in various industries use data science too.
When businesses and professionals make critical decisions, it is critical to have a solid understanding of the consequences of those decisions. It can take a while for organizations and non-profits to understand data science because it’s a bit technical.
Even with a solid understanding of how data science works, things still can go wrong when using data to make decisions.
In order to increase data science skills and decrease the likelihood of making mistakes, organizations can often hire data science experts in-house.
1. Businesses decide what to do based on customer data.
2. Data scientists write code that analyzes the data.
3. Data Science for Government Agencies
For government agencies, data science means taking a data-driven approach to government problems. One example of this is the NYC Taxi and Limousine Commission (TLC) and the Taxi and Limousine Commission (TLC) in NYC.
The TLC uses data science to manage the city’s taxi fleet. The TLC’s mission is to maintain a fair and efficient transportation ecosystem through the regulation of taxis, limos, and other transportation service providers, and to foster public confidence in the fairness of the taxi system.
The TLC’s problem is not that it’s hard for the governor that government regulations would provide it with a monopoly. Instead, the real problem lies in the fact that it is difficult to predict how general trends in general business will unfold over the coming months, weeks, or days. It is up to the TLC to take a data-driven approach to pilot revenue projections to create a successful ride-hailing system for NYC.
The first part of data science is assessing what data there is, and then choosing from it what you want to analyze (e.g., existing taxi fares, cab prices, customer usage trends, competitor pricing, credit card stash.) The second part of data science is organizing this data in a way that provides insights you can use to make better decisions.
Understanding trends is an essential part of being able to act on them. For example, the TLC uses historic taxi pricing data and real-time market data to provide the most accurate and up-to-date pricing.
They also use various data sources to track the service usage and safety behavior of customers; based on these analyses, they’ve shown that many customers consume excessively or irrationally.
For example, people from outside of NYC tend to make far more trips, average more stops, and drive more kilometers than NYC taxi customers. Data scientists at the TLC also designed and tested new pricing models based on their customer behavioral insights.
4. Data Science for Non-Profits and Social Services
Non-profits and charities can benefit from data science by using it to gain insights into how people are interacting with their services.
For example, data science can be used to gain insights into how people are using a website, how people are using a mobile app, or how people are interacting with a piece of offline marketing collateral.
If you have a collection of images of an animal for an animal advocacy campaign, data science skills can help you find which pictures showed the most engagement. You can use this information to craft better visuals in your ad or better develop your online service.
5. Data Science for Healthcare and Biotech Companies
If you’re a Healthcare or Biotech company, it’s important to be using data science to find correlations between patient and product data, or to find the best patient profile for testing a new drug.
Data science is all about finding insights in the data that you have and using that knowledge to make decisions and predictions about the future. The healthcare industry has relied on algorithms and Machine Learning (ML) for a long time, but increasingly the field of data science is emerging. Data science is used across industries, as it is an interdisciplinary field that interrogates multiple disciplines to answer questions about data.
5 Steps to Becoming a Great Data Scientist
A data scientist is a person who is able to understand and learn from massive amounts of data, and then turn that knowledge into usable information for business purposes. If you’re considering entering the field, this article will teach you how to become a data scientist.
1. Learn the Foundations of Data Science
The first step to becoming a data scientist is to learn the foundations of data science. It’s important to learn the basics of R, Python, and SQL, and to have a very solid understanding of data structures and how they work with each other.
To be a great data scientist, it is essential to learn Python and be able to use SAS and/or SQL under the hood.
To help you more here are 6 Best Programming Language for Beginners 2021 that will guide you more about programming.
Once you have a basic command-line understanding, coding skills, and know how to rotate a data frame, then you can learn other coding languages.
When I began doing data analysis, I had to learn Python because my primary program was in PHP, and I had a horrible understanding of how programming worked. I know now that it’s important to learn more than one language for a data scientist.
Next, it’s important to know more about business issues. Depending on where I begin a data analysis project, I take it in one of two directions. Either I take it from a practical technical tool such as R, Python, Java, or SPSS, or I take it as a data scientist from a business perspective.
My favorite business tool and programming language for this question is not R. It’s a visual analytics tool called Tableau that uses both SQL and Python.
The most important thing to focus on when answering this question is to master the art of pivot tables.
To create a pivot table, I will import all of my data (Tableau has free data trying out a paid option), and I then use pivot tables to pivot all of my data in various different columns or rows.
Data science is often misunderstood as a set of skills. It’s more so a system of concepts and methods that you have to know and have ingrained in your head. I also learned more about data science principles, such as creating hypotheses, testing, getting feedback, and debugging. Same advise goes to you to learn the data science in the right way.
