About Data Science
In simple terms, data science is the science of using data to obtain information and make decisions based on that data. Data science allows organizations to make smarter decisions, predict future outcomes, and solve challenging problems.
Let’s look at a simple example in finance.
Predicting the market can be pretty challenging and that's what tons of people are trying to do every day. There are a ton of factors that go into stock prices and fluctuations and there is also a human element to it.
What if we could use data science and machine learning to make more accurate predictions based on existing data and current market trends?
- With Python, we can use linear regression to predict share prices for the next thirty days, using libraries and packages like Matplotlib to create visual representations of this data.
- We can scrape existing data from the S&P 500 using the Google Finance API, import and manipulate this data, and make strategic decisions regarding share prices and other market-related trends.
Data science has the potential to revolutionize the way we do business. Whether we are trying to predict stock prices, analyze political data ahead of the next election, or look at years worth of health data to determine whether to adjust health insurance premiums, there is no question that data plays a crucial role in how we create, market, and deliver products.
In addition, analyzing massive amounts of data can help companies create innovative and purposeful marketing strategies. Marketing costs a lot of money, so it’s in a company’s best interest to make informed decisions about which products to market to whom, and where to invest their marketing budget. Data science takes the guesswork out of this.
What do data scientists do?
Data scientist generally work with data to develop algorithms and predict future outcomes for organizations to make better data-driven decisions.
Data scientists can work across industries from marketing, finance, retail, real estate, and a whole host of industries that can leverage data for better decision-making.
A skilled data scientist could analyze millions or billions of pieces of data to help an insurance company make informed choices about spending, to identify potential instances of fraud, to help developers optimize the company’s user interface, to guide marketing decisions, and to identify company-wide opportunities for improvement.
Data scientists are skilled at statistical analysis, interpreting huge amounts of quantitative and qualitative data, creating machine learning tools for companies, and programming—using Python (or R).
But why exactly is programming a necessary component of data science?
Data scientists need to obtain data from local files or from remote databases or they will have nothing to work with! Unlike the kinds of data analysis that we might do on a small scale (such as reviewing how much money a small business spends on X each year), which we can do easily with Excel, data scientists work with thousands or millions of pieces of data at a time.
This data comes from a variety of sources and databases and must be aggregated, cleaned, and analyzed so that it can be used to make strategic decisions and accurate predictions. In these cases, it’s necessary to use some form of scientific computing to analyze and create visualizations of this data.
What’s the difference between data science and data analytics? What is machine learning?
Data science and data analytics are similar fields, but data science is actually an umbrella term that encompasses data analytics, machine learning, and several other data-related disciplines.
- A data analyst is someone who can perform basic data visualization, statistical analysis, and draw conclusions from data sets.
- A data scientist goes a few steps further and handles complex data visualization and modeling, data cleaning, and extensive analysis.
Machine learning is an important component of data science. With machine learning, algorithms are used to analyze data and to predict market trends. A data scientist is generally skilled with both machine learning and data analytics. A machine learning expert is generally skilled at data science but might have a special aptitude for probability, statistics, and programming in multiple languages.
Overall, data science, machine learning, and data analytics require many of the same skills, but the practical application of each specialty differs a bit.
