Can I learn data science in 1 year?

Want to learn more about data science? Enroll in this  data science syllabus to do so.

Learning data science takes different durations dictated by:

• the course you want to take
• your educational background
• career background
• Experience

 Data science is a vast field, so you continue learning throughout your career. In addition, you can take advantage of online courses and boot camps to up your skills. 

Skills needed in data science

There are some skills considered universal that every data scientist needs to master. They include:

Coding

Data science is a field that is involved in solving issues and helping organizations make data-informed decisions. The job of a data scientist involves taking real inputs and using scientific algorithms, processes, and systems to get insights and knowledge from data.  Data analysis is also part of it. 

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Coding is one of the skills needed in data science. There are different programming languages, and most data scientists are proficient in multiple languages. Some of the popular options include:

• Python, which is the most common
• R used to handle statistical analysis
• SQL or search query language
• Java which is the best for application building
• C/C++ for massive and rapid computations and app building
• Matlab, which helps in mathematical modeling and computation

Math

Data scientists need math skills to find valuable insights regarding opportunities and struggles that a company is dealing with. In addition, you need math skills to turn scenarios into solvable problems. Math helps pose questions that can be answered and interpret the results that you get from the algorithms. You don’t need to be a mathematics genius, but you should have a foundation in:

• Algebra: With this, problems must be set up so they can be handled mathematically. Algebra is needed in this case. 
• Linear algebra: linear algebra handles vectors and matrices. You need to be proficient in matrix operations to handle coding.
• Calculus: calculus computations can be handled using software, but it is still essential to know derivatives, max, mins, and integrals if results are to be understood. 
• Probability and statistics:  Statistics help interpret data. A data scientist needs to predict the future using the available information accurately. Probability helps calculate outcomes. 
• Linear regression: linear regression helps you with approximation of non-linear points in a series to find the best fit. This helps you to predict probabilities and values. It helps crunch data. 

Business skills

Data scientists channel different technical skills to help solve problems related to business. For example, a data scientist needs business intelligence as a skill to evaluate different business scenarios and create a data model with actionable insights. This is why some curricula in data science involve finance and business classes. These are aimed at imparting knowledge regarding how business should be done. 

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Communication

Data scientists need to work with professionals with little knowledge of statistics and coding.  For example, a data scientist should be able to give representations in different formats. They should also be able to explain insights in a way that non-technical professionals can understand. 

Do data scientists need a degree?

The time it takes to be a data scientist usually depends on your chosen learning path. However, you should note that you don’t need a data scientist degree. However, you need data science skills to perform at that level. 

In addition, many companies have embraced that a degree in data science may not be needed for an individual to perform well. 

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How to Become a data scientist

Becoming a data scientist can be achieved through various steps, such as:

College

The benefits of going to college include:

• It is a widely accepted and recognized method
• You get formal training and certification

The main disadvantages include the following:

• It is the most expensive method 
• It takes a long time to complete

You can enroll in a data science bachelor’s degree in many universities. However, this can take four years. If you take this path, you cannot learn data science in one year. The cost is also high compared to boot camps. It’s important to keep in mind that certain positions may require a master’s degree or even more advanced levels of education. You can complete online master programs in one year. 

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Data science boot camps

This is the other available option. The advantages of taking this path include:

• It takes less time to qualify as a data scientist
• It is not as expensive as a college
• You gain valid skills
• You get career coaching once you graduate
• There are better chances of mentoring and networking

The main disadvantage of going the boot camp way is that there are some employers who prefer candidates to have a formal degree. 

This is a middle path you can take in your quest to be a data scientist. In such a program, you get job-specific and intensive training from experts in the field. In a boot camp, you gain hands on experience without going through college systems. 

This is a data-centric approach where students learn all they need at a much lower price and within a shorter time. For example, you can complete a full-time program in 20 weeks or less. Boot camps also have flexible pace options that can take different durations. In this case, you can learn data science in less than one year. 

Self-teaching

With self-teaching, there are some advantages involved, and they include:

• It takes the shortest time if you are committed and disciplined
• You can work at a pace that is suitable for you
• It is cost-effective

There are also disadvantages, including:

• There is no accountability or structure
• Many industries may not readily accept such a qualification
• It may take a longer time if your pace is slow

Data analysts have the communication, business, and mathematical skills that data scientists need. They only need to add some expertise to become data scientists. This takes a shorter time.