Without professionals who turn cutting-edge technology into actionable insights, Big Data is nothing. In Today’s world , more and more organizations are opening up their doors to big data thereby increasing the value of a data scientist who knows how to tease actionable insights out of gigabytes of data.
But what are some of the skills needed for a data scientist to stay on top ;
- Knowledge of machine learning
Because data science is a broad term for multiple disciplines, machine learning fits within data science. Machine learning uses various techniques, such as regression and supervised clustering.
Data science needs the application of skills in different areas of machine learning. In a recent survey, Kaggle, an online community of data scientists and machine learners, owned by Google have revealed that a small percentage of data professionals are competent in advanced machine learning skills such as Supervised machine learning, Unsupervised machine learning, Time series, Natural language processing, Outlier detection, Computer vision, Recommendation engines, Survival analysis, Reinforcement learning, and Adversarial learning.
Data science involves working with large amounts of data sets, so you need to be familiar with Machine learning.
2. Data Visualization
The business world produces a vast amount of data frequently. This data needs to be translated into a format that will be easy to comprehend. People naturally understand pictures in forms of charts and graphs more than raw data. An idiom says “A picture is worth a thousand words”.
As a data scientist, you must be able to visualize data with the aid of data visualization tools such as ggplot, d3.js and Matplottlib, and Tableau. These tools will help you to convert complex results from your projects to a format that will be easy to comprehend. The thing is, a lot of people do not understand serial correlation or p values. You need to show them visually what those terms represent in your results.
Data visualization gives organizations the opportunity to work with data directly. They can quickly grasp insights that will help them to act on new business opportunities and stay ahead of competitions.
3. Strong Business Acumen
It is important for a data scientist to be a tactical business consultant. Working so closely with data, data scientists are positioned to learn from data in ways no one else can. That creates the responsibility to translate observations to shared knowledge, and contribute to strategy on how to solve core business problems. This means a core competency of data science is using data to cogently tell a story. No data-puking – rather, present a cohesive narrative of problem and solution, using data insights as supporting pillars, that lead to guidance.
Having this business acumen is just as important as having acumen for tech and algorithms. There needs to be clear alignment between data science projects and business goals. Ultimately, the value doesn’t come from data, math, and tech itself. It comes from leveraging all of the above to build valuable capabilities and have strong business influence.
4. Basic Knowledge of Mathematics and Statistics
At the heart of mining data insight and building data product is the ability to view the data through a quantitative lens. There are textures, dimensions, and correlations in data that can be expressed mathematically. Finding solutions utilizing data becomes a brain teaser of heuristics and quantitative technique. Solutions to many business problems involve building analytic models grounded in the hard math, where being able to understand the underlying mechanics of those models is key to success in building them.
Also, a misconception is that data science all about statistics. While statistics is important, it is not the only type of math utilized. First, there are two branches of statistics – classical statistics and Bayesian statistics. When most people refer to stats they are generally referring to classical stats, but knowledge of both types is helpful. Furthermore, many techniques and machine learning algorithms lean on knowledge of linear algebra.
For example, a popular method to discover hidden characteristics in a data set is SVD, which is grounded in matrix math and has much less to do with classical stats. Overall, it is helpful for data scientists to have breadth and depth in their knowledge of mathematics.
5. Knowledge of Python and R Programming Language
Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists. This is why 40 percent of respondents surveyed by O’Reilly use Python as their major programming language.
Because of its versatility, you can use Python for almost all the steps involved in data science processes. It can take various formats of data and you can easily import SQL tables into your code. It allows you to create datasets and you can literally find any type of data set you need on Google.
In-depth knowledge of at least one of these analytical tools, for data science R is generally preferred. R is specifically designed for data science needs. You can use R to solve any problem you encounter in data science. In fact, 43 percent of data scientists are using R to solve statistical problems. However, R has a steep learning curve.
It is difficult to learn especially if you already mastered a programming language.
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