However, the study areas covered are wide-ranging and comprehensive for a student who is looking to delve into the world of data science via Python programming. This course is conducted in English, but subtitles in Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Spanish and Korean are also available. This course is self-paced, split into five different parts. It is also free to access content, though certification requires a fee. You will learn:
Text mining Python programming Pandas Matplotlib Numpy Data cleansing Data virtualization Data visualization (DataViz) Machine learning (ML) Algorithms Machine learning Scikit-Learn Natural Language Toolkit (NLTK)
This course is flexible but set at a suggested pace of seven hours per week, which will take approximately 11 months to complete. Subtitles for the course are available in: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, Spanish, Chinese (Simplified), Portuguese (Brazilian) and Japanese. You will learn:
Github Machine learning R programming Regression analysis Data science Rstudio Data analysis Debugging Data manipulation Regular expression (REGEX) Data cleansing Cluster analysis
This is a preparatory course to identify whether you’re ready for MA study, improve your data science skills, and get to grips with the basics of Python. This is also a course suitable for students who have an interest in expanding skills in Data Science and AI. This course is only 20 hours long, but you are encouraged to move with it at your own pace. It is entirely free, but for a £52 upgrade you will be able to have printable certification at your disposal. This course will cover:
Programming Mathematics Data fundamentals Statistical thinking Privacy and ethics Context and environments
This course would be best suited to candidates who are already studying data science to some degree and are interested in expanding their knowledge of data science ethics. The course is entirely free, but for a £52 upgrade, you will be able to have printable certification at your disposal. You will learn:
Utilize the framework provided in the course to analyze concerns related to data science ethics Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency Examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems Learn best practices for responsible data management Gain an understanding of the significance of the Fair Information Practices Principles Act and the laws concerning the ‘right to be forgotten’
It is easy to firstly associate data science with science, technology, engineering or mathematics (commonly known as STEM); however, all nature of industries are coming to realize the serious benefits of data science and using data to inform key business decisions. From the perspective of tailored marketing, collecting data on customers and fine-tuning the customer experience is crucial to remaining competitive in today’s fast-moving market. With data comes data analysts. With an abundance of data comes a worldwide influx of jobs available to those with data science qualifications and skills. It is important to remember that taking a free course may not result in certification after its completion. However, there may be options for upgrading for a small fee to gain certification. There are many situations where these courses can be beneficial, such as:
Considering Data Science as a Career Path or Career Change
Data scientists are some of the most sought-after professionals today. However, data science does not merely demand a highly intelligent, analytical mindset. This profession calls for an unusual blend of skills and personality traits that contribute to an exceptional data specialist. You will need a strong and unwavering curiosity and a hungry appetite for accomplishing practical business impact. You will also need a natural ability for problem-solving and a flair for learning new and evolving technology.
Expanding Skills and Knowledge Alongside Your Work or Study
As many as 88% of data scientists will hold a relatable MA Degree, and 46% will possess a PhD. However, to enhance your job prospects further, it is advisable to continue to expand on your practical skill set, and to also experiment with learning skills that you may eventually take with you into a long-term career plan.
Wanting to See Course Materials Before Paying for the Full Tutoring
Data science is such a challenging career (as well as a hugely rewarding one), therefore, it is best to get a sense of whether you are pursuing the right path before committing to a full course. Although online courses will be split into recommended time increments, you will be able to pace yourself alongside full-time work or another study. There are also different fields of data science to a specialist in, so taking the time to research the right course for you should help you in your decision-making.
Starting a Degree or Advanced Course and Wanting to Have Fundamental Knowledge First
You may have already enrolled in a higher education course that is related to data science. Entering further study with sufficient confidence is key to starting your new course with conviction.
What Should You Expect to Gain From Your Data Science Course?
Below are some fundamentals of the knowledge you should look to gain during your course. The precise skills will of course depend on the specific nature of the course itself. Data science is a broad topic to study, and different courses will examine different aspects of this.
Anaconda
Anaconda is the coding language that most users commonly start on before graduating towards more complex languages. Simply put, it is a more user-friendly package of both Python and R. Typically, a user will start on Anaconda and build up to using R and Python.
Python
Python is one of the most common methods of coding typically required for data science roles as it is hugely versatile; it can be applied to many data scenarios, and most plugins are free or open-source. Alongside Python, Java, Perl and C/C++ are common requisites.
R
Alongside Python, R is to date the most commonly used programming language within the data science field. R is a popular programming language to use because it enables its users to produce publication-quality graphs, including mathematical formulae.
Hadoop Platform
Hadoop is not as much of a common requirement as Python, however, this is not to say that it is not still a sought-after skill that will help you to achieve the job role you are seeking. It is a software framework that is designed to deal with big data analytics.
SQL Database/Coding
SQL stands for structured query language. It is a programming language that operates solely through clear statements of intent. It is used to communicate, and therefore manage, relational databases. It is also used for stream processing in a relational data stream management system.
Apache Spark
Apache Spark follows a similar framework to Hadoop, processing large amounts of data. It also contains inbuilt modules for SQL, machine learning, graph processing and streaming.
Machine Learning and AI
Machine learning is becoming ever-increasingly popular, where a machine learning model predicts and recommends via an automated system. This operates off a high probability rate. For instance, users of Netflix and Spotify will experience this and using a high percentage of probability, these machine learning models will continue to successfully work using an evolving set of statistics. AI, on the other hand, requires a much wider field of definition. It is most easily defined by this quote from Andrew Moore, Former-Dean of the School of Computer Science at Carnegie Mellon University: “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence”. Learning about machine learning and AI could significantly help a data scientist to stand out from other prospective job candidates. Both of these skills require a complex set of skills, including neural networks, reinforcement learning and adversarial learning.
Data Visualization
Data visualization is quite simply the graphical representation of data. This is a key tool within a data scientist’s armory of skills to help organizations spot and understand trends, changes and patterns in data. It also reduces a large set of data to a more easily translatable format. Common types of data visualization could include:
Charts Tables Graphs Maps Infographics Dashboards
However, less common types could also involve:
An area chart A bar chart Box-and-whisker plots A bubble cloud A bullet graph A cartogram A circle view A dot distribution map A Gantt chart A heat map A highlight table A histogram A matrix A network A polar area A radial tree Many others besides
Intuitive, astute data visualization is key to helping data scientists to communicate their findings in a business-savvy context.
How to Improve Your Data Science Learning Alongside Your Course
What Skills Do Data Scientists Need to Develop?
Below are some examples of skills that a would-be data scientist would be well-placed to develop:
Fundamentals of data science Statistics Programming knowledge Data manipulation and analysis Data visualization Programming languages, such as Python Machine learning Deep learning Big data Software engineering Model deployment Communication skills Storytelling skills Structured thinking Curiosity
This is why can be extremely helpful to take on a free and flexible course to find out what area of data science to specialize in, and to further prepare you, or to aid you in a full-time course of study that you are already enrolled for.
