Data Science is one of the most in-demand and desirable careers of the 21st century.
Even though the term was introduced in the early 1960s, its meaning has changed considerably over time. And despite its rise in popularity in recent years, many people outside the field still find the term confusing and don't know what it entails.
So, what is data science, and what do data scientists actually do?
What is the data science process? Why are data scientists so in demand, and how do they help companies gain more customers and increase their profits?
My aim with this article is to answer those questions and outline some of the skills needed for you to become a data scientist yourself with the help of free resources.
Here is what we will cover:
- What is data science?
- Why is data science important and how data science helps businesses
- What does a data scientist actually do? The data science process explained
- What skills does a data scientist need? How to become a data scientist
Digital data are everywhere nowadays, and we produce large amounts daily.
You produce a lot of data just by going for a walk and scrolling on your phone while listening to your favorite music track on a streaming platform.
You produce data just by uploading a photo to a social media platform or browsing a website looking to buy shoes and then purchasing a pair.
And with each passing year, the amount of data we will all be producing will only continue to increase.
Data science is about collecting and analyzing digital data, extracting and obtaining insights, making informed decisions based on that data, and turning it into meaningful and valuable action.
And this is why data science is necessary for businesses regardless of size - it is the study of extracting insights and transforming data into meaningful and practical information.
The type of data that data scientists analyze can be both structured and unstructured.
Structured data can look like numeric data or text values in an Excel spreadsheet or a Comma-Separated Value (CSV for short) file. Structured data is typically in a tabular format, organized in rows and columns, and stored in a database.
And unstructured data can be data from numbers, text, images, videos, or audio files, to name a few.
Data scientists analyze those large volumes of structured and unstructured data, produce meaningful insights, and make informed decisions.
Data science is a multidisciplinary field that uses different tools, methods, and technologies that change over time.
Specifically, it is the intersection between probability, statistics, mathematics, data analysis, artificial intelligence, machine learning, computer science (algorithms and programming), and business.
As mentioned in the previous section, data science is necessary for businesses because it helps them extract meaningful insights and take actionable steps to reach their goals, grow, and remain competitive in the market.
Data scientists are essential for companies because of the value they provide. They help companies make better and more informed decisions.
Data science allows businesses to uncover new or repetitive patterns, understand trends over time, and visualize the relationships between two things.
Investigating and uncovering such patterns could help a business maximize its profits, increase revenue, and prevent it from experiencing significant losses. Data science can predict and prevent future problems and unfortunate circumstances and protect businesses from loss - which ties in with data science detecting fraud.
Businesses are now able to use data science tools to create accurate fraud detection models to help prevent fraud from happening.
Data science can also be helpful for gathering customer feedback and coming up with new ideas for creating new products and services, as well as solutions to problems that customers face. This can help lead to meeting customers' needs and increasing profit.
By analyzing patterns and reoccurring trends, a business can notice and recognize potential gaps, which leads to innovation, creative solutions, and greater customer satisfaction.
Another reason a data science strategy is essential for the growth of every business is that it can attract new customers via targeted ads.
Essentially, companies use your browsing history to learn more about you and gather insights into which of their products and services may be of interest to you. With those insights at hand, they can show and recommend products and services that are tailored and fit your interests.
What tasks does a data scientist carry out on a day-to-day basis?
The tasks will heavily depend on the company size as well as the sector of the company.
In a smaller company, a data scientist may be the only person responsible for all the data processes. In contrast, in a larger enterprise, a data scientist will most likely be part of a bigger team and have a higher degree of specialization in their role.
Below are the steps involved in the data science process.
The first step in the data science process is asking the right questions, some of which include:
- What happened?
- Why did that happen?
- What kind of information do I need to collect?
- What will happen in the future?
- What is the business trying to achieve?
- What are the current challenges?
- What can be done right now?
In this first step, the goal is to understand the problem at hand as completely as possible and define the right questions that need answering. This first step is crucial for the rest of the process and for gathering the type of data that will help solve the problem.
The next step in the data science process, and a big chunk of a data scientist's work, is extracting and collecting the right kind of data.
This step involves:
- Checking what type of pre-existing data is available to them.
- Collecting new data from selected sources.
Data scientists need plenty of data to work with, and they get hold of data in different ways, some of which include:
- Using internal company data.
- Using public data sets.
- Querying relational databases.
- Conducting market research.
- Conducting surveys.
- Performing web scraping - a technique that extracts information from websites.
- Checking server logs.
- Automatically collecting data via website cookies and third-party sources.
At this stage, the data is raw, meaning it could be corrupt and will likely have missing values and contain mistakes and errors.
Raw data is not usable.
The next step in the data science process, and one of the most important and time-consuming parts of the job, is data cleaning and preparing the cleaned data.
Data cleaning standardizes data to a uniform format.
This step includes:
- Looking for missing data values, asking why they are missing, and filling them in if needed.
- Correcting errors and inaccuracies such as spelling mistakes.
- Removing duplicate values.
- Uncovering corrupt records.
- Dealing with inconsistent data.
- Identifying outliers.
Cleaning data will ensure that there will not be any inaccuracies at the end of the data science process.
Exploring data is essentially analyzing it in-depth to gain a deeper understanding, narrowing down the data that will be crucial for answering the initial questions, uncovering patterns, and extracting meaningful insights. With those new insights, data scientists can go on to provide impactful recommendations.
This step in the data science process involves utilizing statistical methods and data visualization tools for creating diagrams, charts, and graphs to represent evident trends and correlations in the data.
Data scientists use algorithms, machine learning, and artificial intelligence techniques to build, evaluate, deploy and monitor a machine learning predictive model for the data.
