*Understanding your data first is a key step before going too far into any data science project. But, you can’t fully understand your data until you know the right questions to ask of it.*

Data science is an interdisciplinary field that uses the scientific method, processes, and algorithms to extract knowledge and insights from data. The field of data science has several subdivisions, such as data mining, data transformation, data visualization, machine learning, deep learning, etc. In this article, we will focus on how to ask meaningful questions that could be answered using data. The quality of any analysis performed…

Data science is an interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from data. The field of data science has several subdivisions such as data mining, data transformation, data visualization, machine learning, deep learning, etc. As a scientific discipline, a data science task could be broken into 3 main stages:

Data science skills have become increasingly more important for jobs that once had little to do with statistics, including marketing and business. Adding data science skills to your portfolio will give you an edge in your current role in the market this year. Before getting into data science, use the questions below to determine if data science is the right field for you.

Data Science is such a broad field that includes several subdivisions like data preparation and exploration; data representation and transformation; data visualization and presentation; predictive analytics; machine learning, deep learning, artificial intelligence, etc. There are three levels…

“*In 2021, professionals in the digital market space must be comfortable with data — period. They must know how to manipulate data, understand how it is collected, and analyze and interpret it. The future of decision making is grounded in data science*.” — Wendy Moe, Professor of Marketing, University of Maryland

Data science skills have become increasingly more important for jobs that once had little to do with statistics, including marketing and business. Adding data science skills to your portfolio will give you an edge in your current role in the market this year.

If you are interested in adding…

*Many resources exist for the self-study of data science. In our modern age of information technology, an enormous amount of free learning resources are available to anyone, and with effort and dedication, you can master the fundamentals of data science.*

There are two basic pathways to data science, the traditional college degree pathway, and the self-study pathway.

**Traditional College Degree Pathway**: Several top universities offer traditional graduate-level programs in data science. Because these are graduate-level programs, most will require an undergraduate degree in an analytical field such as physics, mathematics, accounting, business, computer science, or engineering. These programs typically have…

*Can I become a data scientist with little or no math background?*

*What basic math skills are essential for data science practice?*

There exist so many great packages or libraries available for Data Scientists to perform their work. Some of the most common packages for descriptive and predictive analytics include:

*Ggplot2**Matplotlib**Seaborn**Scikit-learn**Caret**TensorFlow**PyTorch**Keras*

However, mathematical skills are still essential in data science and machine learning because these packages will only be black-boxes for which you will not be able to ask core analytical questions without a solid math foundation. A sound math background is therefore…

*Making your big break into the data science profession means standing out to potential employers in a crowd of tough competition. An important way to showcase your skills and experience is through the presentation of a portfolio. Following these recommendations for developing your portfolio will help you network effectively and stay on top of an ever-changing field.*

In the modern age of information technology, there is an enormous amount of free resources for data science self-study. As a matter of fact, you can even design your own data science curriculum from the innumerable amount of available resources. While knowledge acquired…

There are several platforms and programming languages for data science and machine learning project implementation (see** Figure 1 **below).

For levels 1 and 2 data science, mastery of **pandas**, **numpy**, **matplotlib**, and **scikit-learn** libraries is essential. If you master these 4 packages, then you should be able to perform level 1 and 2 tasks using Python, as outline below.

According to IBM, in 2019, businesses were creating and storing almost 2.5 quintillion bytes of data every day. Big Data is big business and businesses are swimming in oceans of valuable data. As one of the fastest-growing, multibillion-billion dollar industries, corporations, and organizations are trying to make the most out of the data they already have and determine what data they still need to capture and store. In addition, there continues to be an incredible need for data scientists to make sense of the numbers and uncover hidden solutions to messy business problems. …

Physicist, Data Science Educator, Writer. Interests: Data Science, Machine Learning, AI, Python & R, Predictive Analytics, Materials Sciences, Biophysics