2. Get Good at Coding and Using Data
If you go into a career in marketing, you’ll definitely need to learn how to code and how to use data. It’s important to be able to understand and interpret data and to know which tools to use to track and measure your results.
Naturally, all of these skills will come naturally because you’re going to spend a lot of your time interacting with data.
This is where many marketing professionals fail. And it’s quite simple. The current job market puts them in an environment that doesn’t provide a regular pipeline of data and analytics training.
Moreover, many marketing jobs require an MBA or other specialized degree, which usually results in a lifetime of knowledge transfer and increased work complexity. As a result, people don’t keep up with software advancements, they lose out on new skills, and eventually, risk losing their jobs.
This is why it’s so important to become a data-savvy expert on your own. Like all marketing professionals, you’ll need to hire people who are proficient with data and have the foundational skills for your industry.
As a Ph.D. candidate in statistics, I was able to build my own company from scratch, using only my C++ skills and data analytics knowledge.
Because many companies don’t provide such training. And why do companies hire for such basic skills? Because it’s a way to lower their risk.
In fact, if a data scientist doesn’t have data skills, it’s quite easy to lose out on larger projects because of unverified assumptions, or incorrect implementation decisions.
Data and analytics is not a specific field. Rather, it’s a set of skills that encourage you to spend more time analyzing problems and making decisions than making reports.
There are still many companies which require data scientists to have a specific degree, MBA in hand (or another specialized degree) to enter the field.
But that’s changing. Companies are realizing that the hardest thing to find/hire data scientists is someone with crafty skills in data and analytics.
3. Learn More About the Business
You don’t have to be an expert in every aspect of running a business, but you must at least know how all parts of your business work. If you don’t, you’ll make costly mistakes. And even if you have a solid grasp on all related business topics, you’ll still fail if you do not:
1) combine your know-how into one cohesive whole.
2) discipline yourself to learn new skills as fast as you learned the older ones.
3) create a mentor/mentee network (often independent of the area of expertise) that will give you pointers and resources whenever you need them.
This is not a course, you can learn these things by yourself. Rather, it’s a suggestion on how to become a better, faster, and more knowledgeable data scientist. There are many companies that provide Cross-Campus and Online Courses that you can study. An introduction on some of them can be found here.
If you’re a college student or a recent graduate with a business degree, you’re probably not dealing with small data too well yet.
You’re likely spending time studying and collecting data, which feels good but doesn’t seem fast enough. Or you’re working with business data in some capacity, and you feel overwhelmed, overwhelmed, overwhelmed.
If you’re not familiar with the importance of speed when working with large data, be patient. Or simply observe the behavior of industry leaders and great market leaders.
Harvard Business Review mentions this:
“To succeed in business, leaders need to find faster, more effective ways to solve problems with as little detour as possible.
4. Know How to Choose the Right Tools and Techniques
It’s important to know how to choose the right tools for the job. For example, if you’re a marketer, then you may want to get really great at Facebook ads, but if you’re working for a local business then you won’t have time to learn how to use them well.
As a data scientist, you’re going to be working with a lot of machine-learning algorithms. Machine learning is an extremely popular statistical modeling technique and as a whole, it’s valued highly. However, people often treat it as if it can’t be applied beyond statistical modeling.
Simply put, machine learning is a technique for non-linear pattern classification or clustering. It should be noted that machine learning alone is not enough.
If you’re a data scientist, you’re going to need to know linear modelling techniques, predictive analytics, and how data scientists from different disciplines collaborate to build models.
Once you understand these skills, you’re ready for the next step is automation. In order to be a data scientist, you will be working with large datasets. When you work with these datasets, it’s often difficult to know exactly what to do.
One incredibly common thing you’ll see is models expecting days, weeks, or months to run. For example, consider the Facebook Advertising model.
It expects to learn how to predict users’ future actions based on their activity. If this prediction takes months, it probably won’t make any exciting impact. However, if it takes just days, that workflow changes.
5. Make Sure to Keep Learning
If you want to be successful in your career, you need to make sure to keep learning. Learn about different business practices, new technologies, and social media trends. Learning is the key to staying ahead of the competition.
Before you can become a data scientist, you need to have a data-driven mindset. This means that you are curious about what you are learning, what your peers are doing, and how they are connecting the dots. You have to warm up your brain before you are ready to become a skilled data scientist.
As an employee, you are wondering how to improve your performance. You need to decide what metrics you want to use to understand your efforts and determine if they are making your performance worse. You should set goals and objectives based on how you think your contributions can positively influence others.
It’s hard to hire a data scientist. There are very few positions in today’s job market that require data-driven thinking. It’s a prerequisite to get hired as a data scientist for the next data-driven company you want to work for. It’s important if you want to make sure you’re getting compensated well in the future.
