Data science is a growing field that relates to creating and maintaining digital systems that store and analyze information. One way to learn more about data science is to take a course about the subject. Taking a class about data science can help you better understand basic data science concepts and may connect you to someone in the industry who can hire you for a position. In this article, we discuss what a data science course is and list 10 popular ones that you can take to improve your knowledge of data science.
What are data science courses?
Data science courses are any class or set of classes that teach you concepts about the subject of data science. Many of these courses occur online, though there are some options you may find at your local community college or university. Some topics that data science courses may teach include how to code particular data science programs, statistics, data visualization and data analysis.
10 data science courses
Here are a few courses that can teach you more about data science:
1. Data Science Specialization
Johns Hopkins University hosts this Data Science Specialization course on the digital education site Coursera, which offers free classes with an option to purchase a certification after completion. The course features 10 separate classes taught by actual professors from Johns Hopkins University. It’s a self-paced course, with video and written resources that you can keep after your enrollment.
2. Introduction to Data Science
This Introduction to Data Science course on Metis is a live six-week class meant for beginners that covers basic data science concepts. The live lectures occur twice a week and are accredited courses you can use toward a college degree. There’s also a certification you can receive at the end of the course, which is already a part of enrolling in the course.
3. Applied Data Science with Python specialization
Applied Data Science with Python specialization is an online data science course on Coursera offered by the University of Michigan. It teaches how to perform inferential statistical analysis, create data visualizations, determine if your visualizations are effective and use machine learning to improve the efficiency of your analysis. This course focuses on concepts suitable for intermediate learners and takes around five months to complete, with a certificate when you finish.
4. Data Science MicroMasters
Edx is a website that features MicroMasters programs, which are short courses that help you learn about a subject and can count toward a master’s degree at select institutions. This Data Science MicroMasters, offered by UC San Diego, is for people already familiar with computer and data science. The course features live lectures, interactive assignments and the option to purchase a certification.
5. Data Scientist in Python
Dataquest is a digital learning platform dedicated entirely to classes about data science. One of their courses is Data Scientist in Python, which teaches basic data analysis, web scraping and machine learning. This course includes around 360 hours of data science instruction separated into nine sections. Introductory lessons in the course are free, with further classes and live walkthroughs of assignments costing a subscription fee to the platform.
6. Statistics and Data Science MicroMasters
This MicroMasters course by MIT on the Edx platform is a good course for people with intermediate to advanced knowledge of data science or programming. It covers probability and statistics in addition to data science tools. This instructor-led course lasts around one year and is free, with the option to purchase a certification and graded assignments.
7. CS109 Data Science by Harvard
Harvard University offers a free version of its data science course on a personal website. It features slides and videos on a variety of subjects, such as webscraping, statistical models and exploratory data analysis. While there’s no certification for completing this course, it includes most of the basic concepts and terms you need to start learning about data science.
8. Python for Data Science and Machine Learning Bootcamp
This data science and machine learning course presented by Udemy teaches you how to analyze data with several interactive assignments. It focuses primarily on the practical application of using data visualization and machine learning. The course features 25 hours of video, 13 articles and five downloadable resources, as well as a certificate of completion.
9. Become a Data Scientist
Become a Data Scientist is a course on Udacity recommended for people with some familiarity with machine learning concepts. The four-month long program divides into five sections that each teach data science techniques and concepts. It includes a project at the end of each section created by industry professionals.
10. Data Science Course Online
The staff of IIT Madras, an engineering college in India, hosts an expansive data science course on the learning platform Intellipaat. It lasts seven months and features over 50 live instructional sessions with professors and industry experts. There are also self-paced video learning resources as well as projects and assignments that you can complete.
Things to look for in a data science course
Here are some things you may want to consider when choosing a data science course:
A data science certification can show an employer that you possess knowledge and abilities related to data science, which may increase your chances of getting hired. Many courses include a certification after completing your classes, although some may charge additional fees for this feature. When deciding on which courses to take, consider checking if they have a certification associated with the program.
Some courses may cover the subject of data science more in-depth or include related subjects like statistics, which can take longer and may result in a larger course. How long a course is may matter to you if you have a busy schedule or want a certificate in a shorter amount of time. Try to decide how long you want to take classes in data science and look for courses that match that amount of time.
A data science course can vary in cost depending on the host platform, size of the course and any potential career features, such as a certification or course credit. You can take many courses for free and only need to pay if you want a certification. When selecting a course, think about your career needs and budget to help you decide on what classes to take.