They perform hypothesis testing and predict and forecast highly accurate outcomes to determine the best actionable steps for the future.
The last step in the data science process involves communicating and presenting the findings in a compelling and easy-to-understand way to other teams, decision-makers, company executives, stakeholders, and clients. The presentation needs to be accessible to non-technical staff.
Communication skills are one of the most important and underrated skills a data scientist can have in their toolbelt. They are equally as important as the technical skills needed for the job.
This step is also known as data storytelling - the data scientist uses the data and insights they have gathered to interpret and tell a story on the work and explorations they have done, how the business can best use those findings and the conclusions they reached.
During this presentation, the data scientists answer the questions they defined in the first step.
In the following sections, I will outline some of the technical skills you need as an aspiring data scientist.
As a data scientist, you need a good grasp and foundational knowledge of math basics.
But what kind of math is required for data science?
The math requirements and concepts you will need to familiarize yourself with for data science are:
- Linear algebra
- Probability and statistics
Good knowledge of probability and statistics will help you gather and analyze data, figure out patterns, and draw conclusions from the data.
Here are some resources to get you started with calculus:
- Precalculus – Learn College Math Prerequisites with this Free 5-Hour Course
- Learn Calculus 1 in This Free 12-Hour Course
- Learn Calculus 2 in This Free 7-Hour Course
.. linear algebra:
- College Algebra – Learn College Math Prerequisites with this Free 7-Hour Course
- Learn Linear Algebra with This 20-Hour Course and Free Textbook
.. and statistics:
- Statistics for Beginners – Top Stats Concepts to Know Before Getting into Data Science
- Statistics for Data Science — a Complete Guide for Aspiring ML Practitioners
- Learn College-level Statistics in this free 8-hour course
- If you want to learn Data Science, take a few of these statistics classes
Knowledge of algorithms is one of the most important skills in data science.
Here are a couple of the most popular data science algorithms you can start with:
- Linear and logistic regression. A linear regression algorithm is most often used for predictive analysis. It attempts to model the relationship of a variable (also known as the dependent variable) based on the value of another variable (also known as an independent variable). And a logistic regression algorithms is a statistical analysis method used to predict a yes or no outcome.
- Random forest. A random forest algorithm is used for classification and regression problems and combines multiple decision trees into a single model.
One of the most popular programming languages for data science is Python.
Python is a general-purpose programming language, and it is very beginner-friendly (thanks to its readable syntax that resembles the English language) and its versatility.
Python offers a wealth of packages and external libraries for data manipulation, such as Pandas and NumPy, as well as for data visualization, such as Matplotlib.
Below are some free beginner Python resources to get you started:
- Free Python Programming Course 
- How to Code 20 Beginner Python Projects
- Python Fundamentals for Data Science
- Top Python Concepts to Know Before Learning Data Science
Once you understand the fundamentals, you can move on to learning about Pandas, NumPy, and Matplotlib.
- The Ultimate Guide to the NumPy Package for Scientific Computing in Python
- How to Get Started with Pandas in Python – a Beginner's Guide
- Matplotlib Course – Learn Python Data Visualization
- How to Analyze Data with Python, Pandas & Numpy - 10 Hour Course
- Python Data Science – A Free 12-Hour Course for Beginners. Learn Pandas, NumPy, Matplotlib, and More.
Another programming language used in data science is R. This programming language was designed specifically for statistical computing, statistical analysis, data analysis, and data manipulation.
To get started learning R, check out the following resources:
- R Programming Language Explained
- Learn R programming language basics in just 2 hours with this free course on statistical programming
Data scientists need to know how to interact with a database system, such as a relational database, to organize, store, and extract a large amount of data.
A database is an electronic storage localization for data. Data can be easily retrieved and searched through.
A relational database is structured in format and all data items stored have pre-defined relationships with each other.
And this is where SQL comes in. SQL stands for Structured Query Language and is used for accessing, querying, manipulating, and interacting with relational databases.
With SQL queries, you can perform CRUD (Create, Read, Update, and Delete) operations on data.
To learn SQL, check out the following resources:
- Why You Should Learn SQL if You Want a Data Science Job
- Learn SQL – Free Relational Database Courses for Beginners
- SQL Commands Cheat Sheet – How to Learn SQL in 10 Minutes
- Learn SQL with These 5 Easy Recipes
- SQL and Databases - A Full Course for Beginners
- Relational Database Certification
Data visualization is the graphical interpretation and presentation of data - this includes creating graphs, charts, interactive dashboards, or maps that can be easily shared with other team members and stakeholders.
Data visualization tools are used to tell a story with data and drive decision-making.
One of the most popular data visualization tools used is Tableau.
To learn Tableau, check out the following course:
Machine Learning (or ML for short) is the intersection of artificial intelligence (short for AI) and computer science.
Computer systems learn how to perform a specific task without being explicitly programmed.
Machine learning enables systems to learn, recognize and identify statistical patterns, improve, and become more accurate from experience.
And data scientists use machine learning extensively and incorporate it into their work.
Here are some machine learning resources to get you started:
- What is Machine Learning? ML Tutorial for Beginners
- AI vs ML – What’s the Difference Between Artificial Intelligence and Machine Learning?
- How to Learn Machine Learning – Tips and Resources to Learn ML the Practical Way
- Free 10-Hour Machine Learning Course
- 10 Best Machine Learning Courses to Take in 2022
This marks the end of the article – thank you so much for making it to the end!
Hopefully, this guide was helpful, and it gave you some insight into what data science is, what a data scientist actually does, what the data science process entails, and what skills you need to enter the field.
Thank you for reading!