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Have you been wanting to explore the area of data science in 2023 but don’t know where to start?

Then this blog post is for you!

In this post, I’ll walk you through a five-step guide on how to get started in data science in 2023. I’ll share the skills you’ll need to learn, some great resources that you should use, and tips on how to find a job in the data science field.

Let’s get started!

Step 1: Learn the Basic Data Science Skills

The first step to becoming a data scientist is to learn the skills that are needed for the job.

To become a data scientist, you’ll have to know coding, statistics, machine learning, visualization, and data storytelling.

If you’re new to coding and programming languages, you should start with Python. You will also need to have a good understanding of Python or R for data analysis.

For most of my learning purposes when it comes to data science, I always end up picking Coursera. Coursera is a fantastic learning resource that offers you thousands of courses from top universities like Stanford and Imperial College and big tech companies like Google and IBM.

If you’re learning Python for the first time, online courses like the IBM Data Analyst Professional Certificate can be a good starting point for picking up the language. It gives a good introduction to basic coding concepts and specific applications of Python in data science.

Alternatively, you can consider the Python for Everybody Specialization from Coursera! This course is one of Coursera’s popular online courses for picking up Python for the first time. This should provide you with a good introduction too. If you’re still considering, you might want to check out my review of the course.

A good place to start learning R for data analytics is the Google Data Analytics Professional Certificate! I personally went through this excellent course, and it gave me a good foundation for data analytics.

For machine learning, you will want to become familiar with the popular algorithms used for predictive modeling. Statistics is another important skill for data scientists, and it’s important to understand concepts such as hypothesis testing, regression analysis, and time series forecasting.

If you’re serious about learning about statistics and machine learning, I’d recommend the IBM Data Science Professional Certificate—it’s one of the best out there!

In this certificate, you’ll learn about key machine learning concepts and the fundamentals of statistical analysis required for all data scientist jobs.

You should also become familiar with big data technologies such as Hadoop and Spark. These allow you to store, process, and analyze large amounts of data efficiently.

Finally, you’ll need to have a good understanding of data visualization so that you can effectively communicate findings to your team and stakeholders.

Some common data visualization tools used by data scientists are:

  • Tableau
  • Power BI
  • Matplotlib
  • Seaborn

If you’re looking to learn, Tableau, the Google Data Analytics Professional Certificate, also provides a great introduction to this business intelligence tool.

Creating compelling and clear data visualizations is also a crucial part of a data science job.

Having some soft communication skills in data storytelling would help you go a long way and stand out from other data scientists out there.

With good storytelling skills, you’ll be able to provide a more convincing and compelling point when presenting insights!

These skills are also very applicable to data analysts and data engineers too!

Pro Tip: If you’re planning to get multiple courses from Coursera, you should consider getting their Coursera Plus Annual Plan! Get $200 OFF Coursera Plus until Jan 31 using this link.

I’m an avid learner, and I’ve been using Coursera Plus for about half a year now! I really like how it provides full access to 7000+ courses just through a one-time payment.

If you’re dedicated to learning about data science in 2023, you should consider it too!

Step 2: Get Familiar with Databases

Once you understand the skills required for data science, it’s time to start getting familiar with the databases needed for a data science career.

This includes relational databases like MySQL and PostgreSQL and NoSQL databases like MongoDB and Cassandra.

To learn relational databases, you’ll need to understand SQL well. One good resource to get a good foundation is the Data Science Fundamentals with Python and SQL Specialization from Coursera.

As for NoSQL databases like MongoDB, you’ll need to understand the basics of how they store data, their data structure, and their query language.

They’re quite different from how you query data from relational databases, so you’ll need to do some self-learning.

In my previous experience as a data analyst and data scientist, I had to learn to query and analyze data from several different types of databases.

Therefore, I’d recommend really trying out and getting used to the 2 most common databases—MySQL and MongoDB.

Step 3: Start Working on Projects

Now that you understand the data skills required for databases, it’s time to start working on some projects!

Projects are my favorite way to learn data science. They’re just so much more effective!

This is a great way to build up your portfolio and demonstrate your skills to potential employers. You can find some interesting data science projects online or create your own.

For example, you could use publicly available datasets such as Kaggle’s datasets to explore different aspects of data analysis and create an engaging data visualization.

You can also consider doing a capstone project with a certificate like the Google Data Analytics Certification.

Alternatively, you can learn to gather your own datasets if you’re more confident with your skills.

You can try the following:

  • Using Natural Language Processing (NLP) to analyze text data from APIs.
  • Using web scraping techniques to extract data from websites or web APIs.

If you’re going to learn NLP, I highly recommend getting a well-structured course to help you with your learning process. In this case, the Natural Language Processing Specialization by Coursera is a great choice.

You can also work on open-source projects, such as those available on GitHub, to build your portfolio and collaborate with other talented data scientists.

When working on a more advanced data science project, you might need to access data from multiple sources. To help you with this, you should become familiar with tools such as Apache Airflow and AWS Glue that allow you to automate the process of extracting, transforming, and loading (ETL) data.

Step 4: Develop Your Network

As a data scientist in 2023, it’s important to start developing your network. Networking is an invaluable tool for data scientists, as it helps you to build connections with other professionals in the field and develop relationships.

Who knows? These might even lead to great job opportunities!

With all the skills you’ve gained through the various data science projects, you’ll be more than ready to share your work with your network.

One amazing way to network (that I personally use) is LinkedIn. In fact, my first job as a data analyst came about through the use of LinkedIn! My subsequent data scientist role also came about through networking with like-minded people in my industry (healthcare and life sciences).

You should also try reaching out to people in your industry and chatting them up!

Data science jobs are in demand; you’ll want to put yourself out there.

You can join online communities such as Kaggle or Stack Overflow, where you can interact with other data scientists and ask questions.

You can also attend industry events, conferences, and hackathons to help you stay current and get noticed by recruiters.

Being active in the data science community is a great way to network and build relationships with potential employers.

There are lots of data science-related events, conferences, and meetups that you can attend to learn more about the field and make valuable connections.

Step 5: Build an Online Portfolio

Having a strong online presence is essential if you want to get hired in the data science field in 2023. It’s essential to create an online portfolio to showcase your projects and skills to future potential employers.

Think of your online portfolio as an extension of your resume!

Your portfolio should include the following:

  • An overview of your relevant education, experience, and technical skills
  • Links to your GitHub profile and any other source code repositories that you use
  • Any data science projects that you have completed, along with an explanation of the techniques used
  • Links to any relevant blog posts or articles that you have written about data science topics

Your portfolio should also include a description of yourself and what makes you unique. This will help potential employers get to know you better and understand why you will be a great fit for their team.

Final Thoughts

Now that you know the steps to get started on your data science journey, it’s time to take action!

Start developing your skills by working on projects, attending events and conferences to build your network, and creating an online portfolio so employers can find out more about you.

I hope this article helps you reach your goal of getting started in data science in 2023!