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What Is Data Science & Why Should I Learn It?

Data Science is a relatively new field in the tech world. There are organizations in nearly every industry going digital and with the transition comes huge amounts of data. Data is extremely useful for business. Data scientists and analysts are more in-demand than they’ve ever been before.

Data science can be used to: 

  • Predict spending behaviors of customers
  • Recommend similar products for more revenue
  • Analyze an entire population
  • Inform investment decisions 
  • Populate social media feeds
  • Make workflows more efficient
  • Drive custom advertising 
  • Choose your next favorite movie
  • Analyze political elections and campaigns
  • Detect fraud
  • Develop better products
  • Optimize pricing
  • And much more

These are revolutionary insights that companies just a decade ago couldn’t even imagine and it’s all thanks to data science. Each of these insights generates revenue, save companies money, make business processes faster, and drive our economy. 

What is data science anyway? Why should you learn it? Do you need to learn how to code? Keep reading for answers to those questions and more!

What is data science?

Data science uses scientific methods, algorithms, systems, and processes to extract knowledge from data. Data scientists use programming languages, data analysis, statistics, and other techniques to create insights from data. 

Data science is used in almost every industry including retail, academia, transportation, healthcare, politics, finance, and more. It’s used by data scientists and data analysts but also by researchers, bankers, and marketers. 

Why is data science so popular?

These days, everything is going digital. From books to health records to government services, you’ll be hard-pressed to find a corner of society that has not been impacted by the digital revolution. 

This revolution has grown exponentially over the past decade, culminating in big data. Big data is “data that contains greater variety arriving in increasing volumes and with ever-higher velocity.” Ever-increasing variety, volume, and velocity make up the three Vs of big data. 

Big data includes all of the photos and videos uploaded to social media sites like Facebook, Instagram, Youtube, Pinterest, and more. It includes all of the information about users, shoppers, and patients gathered by retail companies, healthcare systems, and streaming services. It even includes monetary transactions through digital currencies like credit cards, debit cards, checks, and direct deposits.

We’re generating data more rapidly than ever before. In just the last hour over 1 million minutes of video have been uploaded to Youtube, over 27 million tweets have been tweeted, 2.8 million photos were posted on Instagram, and 216 million Google searches have been made. 

The massiveness of big data is nearly incomprehensible! It’s certainly more data than we can fit in a single spreadsheet. This amount of data would take years to sort through by hand and huge warehouses to store. But with the cloud and data science, we can use this data to our advantage.

Billions of useful data points are being collected every week and the way we make sense of them is through data science. These concepts are utilized to power our economy’s biggest businesses, help the environment, and analyze our elections. 

Here are some incredible ways data science is impacting our world:

  • Southwest Airlines saved $100 million by reducing the time its planes idle on the tarmac.
  • UPS saved $39 million gallons of fuel by optimizing its fleet.
  • $2 billion tax dollars were saved by the IRS as a direct result of algorithms designed to catch identity fraud and improper payments.
  • Amazon is exponentially increasing its profits through predictive analytics that increase customer satisfaction with product suggestions and personalized search results.
  • Uber uses user data to make rides faster, calculate its surge pricing, and match users with the most suitable driver.
  • Bank of America is using machine learning to power their virtual financial assistant Erica who is an automated customer advisor serving over 45 million users through speech recognition.
  • Netflix is using data science to power their recommendation which is responsible for over 80% of user choices.

What programming languages are used for data science?

A data scientist's toolbox is made up of programming languages, the libraries, and frameworks created for their programming language of choice, relational database management systems (RDBMS), an integrated development environment, and software tools like Tableau. 

Programming languages can also be used by data scientists for machine learning and artificial intelligence, which are subsets of data science. Machine learning is when data engineers train a computer with data and algorithms. Artificial intelligence uses more complex machine learning methods to train computers to learn without supervision. 

Python 

Python is a general-purpose programming language that emphasizes readable code and versatility. It’s one of the world’s most popular programming languages. Python is one of the easiest programming languages to learn and it’s a great place to start if you want to get into data science.

The best part about Python is that there are over 137,000 Python libraries and frameworks that make using Python efficient, fun, and beginner-friendly. There are libraries for data science, web development, machine learning, cybersecurity, fintech, and more. 

SQL 

SQL stands for Structured Query Language. It is a programming language that is used by data sciences to retrieve information from databases and by web developers to display information on websites. 

SQL can be used in combination with back end programming languages like Python, R, PHP, and Ruby for data science and web development. SQL is usually used with a relational database management system (RDBMS) like PostgreSQL. 

R

R is a language and environment for statistical computing and graphics. R is almost exclusively used by data scientists, statisticians, and researchers. R also has its own libraries but there are only about 11,000 of them. 

What does a data scientist do?

Data Scientists gather, process, model, and analyze data using programming languages and software tools. They use their analyses to contribute valuable insights to their employer, create reports, predict outcomes, conduct risk assessments, and find improvements within current organizational processes or products. 

Data Scientists can work in many industries including retail, transportation, tech, medicine, and government agencies. Most Data Scientists work on a team to make data understandable to their employers. They usually work full-time in onsite or remote positions. There are also freelance opportunities available for data scientists.

Data Scientists’ day-to-day tasks include: 

  • cleaning data
  • collecting data
  • analyzing data
  • creating data visualizations
  • making predictions
  • researching
  • risk modeling
  • testing hypotheses

Job Outlook for Data Scientists

The skills to work with massive amounts of data are incredibly important in our increasingly digitized and data-driven world. The future is bright for data scientists and they can look forward to potentially decades of high-demand. 

Data science job opportunities are projected to grow 16% over the coming years which is significantly more than the average career in the U.S. The labor market for data scientists is highly competitive for companies. There is a skilled data scientist talent shortage in the U.S. and many companies have difficulty filling these positions. 

The median salary for Data Scientists is about $122,840 and entry-level data scientists can expect to earn $69,990 on average, depending on their location and experience. The potential for upward mobility and salary increases in this field is enormous. 

Data Scientists can look for these job titles:

  • Data Analyst
  • Data Scientist
  • Data Engineer
  • Machine Learning Engineer
  • Machine Learning Scientist

Eventually, data scientists can acquire the skills to become a Data Architect, Business Intelligence Developer, Statistician, or Senior Data Scientist. Each of which would likely result in a raise. 

Why You Should Learn Data Science

Organizations small and large are coming to depend more and more on data and its revealing analytics. The future of data science is strong. Data science is rapidly becoming an essential skill. It’s valued by the government and the private sector alike. 

From business to accounting, education to science, engineering to technology, healthcare to energy, and even government, everything is data-driven. Even the economy itself can be analyzed with data science. 

The technologies for data science are some of the easiest programming languages and tools to learn. Python is revered as one of the easiest programming languages in the world to learn. In combination with SQL, data science has never been easier and more efficient than right now. 

The educational opportunities for data science are abundant. With the options of online or in-person bootcamps and courses, it’s completely possible to fit learning data science into your schedule. There are part-time and full-time options or you can take just one course on the side.

You don’t have to be a data scientist to learn and use data science. Even basic data science is an applicable skill for analysts, high-level marketers, researchers, and finance positions. Some companies will even pay for your data science education! 

But if you do want to pursue a career in data science, the path is quite lucrative. Data scientists are valuable to the economy and there is a shortage of skilled workers in this position. This makes for a competition to hire the best data scientists and usually results in high salaries with great benefits and the potential for bonuses.

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