Data science is a complicated topic that includes many levels of expertise. Some courses may require a deeper knowledge of math, programming or statistics than other courses. Before enrolling in a course, try to look at their prerequisites page to ensure you meet their requirements and have enough previous knowledge to gain the most value from the classes.
Some online courses feature only videos and text resources that you can explore independently. Others work in a structured live course environment or a hybrid system that gives you self-guided study with a mentor you can consult. Consider which class form works best for your circumstances and individual learning style, then try to select a course that best fits your needs.
5 data science jobs you can pursue
Here are some careers that you may qualify for after taking some data science courses. For the most up-to-date salary information from Indeed, visit indeed.com/salaries:
1. Business intelligence developer
National average salary: $102,915 per year
Primary duties: Business intelligence (BI) developers are engineers who design and create business intelligence interface. These interfaces help analysts and other roles make business decisions for companies or products. Developers can set the requirements for BI tools, make, implement and maintain software programs, design data warehouses and create documents that help others navigate their interfaces.
2. Data scientist
National average salary: $103,179 per year
Primary duties: Data scientists design and implement data modeling processes for an organization. They can create algorithms, predictive models and visualizations to help people make decisions using their data. While they can occasionally analyze their information, data scientists primarily create processes to model data.
3. Machine learning engineer
National average salary: $116,180 per year
Primary duties: Machine learning engineers create AI programs and algorithms that are capable of making decisions based on previous experience. Common duties include cleaning data, developing code, creating and customizing machine learning applications and documenting their code so others may replicate it. Most machine learning engineers work in teams, either with data scientists or other engineers.
4. Data engineer
National average salary: $116,378 per year
Primary duties: Data engineers design, build and maintain an organization’s data infrastructure to convert raw data into useable information. Structures a data engineer may create can include databases, data warehouses and servers. Other duties a data engineer performs may include assembling large or complicated datasets, identifying possible improvements to existing datasets, use analytics tools to analyze the data and create infrastructure for effective extraction, transformation and loading of data from a source.
5. Application architect
National average salary: $131,951 per year
Primary duties: Application architects supervise the design, development and implementation of software applications. They work with a team to design an application, monitor the application’s development and document any progress or challenges they may encounter. Some application architects may also write code for their applications or run error testing to ensure it functions properly.
19 Popular Data Science Tools Used by Professionals
Data science tools can assist data scientists in many of their daily tasks. Common types of these tools include languages, data libraries and analytics platforms. Learning about specific data science tools may help you decide which ones to use to assist in specific data-related tasks. In this article, we discuss the definition of data science tools and list 19 specific tools that data scientists use.
What are data science tools?
Data science tools are a set of packages and programs that data scientists can use for a variety of purposes. Data scientists use these tools to automate data processing, algorithm development and result analysis tasks. These tools provide capabilities needed to collaborate with others on large data sets while developing models or algorithms to solve problems in many different fields, including medicine and finance.
19 tools used by data scientists
Here is a list of several types of data science tools, with a description of each:
1. Apache Spark
Apache Spark is an open-source cluster-computing framework originally developed in the AMPLab at the University of California, Berkeley’s Collaborative Research Laboratory. Spark provides a general execution environment for large-scale data processing. Data scientists use Apache Spark to run computations across clusters for large-scale data processing, and it supports distributed machine learning algorithms. Data scientists can use Spark to manipulate, explore, visualize and analyze various types of big data using multiple languages such as Java, Scala and Python. Apache Spark includes libraries for machine learning (ML), graph analytics and streaming analytics using the Reactive Manifesto paradigm.
2. Apache Hive
Apache Hive is a SQL-like query language for querying a distributed data store. It allows data analysts and BI professionals to administer, analyze and manage large-scale data warehouses. Data scientists can use hive as an interface between a relational database and a MapReduce cluster. Data scientists use Apache Hive for extract, transform and load (ETL) tasks, which are used to move data from one system or framework into another.
3. Apache Pig
Apache Pig is a high-level data-parallel programming language for working with large datasets. Data analysts and BI professionals use it to treat multiple high-level programming concepts such as joining, aggregating, partitioning and sorting. Data scientists can use Apache Pig to render complex analytical models using MapReduce frameworks and with Python-based languages such as R and Java.
4. Jupyter Notebook
Jupyter Notebook is an open-source web application that allows users to create and share documents that contain live code, equations, visualizations and narrative text. Data scientists can use Jupyter Notebook as an interface between a Python program and the rest of the world. It allows them to develop and test code interactively, which is useful for prototyping algorithms that use complex math.
Keras is an open-source neural network library written in Python, which can be used to train deep learning models. Data scientists can use Keras for building neural networks for unsupervised or supervised learning, and for image processing. Data scientists can use Keras to automate data analysis tasks such as image classification and model training.
