Data Science

Data science is now one of the most in-demand career paths. For both new and experienced professionals, it is a world rich with opportunities at all levels of entry. Individuals with the right skills are highly sought-after and the data science courses we offer will enable you to develop these skills so you can enjoy a rewarding career.


Why Pursue a Career in Data Science?


‘Data scientist’ is #2 in the fifty best occupations in the USA

Glassdoor, 2021


The field of data science will grow 28% by 2026

US Bureau of Labor Statistics, 2020


Browse our Data Science Learning Catalog

Career-Oriented Data Science Programs

Fast-track the development of your data science skills with one of our industry-focused programs. Gain job-ready skills, practical experience, and professional certifications to kick-start and boost your data science career.

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Skills-Oriented Data Science Courses

Build your skills with our extensive range of data science courses that take you from foundational training to advanced competency development.

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Gain In-Demand Skills with our Data Science Courses

  • Statistics
  • Data analysis
  • Data processing
  • Modeling
  • Machine learning
  • Python programming
  • R programming
  • Hadoop
  • SQL databases
  • Data visualization
  • Big data
  • Model deployment
  • Deep learning
  • Apache Spark

When You Enroll with SkillUp Online

Our data science courses offer forward-looking curriculums that build job-ready skills specific to this technical field. You can benefit from a range of helpful support services and gain practical experience as you learn.


Personalized mentoring

  • 1-to-1 mentoring sessions
  • Interactive live classes
  • Webinars with industry experts
  • Weekly personalized feedback
  • Collaborative discussion spaces

Practical experience

  • Real-world projects
  • Hands-on labs
  • Capstone projects
  • Knowledge checks
  • Industry feedback

Support & guidance

  • Explains tech-based subject matter
  • Deepens your understanding
  • Builds your confidence
  • Connects you with likeminded peers
  • Ensures you complete your course

Certificates You Can Earn

Professional Certification

Through our close partnerships with top industry organizations, you can earn fully accredited Professional Certifications in AI technologies and methodologies.

Certificate of Completion

Our carefully designed catalog of AI courses enables you to earn career-enhancing Certificates of Completion confirming job-ready skills.



Data science is a blend of programming tools, statistical analysis, algorithms, and machine learning principles. It involves the use of multiple approaches including statistics, scientific methodologies, artificial intelligence (AI), and data analysis.  

Data scientists utilize all these instruments to then discover meaningful patterns hidden in raw data. They tap into a variety of skills to evaluate data acquired from the web, smartphones, customers, sensors, and other sources. And it is data science that provides the framework and methodologies used to glean the valuable insights required. In working this way, the trends that data scientists identify then enable businesses to become more efficient through improved decision making and more innovative with respect to their products and services.  

The goal of a data scientist is to discover the meaningful patterns hidden in raw data. Data scientists work collaboratively with business stakeholders to establish how data can help the organization achieve its goals. They create algorithms and prediction models to extract the required data for a given objective in order to then analyze the data, create meaningful visual reports, and share information.  

A data scientist needs strong skills in the areas of data analysis and programming. However, these are not the only skills they require to be successful. They also need to develop abilities in other ways. Our data science courses and human skills courses will help you gain these skills. 

Technical Skills - To be a good data scientist, you must excel in mathematics and statistics. However, other technological expertise is also required, including: 

  • Programming: Computer languages and programming languages, e.g. Python and R.  
  • Knowledge of analytical tools: SAS, Hadoop, and Spark are three popular analytical tools used by data scientists.  
  • Workability with unstructured data: Ability to manage unstructured data obtained through various channels.

Non-Technical Skills - An individual’s soft skills - sometimes referred to as human skills - are classified as non-technical. To be a good data scientist, you need:   

  • Exceptional business sense: Understanding the relevance and impact of trends.    
  • Strong communication skills: Ability to communicate findings in a meaningful way. 
  • Data intuition: Ability to ascertain when analytical tools have not been accurately specified. 

Through the rise of big data and the need to collect and protect the data being generated, data science has become a fast-evolving discipline. To ensure they are recruiting individuals with the right skills, businesses are actively checking for certification when they advertise a data science role. Now, more than ever, people with the right credentials and expertise in the field of data science have a wide selection of opportunities open to them. For those who can synthesize vast amounts of data and process it in ways that benefit businesses and mankind, a rewarding career journey lies ahead. Consider the following: 

Attractive remuneration packages - The base salary of a data scientist with a few years expertise in the United States is $106,000 per year on average, plus many enjoy excellent median bonuses. A typical annual base income of $140,000 is expected when you reach the managerial level. You can also out-earn many surgeons if you include median annual incentives.


Huge job opportunities - Demand is high for data scientists, so when you add in the fact that there's a severe scarcity of them, it becomes clear this skill shortage presents an excellent opportunity for ambitious and talented individuals.  

A scarcity of alternatives - The skills required for data science do not cross over significantly with other disciplines, thus few specialists are seeking to switch from other professions. For example, certain employers need finance and accounting individuals who have the skills to mine and extract data and spot big data trends, however typical applicants in these fields are not skilled in these subjects.


Exciting employment opportunity - Human behaviour, which is the main focus for data scientists, is a fascinating subject. From designing a chatbot to analyzing user experience on the internet, those working in this field contribute to one of the century's most important and exciting endeavors.


Rapidly growing field - With the rising number of sensors, computing capacity, and insight-extraction techniques, data science is a burgeoning field that shows no signs of slowing down. Plus, the range of tasks involved are wide and varied. Data scientists can be doing anything from undertaking research and analysis, to finding answers to problems, to assisting an organisation in making quantitative educated decisions.


Diverse working exposure - As a discipline, data science offers a broad selection of niches in which to specialize. Data scientists have the pick of working in tech enterprises, ecommerce, start-ups, production firms, renewable energies, and traffic optimization, to name but a few options. This gives the ambitious data scientist plenty of 'horizontal mobility' to explore. 

Learner adaptability - Data science is ideal for both individuals setting out on their first career and for those seeking to switch. As a discipline, it can be studied online from anywhere in the world, which also means people can learn at their own pace.


p>For aspiring individuals, some of the most lucrative data science roles include:


Data Scientist - A data scientist investigates data trends in order to assess their influence on a company. A data scientist's main responsibility is to communicate the significance of the data a business holds in a way that others can comprehend. To achieve this, they need to have a working knowledge of the many programming languages and data science methodologies available for addressing this challenging task.  

Data Analyst - A data analyst studies data and draws meaningful insights from their analysis. In doing this, they contribute to the development of a clear image of the company’s market position. Typical responsibilities include extracting data using high tech computer models, eliminating corrupted data, executing initial analysis to assess the quality of data, making presentations based on analysis, and presenting to management. 

Data Engineer - Data engineers are the backbone of a corporation. They work at the organization's core and oversee creating, designing, and managing the company’s databases. They are responsible for constructing data pipelines, ensuring that data flows correctly and making certain that it reaches the appropriate departments. To communicate results to their colleagues, a data engineer must collaborate with other data professionals, and share their findings with the rest of the firm via data visualization.  

Business Intelligence Analyst - A business intelligence analyst assists in the analysis of data, with the aim of increasing the efficiency of the organization and generating more revenue. Their job is more technical than analytical, and it necessitates a higher level of familiarity with common technological machines and tools. They must act as a link between company and technology, assisting both in improving their performance. 


Marketing Analyst - A marketing analyst assists the marketing department of a business. They research and recommend which products should be mass-produced and which should be phased out. They also use customer satisfaction surveys to aid in the improvement of current products and services, and they assess which products should be offered to target clients, and at what prices they should be offered.

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