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Data Science remains a popular career choice, but the role has evolved significantly. Modern data scientists need to be versatile professionals who can not only analyze data but also deploy models to production, write clean code, and collaborate with teams using tools like Git.
This guide outlines 10 essential steps to become job-ready as a data scientist. You will learn key programming languages, tools, and concepts covering data management, analysis, visualization, machine learning, reporting, and model deployment.
1. Introduction to Data Science
Watching YouTube videos on data science is a great way to learn basic terminologies, processing techniques, and the scope of the field. You will discover how vast and versatile data science is, encompassing subfields like business intelligence, data analytics, computer vision, and natural language processing. These resources can provide valuable insights and practical knowledge to help you understand and navigate the complexities of data science effectively.
2. Master Python and SQL
Both Python and SQL are essential if you want to become a professional data scientist. Most popular data analytics, visualization, and machine learning tools are built in Python, and it is relatively easy to create and run your own scripts. To access data from a database, you need to have a basic understanding of SQL and how to load, process, and analyze the data using queries.
3. Learn Statistics and Probability
Statistical and probability concepts form the foundation of data science, underpinning analytical methodologies, machine learning, and data processing techniques. You need to learn the math behind these technologies to understand and then improve these algorithms for a particular use case.
4. Master Data Management
In the data manager, you will learn how to load data from various sources, including databases, CSV files, and JSON. Additionally, you will learn how to resolve common data issues such as maintaining data formats, handling missing data, reshaping and joining datasets, and validating data.
5. Perform Data Analytics on Real World Data
Data analysis involves multiple key steps: loading, processing, manipulation, and analysis – all aimed at achieving specific business goals. Through hands-on practice with real datasets, you will learn to apply these techniques effectively. You will master the Pandas, NumPy, and Matplotlib Python libraries that are essential for data loading and manipulation.
6. Master of Data Visualization
The art of data visualization is hard to master. You need to learn about each plot and graph and understand when to use them. It’s important to study color patterns, labels, and other components that make it easy for individuals, especially those without a technical background, to comprehend the underlying information. Essentially, you are analyzing the data using code and visualization, telling a story to a general audience.
7. Learn the Basics of Machine Learning
You need to learn about various types of machine learning models, including supervised machine learning, unsupervised learning, and semi-supervised learning. You will also explore subfields of machine learning, such as computer vision, natural language processing, deep learning, reinforcement learning, large language models, generative technology, and more. Additionally, you will master the Scikit-learn Python framework for simple machine learning tasks.
8. Work on the Data Analytical Report
It’s important to distinguish between a data scientist and a data analyst. A data scientist is responsible for analyzing data and presenting findings in a report. Each visualization and concept used should be explained in simple terms to effectively communicate results.
For example, if a company tasks you with identifying underlying patterns in consumer purchases on an e-commerce platform, your job is to analyze the data and provide a detailed report on your findings. You should explain how the data can be leveraged to target specific individuals in order to increase profits.
9. Build a Data Science Portfolio
The most important part of your job will be maintaining your portfolio and showcasing your projects. You should have a few projects on GitHub and Kaggle, as well as some blog posts on Medium or your personal website. Additionally, an active LinkedIn account is essential. All of these elements will help you build your personal brand, open doors for job opportunities, and effectively market yourself.
10. Deploy Machine Learning Models
The production aspect is optional. However, if you look at job descriptions, most companies want their data scientists to be familiar with cloud platforms like AWS or GCP. They also expect experience with Docker and Kubernetes, as well as familiarity with FastAPI or any model serving framework. Things are changing, so gaining experience in deploying models into production, especially on AWS, will enhance your resume and help you secure higher-paying jobs.
Final Thought
Data science is far from dead; it’s the backbone of modern AI and technologies like ChatGPT. If you want to make an impact in the world using technology, data science is the right field for you. You will learn to understand data and provide recommendations to companies on improving profits or customer satisfaction. It is not limited to business alone—data science is transforming hospitals, agriculture, sports, and gaming. There is high demand for data science professionals, and with a solid foundation, you can smoothly transition into niches like computer vision engineering or MLOps engineering.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
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