MATLAB is a high-level programming language and ecosystem of tools that provides numerical computation, data visualization and algorithm development with a focus on engineering and science applications. Data scientists use MATLAB to develop and test algorithms, and to visualize and explore data. MATLAB users can run code instantly on a local server or deploy it on a cluster or cloud service. Data scientists can also use MATLAB’s graphical data representation capabilities with its built-in plotting engine.
Matplotlib is a Python module that creates 2D charts from Python scripts. It is the most common plotting library for scientific computing and data analysis. Data scientists can use Matplotlib to develop interactive visualizations of numerical datasets such as multivariate analysis, image processing and time-series analysis with Python.
NumPy is a high-level programming language for data analysis built on top of the efficient Numerical Python (Numpy) extension to the Python programming language. Data scientists use NumPy arrays to manipulate large sets of data efficiently in memory, making it easy to run statistical tests on them. They also use NumPy’s fast Fourier transform functions to convert time-series or image data into a frequency spectrum.
PyTorch is a deep learning library and software system with a focus on mobile and desktop computing. It allows data scientists to build production-quality deep neural networks in Python, Java, C++ and other languages. Data scientists use PyTorch to transform input data into trained models directly in PyTorch code. Data scientists can also train convolutional or recurrent neural network models using PyTorch’s optimized back-propagation algorithm (OPenn), without having to manually tune parameters.
10. Scikit Learn
Scikit Learn is an open-source machine learning library for Python that implements the Scalable Learning Algorithm for Inference (SALA). Data scientists use Scikit Learn for statistical learning, including classification, regression and clustering. It offers a range of supervised and unsupervised algorithms, including decision trees, random forests and support vector machines (SVMs).
Seaborn is an open-source Python library that offers statistical visualization capabilities similar to MATLAB. Data scientists use Seaborn to explore data sets graphically, especially when they’re too large to be displayed easily in a spreadsheet application. They can use Seaborn’s statistical methods such as kernel density estimation, principal component analysis (PCA) and factor analysis.
SAS is a software system that provides data management, data analysis and reporting for business intelligence and analytics. SoftwareAS allows data scientists to run statistical tests to verify the accuracy of a decision made by the business or enterprise, or to refine an algorithm that is being used to make decisions. Data scientists can use SAS to merge multiple sources of data into different formats using scripting.
TensorFlow is an open-source machine learning library that enables data scientists and developers to build, train and deploy deep learning models. It has a strong bias towards research and development compared to production deployment. Data scientists can use TensorFlow for building deep neural networks that can learn complex tasks automatically from large amounts of data. They can also use TensorFlow for reading and writing data directly from their source, and for deploying models on multiple devices to interact with the real world.
Weka is an open-source machine learning software that focuses on algorithms for data mining tasks. Data scientists use Weka for unsupervised and supervised data mining, including classification and regression. They can also use it to develop neural networks and support vector machines. Data scientists can use Weka to generate predictive models with a built-in visual interface.
15. Wolfram Mathematica
Wolfram Mathematica is an advanced computing software system that integrates computation, visualization, graphics, programming and collaboration tools within a single environment. Data scientists utilize Wolfram Mathematica’s computational engine for writing codes that control applications in other software fields such as web development, business and computer science. Additionally, data scientists can use Wolfram Mathematica’s graph-based programming system to develop connected applications that work together.
WebSockets are a connection technology for two-way data streaming between client and server applications, allowing the developers to create more engaging user experiences. Data scientists can use a WebSocket API to develop interactive real-time applications such as chatbots and video games. Data scientists can also use WebSockets to build applications such as application development platforms, remote controls or even video conferencing software.
Julia is a high-level, high-performance, dynamic programming language for technical computing. Data scientists use Julia for computational analytics and visualization. Julia is a multi-paradigm programming language that features a syntax similar to MATLAB so data scientists can easily integrate it into their existing workflow. Julia also includes an interactive shell and other production features, such as an extensive mathematical function library and multiple backends, so you can use it in standalone or distributed applications.
D3.js is a data visualization library that enables developers to select and manipulate data dynamically. Data scientists mainly use it for web-based applications, but they can also use it in standalone desktop apps. Data scientists use D3.js to create data visualizations such as bar charts, area graphs, heat maps, scatterplots and more.
Tableau is a software tool for visualizing and analyzing data. Data scientists use Tableau to create interactive dashboards such as trends over time, geospatial maps or correlations between different dimensions of data. They also use Tableau to create static data visualizations, such as statistics and maps.