You’re an industrial engineer who wants to move into data science. What skills do you need to learn first?

1 Programming languages

One of the most essential skills for data science is programming. You need to be able to write, run, and debug code to manipulate, analyze, and visualize data. There are many programming languages that are used for data science, but some of the most popular ones are Python, R, SQL, and SAS. Python and R are general-purpose languages that have many libraries and packages for data science, such as pandas, numpy, scikit-learn, ggplot2, and dplyr. SQL is a language for querying and manipulating data from relational databases, such as MySQL, PostgreSQL, and Oracle. SAS is a software suite that offers statistical analysis, data management, and reporting capabilities. You don’t need to learn all of these languages, but you should at least master one or two of them and be familiar with the others.

2 Statistics and mathematics

Another core skill for data science is statistics and mathematics. You need to be able to understand, apply, and interpret statistical concepts and methods, such as descriptive statistics, inferential statistics, hypothesis testing, confidence intervals, correlation, regression, ANOVA, and chi-square. You also need to be able to use mathematical tools and techniques, such as linear algebra, calculus, optimization, and discrete mathematics. These skills will help you to perform data analysis, build models, and solve problems.

3 Machine learning and artificial intelligence

A more advanced skill for data science is machine learning and artificial intelligence. Machine learning is a branch of artificial intelligence that uses algorithms and data to learn from experience and make predictions or decisions. Artificial intelligence is a broader term that encompasses machine learning and other fields, such as natural language processing, computer vision, and robotics. You need to be able to understand, implement, and evaluate machine learning and artificial intelligence techniques, such as supervised learning, unsupervised learning, reinforcement learning, classification, clustering, regression, neural networks, deep learning, and computer vision. You also need to be aware of the ethical and social implications of using these techniques.

4 Data engineering and management

A complementary skill for data science is data engineering and management. Data engineering is the process of designing, building, and maintaining data pipelines, architectures, and systems. Data management is the process of collecting, storing, organizing, and accessing data. You need to be able to work with different types of data sources, such as structured, unstructured, semi-structured, streaming, and web data. You also need to be able to use various data tools and platforms, such as Hadoop, Spark, Kafka, MongoDB, AWS, and Azure. These skills will help you to handle large, complex, and diverse data sets and prepare them for analysis.

5 Communication and visualization

A final skill for data science is communication and visualization. Communication is the ability to explain, present, and persuade using data. Visualization is the ability to create, use, and interpret graphical representations of data. You need to be able to communicate effectively with different audiences, such as technical, business, and general. You also need to be able to use various visualization tools and techniques, such as charts, graphs, maps, dashboards, and interactive displays. These skills will help you to convey your findings, insights, and recommendations in a clear, concise, and compelling way.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?