Data science online courses are a great way to get started in the growing field while they cover a range of topics within data science. With an online, flexible format they could be a good fit for working professionals and students to gain experience.
Data science online courses cover a wide range of topics, from machine learning to data extraction. Whether you are at the start of your career or already in an IT job, a data science online course may teach valuable skills to further your career. If you are looking to gain experience with languages like R and Python or want to learn to tell stories by effectively visualizing your data, online courses are available.
There are online data science courses for students at many levels. Though many courses expect some level of programming experience, others are appropriate for beginners. Some programming courses are also available online to get you up to speed. Courses generally require a commitment of under 80 hours to complete. Some courses are available for free with certificates available for an added fee.
Data Science Course Topics
Data science courses cover a wide variety of topics.
- Programming languages and frameworks are essential tools in data science, so these are often topics of interest. Programming courses that teach essential data science languages like R and Python may also be available.
- These courses may also cover essential techniques such as neural networks, machine learning, and classical statistics.
- Topics in ETL (extract, transform, load) such as web scraping, data cleaning and data warehousing are key skills for a data scientist.
- Data communication is an essential element of data science. Courses covering topics such as data presentation and business applications are also available.
Introduction to Data Science
Data science is a field of study that applies scientific techniques to extract meaning and insights from data. It exists at the intersection of statistics and computer science. The combination of these fields provides important tools to many different industries. Data science is a relatively new field that is exploding in popularity. The availability of trained professionals is lagging behind the demand for data scientists.
Data science has entered the limelight due to the many applications enabled by technological advances. The increasing availability of large datasets with information about customers, marketing, products and more is essential to driving businesses forward in efficiency. In addition, the ubiquity of high-speed reliable internet and low cost remote computing has enabled the processing and analysis of this data.
Data science also has widespread applications. The amount of data that is being collected today is “mind-blowing”. This rate is continuing to grow as more and more items join the internet of things (IoT). The availability of massive publicly accessible databases of scientific, sociological and economic information enables even more applications. Industries such as healthcare, banking and marketing have massive amounts of data to derive insights from. Even less data centric businesses need data science due to the digital nature of business applications.
Data Analysis in Data Science
Data analysis is the process by which data is processed and analyzed. Data analysis is how meaningful data strategies are derived from existing datasets. This is an essential part of the data science process.
Data analysis can also be defined as the application of statistical tools to datasets. Large scale analysis is enabled by statistical programming frameworks. It assists in the application of the techniques and tools of data science to real world data in reaching meaningful conclusions.
The essential tools for data analysis include statistical methods, programming languages, and frameworks. Requiring the extraction of data using a query language like SQL or through web scraping or an API, data analysis also requires applying data transformations and statistics using a programming language such as R or Python.
Machine Learning in Data Science
Machine learning is an area of research into techniques that enable computers to learn from data. These techniques enable computers to recognize objects in images, meaning in text and trends in data – involving a variety of useful techniques that can be applied to big data.
Machine learning is most often conflated with neural networks and deep learning. Though these techniques are not the only type of machine learning, they are very popular. They often emulate the function of brains by simulating neurons. Neural networks and convolutional neural networks are able to detect patterns in data, based on training. This allows them to automate processes traditionally done by humans.
Machine learning also takes advantage of many of the same tools as other areas of data science. These techniques require large, labeled datasets for training. Machine learning also uses statistical programming languages such as Python with pandas and R. There are many freely available libraries for many programming languages that implement machine learning techniques. These include Google’s TensorFlow and OpenCV.
Data Science for Business
The applications of data science are seemingly endless. Though many of these techniques and processes were originally developed for scientific applications, they are useful in many other areas. Many industries are applying data science to create better products and improve profit margins. The shift online of so many aspects of life has created a huge opportunity for businesses – understanding consumer behavior, competition and marketing is now possible.
Business analytics is the use of data, data analysis, and statistical tools to create new insights and discover meaningful patterns in data. This differs from the more traditional business intelligence, which uses defined metrics to track performance and guide decisions. Business analytics takes advantage of the complexity of available data by identifying insights that may be overlooked my traditional metrics.
With applications in a wide range of businesses and industries, business analytics can be prominently viewed within the fintech industry – a new industry that aims to use data science and modern technologies to streamline financial processes. This often includes using big data to inform decisions traditionally made by humans. Lending decisions, financial advising, investing, and more are areas where data science and business analytics are being applied.
Value of Data Science Online Courses
Data science online courses offer a way to gain skills in data science without the commitment and expense of a masters or other degree program. The remote learning environment may allow for students to fit learning into their busy lives. Stand-alone courses have the added benefit of relatively small time-commitments – possibly lending to gaining skills as students have time.
An online course could be a great way to gain new experience and move up in your career. Whether you are an IT professional wanting to gain skills for your job or just getting started in data science, data science online courses could be a great option for you.
Data Science Course Duration
Online data science courses offer a selection of learning styles. Though all courses require a regular time commitment, many allow students to organize their time as they please. Self-paced courses could make it more feasible for students to fit learning into a busy schedule. Other courses offer a more defined timeline with tests and benchmarks at specified times.
With total time commitments ranging from 10-100 hours there are many levels of commitment available. Courses often take between two to six weeks to complete. Weekly commitments in online courses generally range from two to 10 hours per week.
Data science online courses may require a smaller commitment than alternatives such as boot camps and masters programs. The complexity of the topics covered will dictate the total time requirement. Familiarity with the topic and tools used will also affect the amount of time needed.
Data Science Skills Learned
Students taking data science online courses may learn a variety of skills. These often cover the full range of topics within data science and range from basic statistics to advanced machine learning techniques. General introductory data sciences tend to all teach the core skills of data science. Essential skills include:
- Programming language such as Python and/or R as well as SQL
- Structuring the data science process and how to develop from a question to a decision
- Collecting and organizing data in a useful format through data acquisition, cleaning, storage and access
- Derive meaning from datasets by discovering patterns and developing a model
- Communicating data by visualizing the results – you may learn to use data to tell a story that convinces stakeholders and leads to decisions
Projects in Data Science Course
Data science online courses often include a project to allow students to apply their learning. The nature of these projects depends upon the class.
Courses in computer vision may provide an opportunity for students to design and train their own image classifier. A course in data visualization could include a final presentation where data is used to tell a compelling story.
Any data science course project will provide the opportunity to apply the courses subject matter to a real world problem. This provides a way for students to test their learning. A good data science course project allows students to work through the entirety of the data science process on their own. Experiential learning provides students with an opportunity to learn through doing.
Growth in the Data Science Field
Data science is a rapidly growing field with applications in many different industries. From SearchBusinessAnalytics, a summary of a January 2019 report by Indeed shows a 29% increase in demand but a 14% increase in job searches for data science positions. Therein lies the disparity between supply and demand.
Data science is being applied throughout the business world. More and more businesses are collecting data. As the amount of data increases, so does the need to analyze it. This can only be done by highly trained professionals. This has led to a huge demand for data scientists at companies in many industries. Though the technology sector employs 41% of data scientists, more than half of data scientists work outside of the technology sector.
Industries such as energy, telecommunications, medicine, financial services and more all need data scientists.