Data science is one of the most exciting and rewarding skills in the tech industry. A data scientist is someone who analyzes and interprets large and complex data sets using various tools and techniques such as statistics, machine learning, programming, visualization, etc. Data scientists can work in various domains such as business, healthcare, education, finance, social media, etc.

Data science is also one of the most in-demand skills in the tech industry. According to LinkedIn, data science was the third most in-demand skill in 2020, and it continues to be in high demand in 2023. According to Glassdoor, the average salary of a data scientist in the US is $113,000 per year. Moreover, according to IBM, the demand for data scientists will grow by 28% by 2020.

So how can you become a data scientist and land your dream job in the tech industry? Here are some steps to help you get started:

Learn the basics of data science

Before you dive into data science, you need to learn the basics of data science. This means understanding what data science is, what data scientists do, and what are the skills and tools required for data science.

Data science is the process of extracting insights and knowledge from data using various tools and techniques such as statistics, machine learning, programming, visualization, etc. Data science can be used for various purposes such as finding patterns, making predictions, solving problems, optimizing decisions, creating products, etc.

Data scientists are professionals who apply data science to various domains and problems. Data scientists typically perform tasks such as collecting and cleaning data, exploring and analyzing data, building and testing models, communicating and presenting results, etc.

Data science requires a combination of skills and tools such as:

Mathematics and statistics
Programming and coding
Machine learning and artificial intelligence
Data visualization and storytelling
Domain knowledge and problem-solving

You can learn the basics of data science online through various courses, tutorials, and resources. Some popular platforms for learning data science are:

Coursera
Udemy
edX
FutureLearn
Khan Academy

Choose a programming language

Once you have learned the basics of data science, you can move on to choosing a programming language. A programming language is a set of rules and symbols that allows you to write instructions for computers to perform tasks. A programming language can also provide you with libraries and frameworks that simplify and speed up your work.

There are many programming languages to choose from, but some of the most popular ones for data science are:

Python
R
SQL
Julia
Scala

Each language has its own advantages and disadvantages, so you need to do some research and find out which one suits your needs and preferences.

You can learn a programming language online through various courses, tutorials, and resources. Some popular platforms for learning a programming language are:

Coursera
Udemy
edX
FutureLearn
Khan Academy

Learn machine learning

Another important skill for data science is machine learning. Machine learning is a branch of artificial intelligence that allows computers to learn from data without being explicitly programmed. Machine learning can be used for various purposes such as classification, regression, clustering, recommendation, anomaly detection, etc.

Machine learning involves concepts and techniques such as:

Supervised learning
Unsupervised learning
Reinforcement learning
Deep learning
Neural networks
Natural language processing
Computer vision

You can learn machine learning online through various courses, tutorials, and resources. Some popular platforms for learning machine learning are:

Coursera
Udemy
edX
FutureLearn
Khan Academy

Build projects

The best way to learn data science is by building projects. Projects allow you to apply what you have learned and showcase your skills and creativity.

You can start by building simple projects that use basic data science concepts such as importing and manipulating data, performing descriptive and inferential statistics, creating simple and interactive visualizations, etc. Then, you can gradually move on to more complex projects that use advanced data science techniques such as applying machine learning algorithms, building predictive models, deploying web applications, etc.

Some examples of projects you can build are:

A Titanic survival analysis
A Netflix movie recommendation system
A spam email classifier
A face recognition system
A sentiment analysis of tweets

You can find inspiration and ideas for projects online through various platforms such as Kaggle, DataCamp, DrivenData, Google Colab, and Streamlit.

You can also use platforms such as GitHub, Jupyter Notebook, Anaconda, or Google Cloud Platform to host your projects online and share them with others.

Get certified

Another way to become a data scientist is to get certified. Getting certified means obtaining a credential or certificate that validates your knowledge and skills in a specific data science domain or role. Getting certified can help you boost your credibility and confidence, stand out from the crowd, and advance your career.

There are many certifications to choose from, but some of the most popular ones are:

IBM Data Science Professional Certificate
Google Data Analytics Professional Certificate
Microsoft Certified: Azure Data Scientist Associate
SAS Certified Data Scientist
Cloudera Certified Data Engineer

Each certification has its own requirements and benefits, so you need to do some research and find out which one suits your needs and preferences.

You can prepare for different certifications online through various courses, tutorials, and resources. Some popular platforms for preparing for certifications are:

Coursera
Udemy
edX
FutureLearn
Khan Academy

Keep learning

Data science is an ever-changing field that requires constant learning and updating. New data sources, technologies, tools, and trends emerge every day that can affect your work and career. Therefore, you need to keep learning new skills and improving your existing ones.

You can keep learning by following online courses, tutorials, and resources that cover the latest developments in data science. Some popular platforms for keeping up with new skills are:

Coursera
Udemy
edX
FutureLearn
Khan Academy

You can also keep learning by reading blogs, articles, books, podcasts, and newsletters that cover the latest news and insights on data science. Some popular sources for keeping up with new trends are:

Towards Data Science
KDnuggets
Data Science Central
O’Reilly Data Show Podcast
Dataquest Newsletter