Top 5 Data Science Jobs of 2021
Data science is one of the fastest-growing fields in employment, and it’s a major field for growth and innovation. In fact, while many of us dream about starting our own businesses or working remotely, high salaries are still available if you’re looking to work for someone else. If you’re interested in becoming a data scientist or transitioning into a more advanced data scientist role, here are the top ten highest paying jobs in data science.
1. Database Administrator
Database administrators (DBAs) are a crucial part of any company that uses databases or software that requires a backend database.
DBAs take care of the databases, not only ensuring that they’re running smoothly but also making sure they’re secure. DBAs should have a strong understanding of how databases work and how to fix problems that arise.
As a side job, you can also earn money and be flexible while you do it, unlike other jobs that require you to be deeply committed to your skillset. If you enjoy writing code, you can show your creativity by writing custom SQL queries that are used to access data from databases.
As a bonus, you can help customers because the companies need developers who can work in SQL daily or have more knowledge about related databases.
If you enjoy helping your customers, this job is for you! In addition to being able to help them from programming, you’ll need to count how many recurring revenue or other recurring revenues their company has.
This can be tricky because data triage systems can generate hundreds of lead reports for a single company, which needs to reconcile different databases and make sure they’re all matching.
2. Software Developer
A software developer is a person concerned with facets of the software development process, including the research, design, programming, and testing of computer software.
Becoming a Data Science Software Developer is the fastest growing software role with a projected 4+ job openings for each month t. Engineer roles are mostly in demand as they’re used to lead teams of developers.
As a developer, you’ll need to pull together the code required to build or extend end-user products and services or to create applications that run beyond a single device or user. You’ll also need to be able to develop apps at a fast pace, keeping up with new software versions as they come out.
In general, companies are increasingly demanding data scientists have some in-depth knowledge (or at least exposure) in statistics, machine learning, and the statistical margins. You should aim to have a solid statistical analysis background at minimum.
“With the COVID-19 pandemic, demand has skyrocketed for data scientists who can effectively integrate with BI tools, dashboard design and visualizations, scores and reporting from reporting tools and massive data sets,” says Indeed.
3. Business Intelligence Analyst
Business Intelligence Analysts are professionals who use data to make informed decisions about business strategy. Business Intelligence Analysts use sophisticated data gathering, processing, and reporting tools to generate information that can be used to make business decisions.
Data Scientists are analytical specialists who apply machine learning techniques to solve data-driven problems. Data scientists use statistical methods, mathematical models, and machine learning to uncover and explain sources of information both in data and in the human language.
Data scientists apply these mathematical methods to create new insights and models. The types of data that data scientists gather and process may include:
SQL – Structured Query Language that use a database as a composition.
Pattern Recognition and Machine Learning – processes to extract visual patterns from large amounts of raw data. (Example: image recognition or pattern based photo recognition)
Natural Language Processing – a method to parse and disambiguate language.
Sentiment analysis – a method for determining the emotion of any given set of data at any given time.
4. Network Security Administrator
The network security administrator is responsible for the overall security of an organization’s network infrastructure. This individual develops security policies and procedures to protect the network from internal and external threats.
The network security administrator may also be tasked with monitoring network traffic, conducting vulnerability assessments, and installing anti-virus software.
The combination of data analysis and security management is critical to ensuring that all computer users have a secure and optimized experience on the network.
The network security administrator not only works with security teams to create and/or update security policies, but also helps to coordinate security projects, provides regular security briefings, and helps to polish up the security operations and policies of enterprise infrastructures.
The goal is to provide visibility over an organization’s network security to clients, partners, and the general public.
Having experience with SQL is not only crucial to the job description, but it’s also extremely valuable because data scientists are looking for ways to mine data and provide insights from it. Specifically, data scientists looking to enter the industry are looking for tech talent with a solid understanding of both SQL and Pre-Semi Python.
Tech candidates who already have experience in SQL and/or have worked with a BI tool or programming language will get extra consideration.
5. IT Support Specialists and Help Desk Techs
It’s of little secret that customer service is one of the most important functions in any business, but when it comes to the tech industry, customer loyalty is more valuable than almost any other commodity. However, providing great support isn’t as simple as plucking someone out of the crowd.
The demand for help desk support specialists is growing every year, especially for those who can work on-site with clients and/or remotely via online tools.
If you’re an IT professional who’s interested in learning more about the field of data science, you may think that it’s an entirely different career track. But according to a new report from Glassdoor, IT support specialists and help desk technicians are making a huge salary as data scientists.
As a technical support specialist or IT support specialist, you’re more than just a help desk employee. You’re also the face of the company and if you put on your customer-service hat, it’s your job to make clients happy and keep them coming back.