Data engineer, data analyst, and data scientist — these are job titles you'll often hear mentioned together when people are talking about the fast-growing field of data science.
Last week we discovered the secrets behind a data engineer, today we are going to learn more about the role of a data scientist.
A data scientist is a specialist who applies their expertise in statistics and building machine learning models to make predictions and answer key business questions.
A data scientist still needs to be able to clean, analyze, and visualize data, just like a data analyst. However, a data scientist will have more depth and expertise in these skills, and will also be able to train and optimize machine learning models.
What does a Data Scientist do?
Data scientists bring an entirely new approach and perspective to understanding data. While an analyst may be able to describe trends and translate those results into business terms, the scientist will raise new questions and be able to build models to make predictions based on new data.
The following are examples of work performed by data scientists:
Evaluating statistical models to determine the validity of analyses.
Using machine learning to build better predictive algorithms.
Testing and continuously improving the accuracy of machine learning models.
Building data visualizations to summarize the conclusion of advanced analysis.
As stated by the United States of Labor Statistics, the employment of all computer and information research scientists is expected to rise 19 percent by the year 2026, which is deemed much faster than the average for all professions. About 5,400 new jobs are projected over the decade. As the demand for new and improved technology increases in the data science field, the demand for qualified data scientists will rise. The rapid growth in data collection will result in a heightened need for data-mining services.
How about the pay?
The new increasing demand for data scientists results in competitive starting and continuing salaries. According to Payscale, the average pay for data scientists is about $91,000 per year, with the top 10 percent earning more than $120,000 and the lowest 10 percent making less than $62,000 per year. Actual annual pay varies on an array of factors, including location, industry, employer, skills, experience, and job duties.
Portuguese Tech Salaries Report — 2019 Edition
The big data technology and services market is expected to reach $58.9 billion in 2020, market research firm International Data Corp, or IDC, predicted. As the industry records growth, there is a surge in demand for data processing and analysis expertise.
Data scientists are vital to a variety of organizations as they are a part computer scientist, mathematician, and domain expert. The increasing popularity of this profession is a reflection of how entities think about big data. These professionals help increase revenue and discover business insights to boost production. For any organization seeking to enrich their business and becoming more data-driven, data scientists are the answer.
Every Data Scientist Ninja must have their tools! And so there are multiple skills that are required for a Data Scientist ranging across different fields. Most of them are mentioned below:
1. Statistical Analysis
As a Data Scientist, your primary job is to collect, analyze and interpret large amounts of data and produce actionable insights for a company. So obviously Statistical Analysis is a big part of the job description!!!
That means you should be familiar with at least the basics of Statistical Analysis including statistical tests, distributions, linear regression, probability theory, maximum likelihood estimators, etc. And that’s not enough! While it is important to understand which statistical techniques are a valid approach for a given data problem, it is even more important to understand which ones aren’t.
2. Programming Skills:
Programming Skills are a necessary tool in your arsenal as a Data Scientist! That’s because it is much easier to study and understand data in order to draw useful conclusions if you can use certain algorithms according to your needs.
Python is the most popular language of Data Science and will be the one we taught throughout the Bootcamp. If you already know Ruby, you’ll find a lot of similarities, that’s perfectly normal! Libraries like numpy, pandas, scikit-learn, keras are all written for Python. The bootcamp will dive into those libraries from the first day, that’s why this preparation work is very important to get the basics right.
You can start testing your data skills with our free online data workshops: SQL for beginners, Data Analystics with Python and Web Scraping with Python. You can register here.
3. Machine Learning:
If you are in any way connected to the tech industry, chances are you have heard of Machine Learning! It basically enables machines to learn a task from experience without programming them specifically. This is done by training the machines using various machine learning models using the data and different algorithms.
So you need to be familiar with Supervised and Unsupervised Learning algorithms in Machine Learning like Linear Regression, Logistic Regression, K-means Clustering, Decision Tree, K Nearest Neighbor, etc. Luckily, most of the Machine Learning algorithms can be implemented using R or Python libraries (mentioned above!) so you don’t need to be an expert on them. What you need expertise on is the ability to understand which algorithm is required based on the type of data you have and the task you are trying to automate.
Data plays a big part in the life of a Data Scientist (Obviously!). So you need to be proficient in Data Management which involves Data Extraction, Transformation, and Loading. This means that you have to extract the data from various sources, then transform it in the required format for analysis and finally load it into a data warehouse. To handle this data, there are various frameworks available like Hadoop, Spark, etc.
Now that you are done with the process of Data Management, you also need to be familiar with Data Wrangling. Now, what is Data Wrangling you ask? Well, it basically means that the data in the warehouse needs to be cleaned and unified in a coherent manner before it can be analyzed to obtain any actionable insights.
5. Data Intuition:
Don’t underestimate the power of Data Intuition! In fact, it is the primary non-technical skill that sets a Data Scientist apart from a Data Analyst. Data Intuition basically involves finding patterns in the data where there are none! This is almost like finding the needle in the haystack which is the actual potential in the huge unexplored pile of data.
Data Intuition is not a skill that you can be easily taught. Rather it comes from experience and continued practice. And this, in turn, makes you much more efficient and valuable in your role as a Data Scientist.
6. Communication Skills:
You must be great at Communication Skills as well in order to become an expert Data Scientist! That’s because while you understand the data better than anyone else, you need to translate your data findings into quantified insights for a non-technical team to aide in the decision making.
This can also involve data storytelling! So you should be able to present your data in a storytelling format with concrete results and values so that other people can understand what you are saying. That’s because eventually, the data analysis is less important than the actionable insights that can be obtained from the data which will, in turn, lead to business growth.
There are many resources online, but don’t have the mistaken impression that the data scientist career path is as simple as taking a few MOOCs. Unless you already have a strong quantitative background, the road to becoming a data scientist will be challenging – but not impossible.
However, if it’s something you’re sincerely interested in and have a passion for data and lifelong learning, don’t let your background discourage you from pursuing data science as a career.