Breaking into top tech jobs: How to become a data scientist

So, you want to begin your data science career, but don’t know where to start? Not a problem—I’ve written this handy guide on how to land a job in data science, full of career tips and tricks to help you along the way.

Learn the required skills

Data science is the field of extracting valuable insights from raw data, which can be used in business decision-making. To do that, a data scientist needs to have a wide range of technical skills, including statistics, machine learning, natural language processing, and the rapidly growing field of Gen AI. 

Overwhelmed by that list of requirements? Don’t worry! Just like any other field, you can pick these skills up as you go along, and some are more critical than others. To that end, here is a list of seven key skills you should focus on as an entry-level data scientist. 

As you go through this list, just remember the old proverb: “A journey of a thousand miles begins with a single step.” Learn one thing at a time, and after a while you’ll be surprised how far you’ve come.

1. Foundational math 

Math is the bread and butter of being a data scientist. All machine learning models and algorithms are fundamentally built on mathematical principles, and as your data science career progresses, you’ll use many advanced math techniques. To get started, though, focus on foundational algebra and statistics concepts.

2. Python programming

According to industry research, between 62% to 77% of all data science job postings require Python. The reason? It has a vast number of libraries specific to data science and machine learning.

As an aspiring data scientist, you should learn not only Python, but also become familiar with its data manipulation libraries (pandas, NumPy, and Matplotlib) and ML packages (scikit-learn, Keras, and Tensorflow). Here is a step by step guide to learn Python programming for data science. 

3. SQL

SQL is another must-have skill for any data science career, whether as a data analyst, business analyst, or data scientist, since it lets you access and manipulate data stored in relational databases. Check out this SQL introductory course for data manipulation.

4. Data exploration and visualization

Data preparation is a crucial step in any data science project. The process involves analyzing and visualizing data to identify key insights and patterns. You might also manipulate the data in this step to make it suitable to your use case. This is where you will use the Pandas, Numpy, Seaborn, and other library skills you learned before. 

In addition to Python packages, sophisticated tools like Power BI and Tableau can be used to build compelling visualizations. 

To develop these skills, check out this course on exploratory data analytics techniques in Python. Also, try this free data visualization course in Python. It covers all essential data visualization techniques and how to implement them.

5. Machine learning and deep learning 

These are often the most exciting parts of data science to learn! Machine learning contains math algorithms to create data models that can predict future outcomes, enhancing business decision-making. Meanwhile, deep learning is a step further to machine learning that uses more powerful techniques like artificial neural networks to make predictions.

To start learning ML, here is a complete machine learning path you can take, starting with the basics all the way to advanced topics. For deep learning, there’s a similar path you can check out.

6. Natural language processing

Text is the most common format in human communication. Natural language processing enables computers to understand the text data and extract meaningful insights from it, which is why this sub-field is on the rise in the data industry. This technology has also become the base for the most popular text-handling AIs like Chat GPT, BARD, and Gemini. 

As a data practitioner, you should at least have a basic understanding of this technology and its various text-handling techniques. If you want to cover all parts of natural language processing, from basic to advanced, check out this structured learning path. However, if you feel the basics are enough, get started with natural language processing here. 

7. Big data 

With data generation expected to reach 180 zettabytes by 2025, data professionals often need to handle massive data. Thankfully, technologies like Apache Spark, Apache Hadoop, Elasticsearch, and Hive in the Big data ecosystem have made it easy for data practitioners to process big data quickly at scale. 

To learn the fundamentals of big data and about the different management and processing tools available, check out this big data course. You can also learn the complete AWS big data ecosystem with this organized learning path. 

Build projects (and engage in competitions)

One of the best ways to reinforce all you’ve learned—and showcase your skills—is to create your own data science projects. Using a your work provided or individual professional Pluralsight subscription provides access to hands-on labs you can use to build up your data science skills and portfolio to get a foot in the industry.

Start your data science portfolio

You can use what you've learned from online courses and hands-on labs to create your own portfolio of projects. Start by using open-source datasets like those on Github. If you're interested in the cinema industry, for example, you could analyze a movie dataset to uncover valuable insights such as box office trends, audience preferences, or genre popularity. 

Once you are comfortable handling and analyzing data, start with industry-specific projects. A site like Kaggle also has numerous open-source data sets of different types in different fields, which you can pick and analyze. If you are interested in building end-to-end projects using health-specific or big retail datasets, you can find them here.

Use Kaggle for free data science competitions

Kaggle is also the go-to place for anyone interested to participate in worldwide data competitions for free. In aditino to their well-known competitions, Kaggle has tons of interesting data problem statements that you can solve and add to your resume. 

Network and apply for Jobs

Networking is one of the most important skills you can develop for starting (and advancing) any tech career, not just in data science. Here are some steps you can take:

Build an online presence: I highly recommend you to be active on LinkedIn and other professional communities as the first step to building the professional network that might help you with an opportunity to land your dream job or project.

Attend events and conferences: Many companies conduct data conferences and professional events to share knowledge and industry trends. Attending these will keep you updated with what’s happening in the industry and, of course, make new friends in the event.  

Search online job portals: With the mentioned data skills, you will be eligible to apply for data analyst, data scientist, business analyst, and BI developer roles. Apply for them on different job portals like Indeed, Monster, ZipRecruiter, LinkedIn, and others. Just a heads up, job searching can be a tiring task, and never giving up is the only key for success here. 

Into the world of Data Science 

The fact that you've reached the end of this long article is a good sign that you've got clear steps to get into a data science career. Data science is a highly rewarding profession where you work on industry problems using tech, and you can witness your impact on the organization's growth.