What you need to know before starting a data science course
Are you considering a career in data science? With the world becoming increasingly data-driven, the demand for data scientists is growing rapidly. DataScientest gives us the scoop on what you need to know before getting started.
Becoming a data scientist is an exciting and rewarding career, but it’s not an easy journey. To ensure success, it’s important to understand what is required before starting a data science course. Whether you’re a beginner or a seasoned professional, here is what you need to know.
DataScientest courses offer state-of-the-art learning, lifetime career support, and are eligible for a plethora of funding options.
Why is data science important?
Many industries are slowly transitioning from a focus on qualitative data to a focus on quantitative data. With this transition comes a demand for data scientists. In other words, companies are realising that their success depends on the ability to process, analyse, and interpret large amounts of data.
Data scientists provide companies with the tools to do this. In fact, data scientists are often tasked with identifying and quantifying risks, making them critical to industries such as insurance, finance, and healthcare. In order to meet these changing demands, many data scientists have to find new ways to process and analyse data. This is where machine learning comes into play - this is an area that is growing at an exponential rate.
What skills do you need to become a data scientist?
Due to the ever-changing data landscape, data scientists must continually learn new skills. Therefore, it’s important to understand what skills you need to become a data scientist.
Some of the most important skills include programming skills, statistical and mathematical skills, the ability to use tools such as Excel and SQL, critical thinking and analytical skills, an understanding of data storage and architecture, and communication skills.
Data scientists use a variety of tools in their work, so programming skills are essential. Popular programming languages used in data science include Python, R, and Java. Data scientists also use tools such as Excel, SQL, or Power BI. A strong foundation in statistics and mathematical concepts is helpful, as data science is very mathematical in nature.
To join a DataScientest course, you should have the equivalent of a bachelor level diploma in mathematics, statistics or science - this is to ensure you have a good understanding of the concepts discussed - but people with less formal qualifications are also welcome to apply.
What do data science courses cover?
The exact content of data scientist courses will vary, but they will generally cover the following areas:
- Programming languages and data structures: This includes studying popular programming languages such as Python and R, as well as learning data structures and algorithms.
- Statistical concepts: This includes studying concepts such as probability, regression, and experimentation.
- Machine learning: This is an ever-growing field in data science that focuses on the creation of algorithms designed to solve specific problems.
- Data visualisation: This is an important skill for data scientists, as they must be able to use visual representations of data to communicate findings.
- SQL: This is the most common language used for data manipulation and is essential for data scientists.
- Hadoop: This is a distributed data storage system that is commonly used in analytics.
- Architecture: This refers to the design and implementation of data storage systems.
- EDA: This stands for exploratory data analysis and is used to identify patterns in data.
DataScientest courses comprise 400 hours of instruction, including 280 hours for training and 120 hours to work on your own personal project. The training covers topics like programming in python, data visualisation, machine learning, big data, complex systems and AI.
How to choose the best data science course
When choosing a data science course, you will come across many different options. Unfortunately, not all of these courses are created equal. To ensure that you choose the best data scientist course, here are some things to consider.
- Reputation: This is related to the type of institution that offers the data science course. Does it have a good reputation? Does it have accreditation? What is the background of the instructors?
- Course structure and content: Does the course provide a syllabus that outlines the topics to be covered? What is the length of the course? How long have they been in business?
- Course price: While price is not the only factor to consider, it is important to remember that you get what you pay for.
- Variety of classes: Does the company offer only a data science course, or do they have a variety of courses as well, like a data analysis course, or a data engineering one?
- Types of students: What is the make-up of the student body? What are their backgrounds?
- Location: Is it an online data science course? Or are the courses delivered exclusively in person?
- Certifications and job assistance: Does the course provide certification? Does it assist students in finding jobs?
What are the prerequisites to start a data science course?
While there are no strict prerequisites to start a data science course, it’s important to understand what skill sets you will need to progress. First things first, you need to make sure that you have enough time to complete the course. Most data science courses last between six and 18 months.
On top of this, most data science courses will require you to have at least some of the following:
- Mathematical skills: Data scientists use mathematics to solve a variety of different problems. Therefore, it’s important to have a strong mathematical foundation.
- Programming skills: While many data scientist courses provide the programming skills you need, some don’t. If you want to be sure that you have the necessary programming skills to complete the course, make sure that you already have them.
- An understanding of data: Data comes in many different forms, and it is important to have an understanding of the different types of data.
What kind of job opportunities can you expect after completing a data science course?
While this will vary depending on the course you choose and your skill set, many students who complete data science courses are able to find employment as data scientists. Data science is a growing field, and many companies are looking to hire data scientists. Some of the most in-demand industries include insurance, healthcare, finance, government, and education.
You can also earn money as a freelance data scientist. While this is not as stable as a full-time job, it can be a great way to get your foot in the door and make connections.
All DataScientest students get access to a platform dedicated to career services from day one of their course, while career managers are on hand for anyone who wants to discuss their future options. Students also benefit from monthly careers workshops, and an annual recruitment fair.
How can you stay up-to-date with the ever-changing data science landscape?
As previously mentioned, data is ever-changing, and the field of data science is constantly evolving. To remain relevant in this industry, it is important to keep up-to-date with new trends. Fortunately, there are a variety of ways that you can stay up to date, including:
- Networking: This is a great way to make connections and find mentors.
- Reading blogs and articles: There are many blogs and articles written by data scientists that are published on a frequent basis.
- Attending conferences: Conferences are a great way to network with data scientists and keep up-to-date with new trends.
- Joining a community: There are many communities that are dedicated to data science. These are great ways to meet other data scientists, ask questions, and find inspiration.
What resources are available to learn data science?
With the demand for data scientists growing, there are now many resources available to help you learn data science. There are a variety of online courses and in-person courses that you can choose from. There are also eBooks, podcasts, and video series that you can use to learn.
Online courses are a great option if you want to learn data science on your own time and from the comfort of your own home. They usually provide a syllabus, lecture notes, and readings. In-person courses are great if you prefer to learn in a classroom environment and have the added benefit of networking with other students.
Learn more about DataScientest
DataScientest, a leading European institution, offers courses that combine the pros of each type of course: a state-of-the-art platform where you get to learn to code - by coding - and a remote classroom environment where you can challenge your classmates, chat with professors and carry-out a real-life project.
Their courses are certified by the prestigious University la Sorbonne, offer lifetime career support and are eligible for a plethora of financing options. Find out more by visiting the website.
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